@misc{https://doi.org/10.48550/arxiv.2503.07210_Precision_Weed_Mapping,
    author = {Swindell, Jacob and Darbyshire, Madeleine and Popovic, Marija and Polvara, Riccardo},
    copyright = {Creative Commons Attribution 4.0 International},
    doi = {10.48550/ARXIV.2503.07210},
    keywords = {Robotics (cs.RO), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
    publisher = {arXiv},
    title = {Discrete Gaussian Process Representations for Optimising UAV-based Precision Weed Mapping},
    url = {https://arxiv.org/abs/2503.07210},
    year = {2025}
}

@article{Poozhiyil_2025_Autonomous_Robotic_Sorting,
    author = {Poozhiyil, Mithun and Argin, Omer F. and Rai, Mini and Esfahani, Amir G. and Hanheide, Marc and King, Ryan and Saunderson, Phil and Moulin-Ramsden, Mike and Yang, Wen and García, Laura Palacio and Mackay, Iain and Mishra, Abhishek and Okamoto, Sho and Yeung, Kelvin},
    doi = {10.3390/machines13030214},
    issn = {2075-1702},
    journal = {Machines},
    month = {March},
    number = {3},
    pages = {214},
    publisher = {MDPI AG},
    title = {A Framework for Real-Time Autonomous Robotic Sorting and Segregation of Nuclear Waste: Modelling, Identification and Control of DexterTM Robot},
    url = {http://dx.doi.org/10.3390/machines13030214},
    volume = {13},
    year = {2025}
}

@article{Carnelos_2025_Efficient_Rustbased_Inference,
    author = {Carnelos, Matteo and Pasti, Francesco and Bellotto, Nicola},
    doi = {10.1016/j.iot.2025.101498},
    issn = {2542-6605},
    journal = {Internet of Things},
    month = {March},
    pages = {101498},
    publisher = {Elsevier BV},
    title = {MicroFlow: An Efficient Rust-Based Inference Engine for TinyML},
    url = {http://dx.doi.org/10.1016/j.iot.2025.101498},
    volume = {30},
    year = {2025}
}

@article{Baxter_2025_Social_Robot_Behavioural,
    author = {Baxter, Paul},
    doi = {10.3390/biomimetics10010050},
    issn = {2313-7673},
    journal = {Biomimetics},
    month = {January},
    number = {1},
    pages = {50},
    publisher = {MDPI AG},
    title = {Multi-Modal Social Robot Behavioural Alignment and Learning Outcomes in Mediated Child–Robot Interactions},
    url = {http://dx.doi.org/10.3390/biomimetics10010050},
    volume = {10},
    year = {2025}
}

@conference{11577_3549114_Complex_Intralogistic_Environments,
    author = {Stracca, E. and Rudenko, A. and Palmieri, L. and Salaris, P. and Castri, L. and Mazzi, N. and Rakcevic, V. and Vaskevicius, N. and Linder, T. and Bellotto, N. and Schreiter, T. and Zhu, Y. and Castellano-Quero, M. and Napolitano, O. and Stefanini, E. and Heuer, L. and Magnusson, M. and Swikir, A. and Lilienthal, A. J.},
    booktitle = {European Robotics Forum 2025},
    title = {DARKO-Nav: Hierarchical Risk- and Context-aware Robot Navigation in Complex Intralogistic Environments},
    year = {2025}
}

@article{Atanbori_2025_Artificial_Neural_Networks,
    author = {Atanbori, John and Frantzidis, Christos A. and Al-Khafajiy, Mohammed and Aliyu, Aliyu and Sohani, Behnaz and Appiah, Kofi and Moore, Harriet and Sanders, Catherine and Ward, Alastair I.},
    doi = {10.1016/j.neucom.2025.129892},
    issn = {0925-2312},
    journal = {Neurocomputing},
    month = {June},
    pages = {129892},
    publisher = {Elsevier BV},
    title = {Learning with noisy labels for classifying biological echoes in polarimetric weather radar observations using artificial neural networks},
    url = {http://dx.doi.org/10.1016/j.neucom.2025.129892},
    volume = {634},
    year = {2025}
}

@conference{11577_3549116_Predict_Human_Motion,
    author = {Rudenko, A. and Zhu, Y. and Rodrigues de Almeida, T. and Schreiter, T. and Castri, L. and Bellotto, N. and Linder, T. and Vaskevicius, N. and Palmieri, L. and Magnusson, M. and Lilienthal, A. J.},
    booktitle = {German Robotics Conference},
    title = {Hierarchical System to Predict Human Motion and Intentions for Efficient and Safe Human-Robot Interaction in Industrial Environments},
    year = {2025}
}

@article{Benos_2025_Human_Activity_Recognition,
    author = {Benos, Lefteris and Tsaopoulos, Dimitrios and Tagarakis, Aristotelis C. and Kateris, Dimitrios and Busato, Patrizia and Bochtis, Dionysis},
    doi = {10.3390/app15020650},
    issn = {2076-3417},
    journal = {Applied Sciences},
    month = {January},
    number = {2},
    pages = {650},
    publisher = {MDPI AG},
    title = {Explainable AI-Enhanced Human Activity Recognition for Human–Robot Collaboration in Agriculture},
    url = {http://dx.doi.org/10.3390/app15020650},
    volume = {15},
    year = {2025}
}

@article{Abdal_Hafeth_2025_Iot_Contextual_Understanding,
    author = {Abdal Hafeth, Deema and Al-Khafajiy, Mohammed and Kollias, Stefanos},
    doi = {10.1109/jiot.2024.3492066},
    issn = {2372-2541},
    journal = {IEEE Internet of Things Journal},
    month = {March},
    number = {6},
    pages = {6519–6535},
    publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
    title = {EdgeScan for IoT Contextual Understanding With Edge Computing and Image Captioning},
    url = {http://dx.doi.org/10.1109/JIOT.2024.3492066},
    volume = {12},
    year = {2025}
}

@misc{https://doi.org/10.48550/arxiv.2503.08843_Outdoor_Agricultural_Environments,
    author = {de Silva, Rajitha and Cox, Jonathan and Popovic, Marija and Cadena, Cesar and Stachniss, Cyrill and Polvara, Riccardo},
    copyright = {Creative Commons Attribution 4.0 International},
    doi = {10.48550/ARXIV.2503.08843},
    keywords = {Computer Vision and Pattern Recognition (cs.CV), Robotics (cs.RO), FOS: Computer and information sciences, FOS: Computer and information sciences},
    publisher = {arXiv},
    title = {Keypoint Semantic Integration for Improved Feature Matching in Outdoor Agricultural Environments},
    url = {https://arxiv.org/abs/2503.08843},
    year = {2025}
}

@article{Petropoulos_2025_Organic_Carbon_Assessment,
    author = {Petropoulos, Theodoros and Benos, Lefteris and Busato, Patrizia and Kyriakarakos, George and Kateris, Dimitrios and Aidonis, Dimitrios and Bochtis, Dionysis},
    doi = {10.3390/agriculture15050567},
    issn = {2077-0472},
    journal = {Agriculture},
    month = {March},
    number = {5},
    pages = {567},
    publisher = {MDPI AG},
    title = {Soil Organic Carbon Assessment for Carbon Farming: A Review},
    url = {http://dx.doi.org/10.3390/agriculture15050567},
    volume = {15},
    year = {2025}
}

@article{Poozhiyil_2025_Autonomous_Robotic_Sorting_Autonomous_Robotic_Sorting,
    author = {Poozhiyil, Mithun and Argin, Omer F and Rai, Mini and Esfahani, Amir G and Hanheide, Marc and King, Ryan and Saunderson, Phil and Moulin-Ramsden, Mike and Yang, Wen and García, Laura Palacio and Mackay, Iain and Mishra, Abhishek and Okamoto, Sho and Yeung, Kelvin},
    doi = {10.20944/preprints202501.1606.v1},
    month = {January},
    publisher = {MDPI AG},
    title = {A Framework for Real-Time Autonomous Robotic Sorting and Segregation of Nuclear Waste: Modelling, Identification, and Control of Dexter Robot},
    url = {http://dx.doi.org/10.20944/preprints202501.1606.v1},
    year = {2025}
}

@article{Asiminari_2025_Geometric_Field_Indices,
    author = {Asiminari, Gavriela and Benos, Lefteris and Kateris, Dimitrios and Busato, Patrizia and Achillas, Charisios and Grøn Sørensen, Claus and Pearson, Simon and Bochtis, Dionysis},
    doi = {10.3390/agriengineering7030075},
    issn = {2624-7402},
    journal = {AgriEngineering},
    month = {March},
    number = {3},
    pages = {75},
    publisher = {MDPI AG},
    title = {Simplifying Field Traversing Efficiency Estimation Using Machine Learning and Geometric Field Indices},
    url = {http://dx.doi.org/10.3390/agriengineering7030075},
    volume = {7},
    year = {2025}
}

@conference{11577_3537866_Autonomous_Mobile_Systems,
    abstract = {The ability to perceive traversable regions is a crucial prerequisite for truly autonomous mobile systems. In our previous work [8], we introduced P-SVM, a 3D LiDAR-based traversability analysis system that achieves real-time performance in CPU. However, by design P-SVM does not capture features other than geometric features, limiting its performance and generalizability to complex environments. In this work, we introduce an augmented point cloud descriptor that further improves the performance of P-SVM. By exploiting additional features extracted from remission and height information in the point cloud, our model is able to adapt robustly to strong scene changes. Additionally, in the proposed descriptor, remission radial distribution features are introduced to capture local information around keypoints. This addresses the limitation of P-SVM, which focuses only on global features within the cell. Our new P-SVM2 model demonstrates performance almost on par with state-of-the-art deep learning-based methods on the challenging SemanticKITTI [1] dataset in the traversability analysis task. Notably, P-SVM2 is real-time and relies solely on mobile-level CPUs. Moreover, surprising results have been obtained in robustness and generalizability experiments.},
    author = {Wm, Li and Fusaro, D and Olivastri, E and Mosco, S and Bellotto, N and Pretto, A},
    booktitle = {Proceedings of 11th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) / 11th IEEE International Conference on Robotics, Automation and Mechatronics (RAM)},
    doi = {10.1109/CIS-RAM61939.2024.10672725},
    keywords = {Traversability analysis; point cloud descriptor; real-time performance; autonomous mobile systems},
    pages = {352--359},
    publisher = {IEEE},
    title = {P-SVM2: Enhancing LiDAR-based Traversability Analysis with Augmented Point Cloud Descriptor for Autonomous Mobile Systems},
    year = {2024}
}

The ability to perceive traversable regions is a crucial prerequisite for truly autonomous mobile systems. In our previous work [8], we introduced P-SVM, a 3D LiDAR-based traversability analysis system that achieves real-time performance in CPU. However, by design P-SVM does not capture features other than geometric features, limiting its performance and generalizability to complex environments. In this work, we introduce an augmented point cloud descriptor that further improves the performance of P-SVM. By exploiting additional features extracted from remission and height information in the point cloud, our model is able to adapt robustly to strong scene changes. Additionally, in the proposed descriptor, remission radial distribution features are introduced to capture local information around keypoints. This addresses the limitation of P-SVM, which focuses only on global features within the cell. Our new P-SVM2 model demonstrates performance almost on par with state-of-the-art deep learning-based methods on the challenging SemanticKITTI [1] dataset in the traversability analysis task. Notably, P-SVM2 is real-time and relies solely on mobile-level CPUs. Moreover, surprising results have been obtained in robustness and generalizability experiments.

@conference{11577_3515927_Spatial_Interaction_Scenarios,
    author = {Castri, L. and Beraldo, G. and Mghames, S. and Hanheide, M. and Bellotto, N.},
    booktitle = {Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)},
    doi = {10.1109/RO-MAN60168.2024.10731290},
    title = {Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios},
    year = {2024}
}

@article{Almaghout_2024_Cable_Shape_Control,
    author = {Almaghout, Karam and Klimchik, Alexandr},
    doi = {10.3390/robotics13010018},
    issn = {2218-6581},
    journal = {Robotics},
    month = {January},
    number = {1},
    pages = {18},
    publisher = {MDPI AG},
    title = {Manipulation Planning for Cable Shape Control},
    url = {http://dx.doi.org/10.3390/robotics13010018},
    volume = {13},
    year = {2024}
}

@article{Polvara2024_Adaptive_Robot_Localization,
    author = {Hroob, I. and Molina, S. and Polvara, R. and Cielniak, G. and Hanheide, M.},
    journal = {Robotics and Autonomous Systems},
    title = {Adaptive robot localization in dynamic environments through self-learnt long-term 3D stable points segmentation},
    volume = {181},
    year = {2024}
}

@inbook{James_2024_Plant_Stem_Phenotyping,
    author = {James, Katherine Margaret Frances and Cielniak, Grzegorz},
    booktitle = {Towards Autonomous Robotic Systems},
    doi = {10.1007/978-3-031-72059-8_30},
    isbn = {9783031720598},
    issn = {1611-3349},
    month = {December},
    pages = {355–366},
    publisher = {Springer Nature Switzerland},
    title = {Unsupervised Clustering with Geometric Shape Priors for Improved Occlusion Handling in Plant Stem Phenotyping},
    url = {http://dx.doi.org/10.1007/978-3-031-72059-8_30},
    year = {2024}
}

@article{Polvara2024_Ltsnet_Endtoend_Unsupervised,
    author = {Hroob, I. and Molina, S. and Polvara, R. and Cielniak, G. and Hanheide, M.},
    journal = {Lecture Notes in Networks and Systems},
    pages = {17-29},
    title = {LTS-NET: End-to-End Unsupervised Learning of~Long-Term 3D Stable Objects},
    volume = {794},
    year = {2024}
}

@article{Cielniak2024_Agriculture_Postspraying_Evaluation,
    author = {Rogers, H. and Zebin, T. and Cielniak, G. and De La Iglesia, B. and Magri, B.},
    journal = {arXiv},
    title = {Deep Learning for Precision Agriculture: Post-Spraying Evaluation and Deposition Estimation},
    year = {2024}
}

@article{Cielniak2024_Adaptive_Robot_Localization,
    author = {Hroob, I. and Molina, S. and Polvara, R. and Cielniak, G. and Hanheide, M.},
    journal = {Robotics and Autonomous Systems},
    title = {Adaptive robot localization in dynamic environments through self-learnt long-term 3D stable points segmentation},
    volume = {181},
    year = {2024}
}

@article{Kyriakarakos_2024_Farming_Bridging_Technology,
    author = {Kyriakarakos, George and Petropoulos, Theodoros and Marinoudi, Vasso and Berruto, Remigio and Bochtis, Dionysis},
    doi = {10.3390/su16051903},
    issn = {2071-1050},
    journal = {Sustainability},
    month = {February},
    number = {5},
    pages = {1903},
    publisher = {MDPI AG},
    title = {Carbon Farming: Bridging Technology Development with Policy Goals},
    url = {http://dx.doi.org/10.3390/su16051903},
    volume = {16},
    year = {2024}
}

@article{Benos_2024_Human_Activity_Recognition,
    author = {Benos, Lefteris and Tsaopoulos, Dimitrios and Tagarakis, Aristotelis C. and Kateris, Dimitrios and Bochtis, Dionysis},
    doi = {10.3390/app14188520},
    issn = {2076-3417},
    journal = {Applied Sciences},
    month = {September},
    number = {18},
    pages = {8520},
    publisher = {MDPI AG},
    title = {Optimal Sensor Placement and Multimodal Fusion for Human Activity Recognition in Agricultural Tasks},
    url = {http://dx.doi.org/10.3390/app14188520},
    volume = {14},
    year = {2024}
}

@article{Castri_2024_Candoit_Causal_Discovery,
    author = {Castri, Luca and Mghames, Sariah and Hanheide, Marc and Bellotto, Nicola},
    doi = {10.1002/aisy.202400181},
    issn = {2640-4567},
    journal = {Advanced Intelligent Systems},
    month = {November},
    number = {12},
    publisher = {Wiley},
    title = {CAnDOIT: Causal Discovery with Observational and Interventional Data from Time Series},
    url = {http://dx.doi.org/10.1002/aisy.202400181},
    volume = {6},
    year = {2024}
}

@article{Asiminari_2024_Integrated_Routeplanning_System,
    author = {Asiminari, Gavriela and Moysiadis, Vasileios and Kateris, Dimitrios and Busato, Patrizia and Wu, Caicong and Achillas, Charisios and Sørensen, Claus Grøn and Pearson, Simon and Bochtis, Dionysis},
    doi = {10.3390/agriengineering6010039},
    issn = {2624-7402},
    journal = {AgriEngineering},
    month = {March},
    number = {1},
    pages = {657–677},
    publisher = {MDPI AG},
    title = {Integrated Route-Planning System for Agricultural Robots},
    url = {http://dx.doi.org/10.3390/agriengineering6010039},
    volume = {6},
    year = {2024}
}

@conference{11577_3515926_Causal_Analysis_Framework,
    author = {Castri, L. and Beraldo, G. and Mghames, S. and Hanheide, M. and Bellotto, N.},
    booktitle = {Workshop on Causal Learning for Human-Robot Interaction (Causal-HRI), ACM/IEEE International Conference on Human-Robot Interaction (HRI)},
    title = {ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications},
    year = {2024}
}

@article{Hanheide2024_Ltsnet_Endtoend_Unsupervised,
    author = {Hroob, I. and Molina, S. and Polvara, R. and Cielniak, G. and Hanheide, M.},
    journal = {Lecture Notes in Networks and Systems},
    pages = {17-29},
    title = {LTS-NET: End-to-End Unsupervised Learning of~Long-Term 3D Stable Objects},
    volume = {794},
    year = {2024}
}

@article{Hu_2024_Attnestedunet_Sugar_Beet,
    author = {Hu, xinzhi and Jeon, Wang-Su and Cielniak, Grezgorz and Rhee, Sang-Yong},
    doi = {10.5391/ijfis.2024.24.1.1},
    issn = {2093-744X},
    journal = {INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS},
    month = {March},
    number = {1},
    pages = {1–9},
    publisher = {Korean Institute of Intelligent Systems},
    title = {ATT-NestedUnet: Sugar Beet and Weed Detection Using Semantic Segmentation},
    url = {http://dx.doi.org/10.5391/ijfis.2024.24.1.1},
    volume = {24},
    year = {2024}
}

@article{Marinoudi_2024_Agricultural_Labor_Market,
    author = {Marinoudi, Vasso and Benos, Lefteris and Camacho Villa, Carolina and Lampridi, Maria and Kateris, Dimitrios and Berruto, Remigio and Pearson, Simon and Sørensen, Claus Grøn and Bochtis, Dionysis},
    doi = {10.3390/su16167061},
    issn = {2071-1050},
    journal = {Sustainability},
    month = {August},
    number = {16},
    pages = {7061},
    publisher = {MDPI AG},
    title = {Adapting to the Agricultural Labor Market Shaped by Robotization},
    url = {http://dx.doi.org/10.3390/su16167061},
    volume = {16},
    year = {2024}
}

@article{Lampridi_2024_Carbon_Footprint_Tracking,
    author = {Lampridi, Maria and Kateris, Dimitrios and Myresiotis, Charalampos and Berruto, Remigio and Fragos, Vassilios and Kotsopoulos, Thomas and Bochtis, Dionysis},
    doi = {10.3390/agronomy14102241},
    issn = {2073-4395},
    journal = {Agronomy},
    month = {September},
    number = {10},
    pages = {2241},
    publisher = {MDPI AG},
    title = {Leveraging Digital Technologies for Carbon Footprint Tracking in Perennial Cultivations: A Case Study of Walnut Orchard Establishment in Central Greece},
    url = {http://dx.doi.org/10.3390/agronomy14102241},
    volume = {14},
    year = {2024}
}

@article{Hanheide2024_Learning_Manipulation_Tasks,
    author = {Arunachalam, H. and Hanheide, M. and Mghames, S.},
    journal = {arXiv},
    title = {Learning Manipulation Tasks in Dynamic and Shared 3D Spaces},
    year = {2024}
}

@article{Hanheide2024_Human_Injury_Assessment,
    author = {Guevara, L. and Khalid, M. and Hanheide, M. and Parsons, S.},
    journal = {Computers and Electronics in Agriculture},
    title = {Probabilistic model-checking of collaborative robots: A human injury assessment in agricultural applications},
    volume = {222},
    year = {2024}
}

@article{Cielniak2024_Industrial_Mobile_Robots,
    author = {Molina, S. and Mannucci, A. and Magnusson, M. and Adolfsson, D. and Andreasson, H. and Hamad, M. and Abdolshah, S. and Chadalavada, R.T. and Palmieri, L. and Linder, T. and Swaminathan, C.S. and Kucner, T.P. and Hanheide, M. and Fernandez-Carmona, M. and Cielniak, G. and Duckett, T. and Pecora, F. and Bokesand, S. and Arras, K.O. and Haddadin, S. and Lilienthal, A.J.},
    journal = {IEEE Robotics and Automation Magazine},
    number = {3},
    pages = {48-59},
    title = {The ILIAD Safety Stack: Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots},
    volume = {31},
    year = {2024}
}

@article{Atas_2024_Kinodynamic_Motion_Planner,
    author = {Atas, Fetullah and Cielniak, Grzegorz and Grimstad, Lars},
    doi = {10.4173/mic.2024.1.2},
    issn = {1890-1328},
    journal = {Modeling, Identification and Control: A Norwegian Research Bulletin},
    number = {1},
    pages = {15–28},
    publisher = {Norwegian Society of Automatic Control},
    title = {CostTrust: A Fast-Exploring, Iteratively Expanding Frontier-Based Kinodynamic Motion Planner},
    url = {http://dx.doi.org/10.4173/mic.2024.1.2},
    volume = {45},
    year = {2024}
}

@inproceedings{Cox_2024_Advanced_Multimodal_Mapping,
    author = {Cox, Jonathan and Hanheide, Marc and Polvara, Riccardo},
    booktitle = {2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)},
    doi = {10.1109/case59546.2024.10711678},
    month = {August},
    pages = {2531–2536},
    publisher = {IEEE},
    title = {AGRIDS: an Advanced Multi-Modal Mapping Architecture for Robotics and Agriculture},
    url = {http://dx.doi.org/10.1109/case59546.2024.10711678},
    year = {2024}
}

@article{Cielniak2024_Strawberry_Dataset_Laststraw,
    author = {James, K.M.F. and Heiwolt, K. and Sargent, D.J. and Cielniak, G.},
    journal = {arXiv},
    title = {Lincoln{'}s Annotated Spatio-Temporal Strawberry Dataset (LAST-Straw)},
    year = {2024}
}

@inbook{Espejel_Flores_2024_Tomato_Harvesting_Scenarios,
    author = {Espejel Flores, Juan Pablo and Yilmaz, Abdurrahman and Soriano Avendaño, Luis Arturo and Cielniak, Grzegorz},
    booktitle = {Towards Autonomous Robotic Systems},
    doi = {10.1007/978-3-031-72059-8_2},
    isbn = {9783031720598},
    issn = {1611-3349},
    month = {December},
    pages = {15–27},
    publisher = {Springer Nature Switzerland},
    title = {Comparative Analysis of Unity and Gazebo Simulators for Digital Twins of Robotic Tomato Harvesting Scenarios},
    url = {http://dx.doi.org/10.1007/978-3-031-72059-8_2},
    year = {2024}
}

@conference{11577_3529284_Continual_Object_Detection,
    author = {Pasti, F. and Ceccon, M. and Dalle Pezze, D. and Paissan, F. and Farella, E. and Susto, G. A. and Bellotto, N.},
    booktitle = {Workshop on Computational Aspects of Deep Learning, European Conference on Computer Vision (ECCV)},
    title = {Latent Distillation for Continual Object Detection at the Edge},
    year = {2024}
}

@article{Cielniak2024_Oscillatory_Plant_Dynamics,
    author = {Wagner, N. and Cielniak, G.},
    journal = {IEEE International Conference on Automation Science and Engineering},
    pages = {3278-3283},
    title = {Mass Estimation of Soft Fruit via Oscillatory Plant Dynamics},
    year = {2024}
}

@article{Stavridis_2024_Advanced_Cognitive_Capabilities,
    author = {Stavridis, Sotiris and Droukas, Leonidas and Doulgeri, Zoe and Papageorgiou, Dimitrios and Dimeas, Fotios and Soriano, Ángel and Molina, Sergi and Deiri, Sami Ahmed and Hutchinson, Michael and Pulido-Fentanes, Jaime and Hroob, Ibrahim and Polvara, Riccardo and Hanheide, Marc and Cielniak, Grzegorz and Samarinas, Nikiforos and Kateris, Dimitrios and Bochtis, Dionysis and Peleka, Georgia and Papadam, Stefanos and Triantafyllou, Dimitra and Papadimitriou, Alexios and Papadopoulos, Christos and Mariolis, Ioannis and Giakoumis, Dimitrios and Tzovaras, Dimitrios},
    doi = {10.1109/mra.2024.3487324},
    issn = {1558-223X},
    journal = {IEEE Robotics & Automation Magazine},
    pages = {2–14},
    publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
    title = {Robotic Grape Inspection and Selective Harvesting in Vineyards: A Multisensory Robotic System With Advanced Cognitive Capabilities},
    url = {http://dx.doi.org/10.1109/mra.2024.3487324},
    year = {2024}
}

@inbook{Williams_2024_Pretrained_Visual_Representations,
    author = {Williams, Emlyn and Polydoros, Athanasios},
    booktitle = {Towards Autonomous Robotic Systems},
    doi = {10.1007/978-3-031-72059-8_6},
    isbn = {9783031720598},
    issn = {1611-3349},
    month = {December},
    pages = {60–71},
    publisher = {Springer Nature Switzerland},
    title = {Pretrained Visual Representations in Reinforcement Learning},
    url = {http://dx.doi.org/10.1007/978-3-031-72059-8_6},
    year = {2024}
}

@inproceedings{Zhou_2024_Open_Source_Components,
    author = {Zhou, Liyou and Sinavski, Oleg and Polydoros, Athanasios},
    booktitle = {2024 Eighth IEEE International Conference on Robotic Computing (IRC)},
    doi = {10.1109/irc63610.2024.00031},
    month = {December},
    pages = {131–138},
    publisher = {IEEE},
    title = {Robotic Learning in your Backyard: A Neural Simulator from Open Source Components},
    url = {http://dx.doi.org/10.1109/irc63610.2024.00031},
    year = {2024}
}

@article{Das_2024_Unified_Topological_Representation,
    author = {Das, Gautham and Cielniak, Grzegorz and Heselden, James and Pearson, Simon and Duchetto, Francesco Del and Zhu, Zuyuan and Dichtl, Johann and Hanheide, Marc and Fentanes, Jaime Pulido and Binch, Adam and Hutchinson, Michael and From, Pal},
    doi = {10.1002/rob.22494},
    issn = {1556-4967},
    journal = {Journal of Field Robotics},
    month = {December},
    publisher = {Wiley},
    title = {A Unified Topological Representation for Robotic Fleets in Agricultural Applications},
    url = {http://dx.doi.org/10.1002/rob.22494},
    year = {2024}
}

@article{Hanheide2024_Causal_Analysis_Framework,
    author = {Castri, L. and Beraldo, G. and Mghames, S. and Hanheide, M. and Bellotto, N.},
    journal = {arXiv},
    title = {ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications},
    year = {2024}
}

@article{Fernandez_Carmona_2024_Waveletbased_Temporal_Models,
    author = {Fernandez-Carmona, Manuel and Mghames, Sariah and Bellotto, Nicola},
    doi = {10.3233/ais-230144},
    issn = {1876-1364},
    journal = {Journal of Ambient Intelligence and Smart Environments},
    month = {June},
    number = {2},
    pages = {181–200},
    publisher = {SAGE Publications},
    title = {Wavelet-based temporal models of human activity for anomaly detection in smart robot-assisted environments1},
    url = {http://dx.doi.org/10.3233/AIS-230144},
    volume = {16},
    year = {2024}
}

@article{Cielniak2024_Advancing_Precision_Agriculture,
    author = {Rogers, H. and De La Iglesia, B. and Zebin, T. and Cielniak, G. and Magri, B.},
    journal = {Neural Computing and Applications},
    number = {32},
    pages = {20211-20229},
    title = {Advancing precision agriculture: domain-specific augmentations and robustness testing for convolutional neural networks in precision spraying evaluation},
    volume = {36},
    year = {2024}
}

@article{Hanheide2024_Robotic_Fleet_Deployment,
    author = {Zhu, Z. and Das, G.P. and Hanheide, M.},
    journal = {Lecture Notes in Networks and Systems},
    pages = {285-301},
    title = {On Optimising Topology of Agricultural Fields for Efficient Robotic Fleet Deployment},
    volume = {794},
    year = {2024}
}

@article{Morellos_2024_Health_Status_Monitoring,
    author = {Morellos, Antonios and Dolaptsis, Konstantinos and Tziotzios, Georgios and Pantazi, Xanthoula Eirini and Kateris, Dimitrios and Berruto, Remigio and Bochtis, Dionysis},
    doi = {10.3390/app14031049},
    issn = {2076-3417},
    journal = {Applied Sciences},
    month = {January},
    number = {3},
    pages = {1049},
    publisher = {MDPI AG},
    title = {An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevines},
    url = {http://dx.doi.org/10.3390/app14031049},
    volume = {14},
    year = {2024}
}

@article{11577_3515923_Editorial_Swarm_Neurorobots,
    author = {Hu, C. and Arvin, F. and Bellotto, N. and Yue, S. and Li, H.},
    doi = {10.3389/fnbot.2024.1386178},
    journal = {FRONTIERS IN NEUROROBOTICS},
    keywords = {bio-inspired; environmental perception; neurorobotic; real-world deployment; swarm},
    publisher = {FRONTIERS MEDIA SA},
    title = {Editorial: Swarm neuro-robots with the bio-inspired environmental perception},
    volume = {18},
    year = {2024}
}

@misc{heselden2024unifiedmaphandlingrobotic_Robotic_Systems_Enhancing,
    archiveprefix = {arXiv},
    author = {James R. Heselden and Gautham P. Das},
    eprint = {2404.13499},
    primaryclass = {cs.RO},
    title = {Unified Map Handling for Robotic Systems: Enhancing Interoperability and Efficiency Across Diverse Environments},
    url = {https://arxiv.org/abs/2404.13499},
    year = {2024}
}

@article{Cielniak2024_Biomass_Estimation_Based,
    author = {Singh, J. and Cielniak, G.},
    journal = {IEEE International Conference on Automation Science and Engineering},
    pages = {3268-3273},
    title = {Non-Destructive Biomass Estimation Based on 3D Reconstruction From A Handheld Camera},
    year = {2024}
}

@article{Zahidi_2024_Optimising_Robotic_Operation,
    author = {Zahidi, Usman A. and Khan, Arshad and Zhivkov, Tsvetan and Dichtl, Johann and Li, Dom and Parsa, Soran and Hanheide, Marc and Cielniak, Grzegorz and Sklar, Elizabeth I. and Pearson, Simon and Ghalamzan‐E., Amir},
    doi = {10.1002/rob.22384},
    issn = {1556-4967},
    journal = {Journal of Field Robotics},
    month = {July},
    number = {8},
    pages = {2771–2789},
    publisher = {Wiley},
    title = {Optimising robotic operation speed with edge computing via 5G network: Insights from selective harvesting robots},
    url = {http://dx.doi.org/10.1002/rob.22384},
    volume = {41},
    year = {2024}
}

@inproceedings{Kateris_2024_Cattle_Breeding_Units,
    author = {Kateris, Dimitrios and Mitsopoulos, Anastasios and Petkoglou, Charalampos and Bochtis, Dionysis},
    booktitle = {The 17th International Conference of the Hellenic Association of Agricultural Economists},
    collection = {ETAGRO 2023},
    doi = {10.3390/proceedings2024094013},
    month = {January},
    pages = {13},
    publisher = {MDPI},
    series = {ETAGRO 2023},
    title = {Lameness Identification System in Cattle Breeding Units},
    url = {http://dx.doi.org/10.3390/proceedings2024094013},
    year = {2024}
}

@article{Hanheide2024_Stable_Points_Segmentation,
    author = {Hroob, I. and Mersch, B. and Stachniss, C. and Hanheide, M.},
    journal = {IEEE Robotics and Automation Letters},
    number = {4},
    pages = {3546-3553},
    title = {Generalizable Stable Points Segmentation for 3D LiDAR Scan-to-Map Long-Term Localization},
    volume = {9},
    year = {2024}
}

@conference{11577_3529302_Human_Motion_Prediction,
    author = {Mghames, Sariah and Castri, Luca and Hanheide, Marc and Bellotto, Nicola},
    booktitle = {IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE International Conference on Robotics, Automation and Mechatronics (RAM)},
    doi = {10.1109/cis-ram61939.2024.10672815},
    pages = {57--62},
    title = {neuROSym: Deployment and Evaluation of a ROS-based Neuro-Symbolic Model for Human Motion Prediction},
    year = {2024}
}

@article{Cielniak2024_Vision_Based_Crop,
    author = {de Silva, R. and Cielniak, G. and Gao, J.},
    journal = {Computers and Electronics in Agriculture},
    title = {Vision based crop row navigation under varying field conditions in arable fields},
    volume = {217},
    year = {2024}
}

@article{Tagarakis_2024_Forestry_A_Review,
    author = {Tagarakis, Aristotelis C. and Benos, Lefteris and Kyriakarakos, George and Pearson, Simon and Sørensen, Claus Grøn and Bochtis, Dionysis},
    doi = {10.3390/s24103117},
    issn = {1424-8220},
    journal = {Sensors},
    month = {May},
    number = {10},
    pages = {3117},
    publisher = {MDPI AG},
    title = {Digital Twins in Agriculture and Forestry: A Review},
    url = {http://dx.doi.org/10.3390/s24103117},
    volume = {24},
    year = {2024}
}

@article{Moysiadis_2024_Dynamic_Movement_Recognition,
    author = {Moysiadis, Vasileios and Benos, Lefteris and Karras, George and Kateris, Dimitrios and Peruzzi, Andrea and Berruto, Remigio and Papageorgiou, Elpiniki and Bochtis, Dionysis},
    doi = {10.3390/agriengineering6030146},
    issn = {2624-7402},
    journal = {AgriEngineering},
    month = {August},
    number = {3},
    pages = {2494–2512},
    publisher = {MDPI AG},
    title = {Human–Robot Interaction through Dynamic Movement Recognition for Agricultural Environments},
    url = {http://dx.doi.org/10.3390/agriengineering6030146},
    volume = {6},
    year = {2024}
}

@article{Cielniak2024_Channel_Randomisation_Selfsupervised,
    author = {Choi, T. and Would, O. and Salazar-Gomez, A. and Liu, X. and Cielniak, G.},
    journal = {Computers and Electronics in Agriculture},
    title = {Channel randomisation: Self-supervised representation learning for reliable visual anomaly detection in speciality crops},
    volume = {226},
    year = {2024}
}

@article{Hanheide2024_Spatial_Interaction_Scenarios,
    author = {Castri, L. and Beraldo, G. and Mghames, S. and Hanheide, M. and Bellotto, N.},
    journal = {arXiv},
    title = {Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios},
    year = {2024}
}

@article{Hardy_2024_Annotated_Broccoli_Heads,
    author = {Hardy, Oliver and Seemakurthy, Karthik and Sklar, Elizabeth I.},
    doi = {10.3390/agronomy14050964},
    issn = {2073-4395},
    journal = {Agronomy},
    month = {May},
    number = {5},
    pages = {964},
    publisher = {MDPI AG},
    title = {A Comparative Dataset of Annotated Broccoli Heads Recorded with Depth Cameras from a Moving Vehicle},
    url = {http://dx.doi.org/10.3390/agronomy14050964},
    volume = {14},
    year = {2024}
}

@article{Cielniak2024_Dualband_Feature_Selection,
    author = {Zahidi, U.A. and ?ukasik, K. and Cielniak, G.},
    journal = {arXiv},
    title = {Dual-band feature selection for maturity classification of specialty crops by hyperspectral imaging},
    year = {2024}
}

@article{Cielniak2023,title = {Navigating in 3D Uneven Environments Through Supervoxels and Nonlinear MPC},journal = {Proceedings of the 11th European Conference on Mobile Robots, ECMR 2023},year = {2023},author = {Atas, F. and Cielniak, G. and Grimstad, L.}}

@article{Heselden_2023, title={Heuristics and Rescheduling in Prioritised Multi-Robot Path Planning: A Literature Review}, volume={11}, ISSN={2075-1702}, url={http://dx.doi.org/10.3390/machines11111033}, DOI={10.3390/machines11111033}, number={11}, journal={Machines}, publisher={MDPI AG}, author={Heselden, James and Das, Gautham}, year={2023}, month=nov, pages={1033} }

@article{Hanheide2023,title = {Smart Parking System Using Heuristic Optimization for~Autonomous Transportation Robots in~Agriculture},journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},year = {2023},volume = {14136 LNAI},pages = {38-50},author = {Ravikanna, R. and Heselden, J. and Khan, M.A. and Perrett, A. and Zhu, Z. and Das, G. and Hanheide, M.}}

@article{Cielniak2023_Risk_Management_Method,
    author = {Mayoral Ba{\~n}os, J.C. and From, P.J. and Cielniak, G.},
    journal = {Robotics},
    number = {3},
    title = {Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method},
    volume = {12},
    year = {2023}
}

@article{Cielniak2023_Foroutdoor_Robot_Navigation,
    author = {Atas, F. and Cielniak, G. and Grimstad, L.},
    journal = {Lecture Notes in Networks and Systems},
    pages = {231-243},
    title = {Benchmark of~Sampling-Based Optimizing Planners for~Outdoor Robot Navigation},
    volume = {577 LNNS},
    year = {2023}
}

@article{darbyshire2023towards_Practical_Object_Detection,
    author = {Darbyshire, Madeleine and Salazar-Gomez, Adrian and Gao, Junfeng and Sklar, Elizabeth I and Parsons, Simon},
    journal = {Frontiers in Plant Science},
    publisher = {Frontiers Media SA},
    title = {Towards practical object detection for weed spraying in precision agriculture},
    volume = {14},
    year = {2023}
}

@article{castagna2023eqrbot_Eqr_Argumentbased_Explanations,
    author = {Castagna, Federico and Garton, Alexandra and McBurney, Peter and Parsons, Simon and Sassoon, Isabel and Sklar, Elizabeth I},
    journal = {Frontiers in Artificial Intelligence},
    pages = {1045614},
    publisher = {Frontiers},
    title = {EQRbot: A chatbot delivering EQR argument-based explanations},
    volume = {6},
    year = {2023}
}

@article{Navrozidis_2023_Disease_Detection_Tasks,
    author = {Navrozidis, Ioannis and Pantazi, Xanthoula Eirini and Lagopodi, Anastasia and Bochtis, Dionysios and Alexandridis, Thomas K.},
    doi = {10.3390/rs15245683},
    issn = {2072-4292},
    journal = {Remote Sensing},
    month = {December},
    number = {24},
    pages = {5683},
    publisher = {MDPI AG},
    title = {Application of Machine Learning for Disease Detection Tasks in Olive Trees Using Hyperspectral Data},
    url = {http://dx.doi.org/10.3390/rs15245683},
    volume = {15},
    year = {2023}
}

@article{Hanheide2023_Continuously_Changing_Environments,
    author = {Hroob, I. and Molina, S. and Polvara, R. and Cielniak, G. and Hanheide, M.},
    journal = {Proceedings of the 11th European Conference on Mobile Robots, ECMR 2023},
    title = {Learned Long-Term Stability Scan Filtering for Robust Robot Localisation in Continuously Changing Environments},
    year = {2023}
}

@article{Perrett_2023_Deepverge_Classification,
    author = {Perrett, Andrew and Pollard, Harry and Barnes, Charlie and Schofield, Mark and Qie, Lan and Bosilj, Petra and Brown, James M.},
    doi = {10.1016/j.compenvurbsys.2023.101968},
    issn = {0198-9715},
    journal = {Computers, Environment and Urban Systems},
    month = {June},
    pages = {101968},
    publisher = {Elsevier BV},
    title = {DeepVerge: Classification of roadside verge biodiversity and conservation potential},
    url = {http://dx.doi.org/10.1016/j.compenvurbsys.2023.101968},
    volume = {102},
    year = {2023}
}

@article{darbyshire2023hierarchical_Maskformer_Panoptic_Segmentation,
    author = {Darbyshire, Madeleine and Sklar, Elizabeth and Parsons, Simon},
    journal = {arXiv preprint arXiv:2310.06582},
    title = {Hierarchical Mask2Former: Panoptic Segmentation of Crops, Weeds and Leaves},
    year = {2023}
}

@article{Zhivkov_2023_Farm_Evaluating_Wireless,
    author = {Zhivkov, Tsvetan and Sklar, Elizabeth I. and Botting, Duncan and Pearson, Simon},
    doi = {10.3390/machines11121064},
    issn = {2075-1702},
    journal = {Machines},
    month = {November},
    number = {12},
    pages = {1064},
    publisher = {MDPI AG},
    title = {5G on the Farm: Evaluating Wireless Network Capabilities and Needs for Agricultural Robotics},
    url = {http://dx.doi.org/10.3390/machines11121064},
    volume = {11},
    year = {2023}
}

@article{Cielniak2023_Agricultural_Robot_Navigation,
    author = {De Silva, R. and Cielniak, G. and Gao, J.},
    journal = {arXiv},
    title = {Leaving the Lines Behind: Vision-Based Crop Row Exit for Agricultural Robot Navigation},
    year = {2023}
}

@article{Hanheide2023_Softfruit_Harvesting_Operations,
    author = {Guevara, L. and Hanheide, M. and Parsons, S.},
    journal = {Journal of Field Robotics},
    title = {Implementation of a human-aware robot navigation module for cooperative soft-fruit harvesting operations},
    year = {2023}
}

@article{Hanheide2023_Survey_Of_Maps,
    author = {Kucner, T.P. and Magnusson, M. and Mghames, S. and Palmieri, L. and Verdoja, F. and Swaminathan, C.S. and Krajn{\'i}k, T. and Schaffernicht, E. and Bellotto, N. and Hanheide, M. and Lilienthal, A.J.},
    journal = {International Journal of Robotics Research},
    number = {11},
    pages = {977-1006},
    title = {Survey of maps of dynamics for mobile robots},
    volume = {42},
    year = {2023}
}

@article{Cielniak2023_Blt_Data_Set,
    author = {Polvara, R. and Molina, S. and Hroob, I. and Papadimitriou, A. and Tsiolis, K. and Giakoumis, D. and Likothanassis, S. and Tzovaras, D. and Cielniak, G. and Hanheide, M.},
    journal = {Journal of Field Robotics},
    title = {Bacchus Long-Term (BLT) data set: Acquisition of the agricultural multimodal BLT data set with automated robot deployment},
    year = {2023}
}

@article{Cielniak2023_Visionbased_Navigation_System,
    author = {de Silva, R. and Cielniak, G. and Gao, J.},
    journal = {arXiv},
    title = {A Vision-Based Navigation System for Arable Fields},
    year = {2023}
}

@article{Hanheide2023_Blt_Data_Set,
    author = {Polvara, R. and Molina, S. and Hroob, I. and Papadimitriou, A. and Tsiolis, K. and Giakoumis, D. and Likothanassis, S. and Tzovaras, D. and Cielniak, G. and Hanheide, M.},
    journal = {Journal of Field Robotics},
    title = {Bacchus Long-Term (BLT) data set: Acquisition of the agricultural multimodal BLT data set with automated robot deployment},
    year = {2023}
}

@article{Das_2023_Unified_Topological_Representation,
    author = {Das, Gautham and Cielniak, Grzegorz and Heselden, James and Pearson, Simon and Duchetto, Francesco Del and Zhu, Zuyuan and Dichtl, Johann and Hanheide, Marc and Fentanes, Jaime Pulido and Binch, Adam and Hutchinson, Michael and From, Pal},
    doi = {10.22541/au.169357512.24867804/v1},
    month = {September},
    publisher = {Authorea, Inc.},
    title = {A Unified Topological Representation for Robotic Fleets in Agricultural Applications},
    url = {http://dx.doi.org/10.22541/au.169357512.24867804/v1},
    year = {2023}
}

@conference{11577_3498542_Human_Motion_Prediction,
    abstract = {Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e.g. robots). In this paper, we present a new approach for context reasoning to further advance the field of human motion prediction. We therefore propose a neuro-symbolic approach for human motion prediction (NeuroSyM), which weights differently the interactions in the neighbourhood by leveraging an intuitive technique for spatial representation called Qualitative Trajectory Calculus (QTC). The proposed approach is experimentally tested on medium and long term time horizons using two architectures from the state of art, one of which is a baseline for human motion prediction and the other is a baseline for generic multivariate time-series prediction. Six datasets of challenging crowded scenarios, collected from both fixed and mobile cameras, were used for testing. Experimental results show that the NeuroSyM approach outperforms in most cases the baseline architectures in terms of prediction accuracy.},
    author = {Mghames, S. and Castri, L. and Hanheide, M. and Bellotto, N.},
    booktitle = {Proceedings of the International Joint Conference on Neural Networks},
    doi = {10.1109/IJCNN54540.2023.10191970},
    isbn = {978-1-6654-8867-9},
    pages = {1--8},
    publisher = {Institute of Electrical and Electronics Engineers Inc.},
    title = {A Neuro-Symbolic Approach for Enhanced Human Motion Prediction},
    volume = {2023-},
    year = {2023}
}

Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e.g. robots). In this paper, we present a new approach for context reasoning to further advance the field of human motion prediction. We therefore propose a neuro-symbolic approach for human motion prediction (NeuroSyM), which weights differently the interactions in the neighbourhood by leveraging an intuitive technique for spatial representation called Qualitative Trajectory Calculus (QTC). The proposed approach is experimentally tested on medium and long term time horizons using two architectures from the state of art, one of which is a baseline for human motion prediction and the other is a baseline for generic multivariate time-series prediction. Six datasets of challenging crowded scenarios, collected from both fixed and mobile cameras, were used for testing. Experimental results show that the NeuroSyM approach outperforms in most cases the baseline architectures in terms of prediction accuracy.

@article{ndekou2023agent_Combat_Antimicrobial_Resistance,
    author = {Ndekou, Paul Python and Drake, Archie and Lomax, Jake and Dione, Michel and Faye, Ardiouma and Nsangou, Mohamed Daly Njiemessa and Korir, Lilian and Sklar, Elizabeth},
    journal = {Science in One Health},
    pages = {100051},
    publisher = {Elsevier},
    title = {An Agent-Based Model for Collaborative Learning to Combat Antimicrobial Resistance: Proof of Concept Based on Broiler Production in Senegal},
    year = {2023}
}

@inproceedings{Seemakurthy_2023_Generalised_Fully_Convolutional,
    author = {Seemakurthy, Karthik and Bosilj, Petra and Aptoula, Erchan and Fox, Charles},
    booktitle = {2023 IEEE International Conference on Robotics and Automation (ICRA)},
    doi = {10.1109/icra48891.2023.10160937},
    month = {May},
    pages = {7002–7009},
    publisher = {IEEE},
    title = {Domain Generalised Fully Convolutional One Stage Detection},
    url = {http://dx.doi.org/10.1109/icra48891.2023.10160937},
    year = {2023}
}

@article{Hanheide2023_Generalise_Vtagexploiting_Perceptual,
    author = {Cox, J. and Tsagkopoulos, N. and Rozsyp{\'a}lek, Z. and Krajn{\'i}k, T. and Sklar, E. and Hanheide, M.},
    journal = {Computers and Electronics in Agriculture},
    title = {Visual teach and generalise (VTAG)?Exploiting perceptual aliasing for scalable autonomous robotic navigation in horticultural environments},
    volume = {212},
    year = {2023}
}

@article{Hanheide2023_Autonomous_Topological_Optimisation,
    author = {Zhu, Z. and Das, G. and Hanheide, M.},
    journal = {Proceedings of the ACM Symposium on Applied Computing},
    pages = {791-799},
    title = {Autonomous Topological Optimisation for Multi-robot Systems in Logistics},
    year = {2023}
}

@inbook{Harman_2023_Agricultural_Workforce_Management,
    author = {Harman, Helen and Sklar, Elizabeth I.},
    booktitle = {Multi-Agent-Based Simulation XXIII},
    doi = {10.1007/978-3-031-22947-3_10},
    isbn = {9783031229473},
    issn = {1611-3349},
    pages = {121–133},
    publisher = {Springer International Publishing},
    title = {Challenges for Multi-Agent Based Agricultural Workforce Management},
    url = {http://dx.doi.org/10.1007/978-3-031-22947-3_10},
    year = {2023}
}

@article{11577_3455200_Distributed_Presynaptic_Connections,
    abstract = {Collision detection is one of the most challenging tasks for unmanned aerial vehicles (UAVs). This is especially true for small or micro-UAVs due to their limited computational power. In nature, flying insects with compact and simple visual systems demonstrate their remarkable ability to navigate and avoid collision in complex environments. A good example of this is provided by locusts. They can avoid collisions in a dense swarm through the activity of a motion-based visual neuron called the Lobula giant movement detector (LGMD). The defining feature of the LGMD neuron is its preference for looming. As a flying insect's visual neuron, LGMD is considered to be an ideal basis for building UAV's collision detecting system. However, existing LGMD models cannot distinguish looming clearly from other visual cues, such as complex background movements caused by UAV agile flights. To address this issue, we proposed a new model implementing distributed spatial-temporal synaptic interactions, which is inspired by recent findings in locusts' synaptic morphology. We first introduced the locally distributed excitation to enhance the excitation caused by visual motion with preferred velocities. Then, radially extending temporal latency for inhibition is incorporated to compete with the distributed excitation and selectively suppress the nonpreferred visual motions. This spatial-temporal competition between excitation and inhibition in our model is, therefore, tuned to preferred image angular velocity representing looming rather than background movements with these distributed synaptic interactions. Systematic experiments have been conducted to verify the performance of the proposed model for UAV agile flights. The results have demonstrated that this new model enhances the looming selectivity in complex flying scenes considerably and has the potential to be implemented on embedded collision detection systems for small or micro-UAVs.},
    author = {Zhao, J. and Wang, H. and Bellotto, N. and Hu, C. and Peng, J. and Yue, S.},
    doi = {10.1109/TNNLS.2021.3106946},
    journal = {IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS},
    keywords = {Visualization; Collision avoidance; Neurons; Computational modeling; Biological system modeling; Feature extraction; Unmanned aerial vehicles; Collision detection; distributed presynaptic connection-based Lobula giant movement detector (D-LGMD); dynamic complex visual scene; presynaptic neural network; unmanned aerial vehicles (UAVs)},
    number = {5},
    pages = {2539--2553},
    publisher = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC},
    title = {Enhancing LGMD's Looming Selectivity for UAV With Spatial-Temporal Distributed Presynaptic Connections},
    volume = {34},
    year = {2023}
}

Collision detection is one of the most challenging tasks for unmanned aerial vehicles (UAVs). This is especially true for small or micro-UAVs due to their limited computational power. In nature, flying insects with compact and simple visual systems demonstrate their remarkable ability to navigate and avoid collision in complex environments. A good example of this is provided by locusts. They can avoid collisions in a dense swarm through the activity of a motion-based visual neuron called the Lobula giant movement detector (LGMD). The defining feature of the LGMD neuron is its preference for looming. As a flying insect's visual neuron, LGMD is considered to be an ideal basis for building UAV's collision detecting system. However, existing LGMD models cannot distinguish looming clearly from other visual cues, such as complex background movements caused by UAV agile flights. To address this issue, we proposed a new model implementing distributed spatial-temporal synaptic interactions, which is inspired by recent findings in locusts' synaptic morphology. We first introduced the locally distributed excitation to enhance the excitation caused by visual motion with preferred velocities. Then, radially extending temporal latency for inhibition is incorporated to compete with the distributed excitation and selectively suppress the nonpreferred visual motions. This spatial-temporal competition between excitation and inhibition in our model is, therefore, tuned to preferred image angular velocity representing looming rather than background movements with these distributed synaptic interactions. Systematic experiments have been conducted to verify the performance of the proposed model for UAV agile flights. The results have demonstrated that this new model enhances the looming selectivity in complex flying scenes considerably and has the potential to be implemented on embedded collision detection systems for small or micro-UAVs.

@article{Polvara2023_Blt_Data_Set,
    author = {Polvara, R. and Molina, S. and Hroob, I. and Papadimitriou, A. and Tsiolis, K. and Giakoumis, D. and Likothanassis, S. and Tzovaras, D. and Cielniak, G. and Hanheide, M.},
    journal = {Journal of Field Robotics},
    title = {Bacchus Long-Term (BLT) data set: Acquisition of the agricultural multimodal BLT data set with automated robot deployment},
    year = {2023}
}

@article{Hanheide2023_Industrial_Mobile_Robots,
    author = {Molina, S. and Mannucci, A. and Magnusson, M. and Adolfsson, D. and Andreasson, H. and Hamad, M. and Abdolshah, S. and Chadalavada, R.T. and Palmieri, L. and Linder, T. and Swaminathan, C.S. and Kucner, T.P. and Hanheide, M. and Fernandez-Carmona, M. and Cielniak, G. and Duckett, T. and Pecora, F. and Bokesand, S. and Arras, K.O. and Haddadin, S. and Lilienthal, A.J.},
    journal = {IEEE Robotics and Automation Magazine},
    title = {The ILIAD Safety Stack: Human-Aware Infrastructure-Free Navigation of Industrial Mobile Robots},
    year = {2023}
}

@article{Jones_2023_Structured_Expert_Elicitation,
    author = {Jones, Aled and Bridle, Sarah and Denby, Katherine and Bhunnoo, Riaz and Morton, Daniel and Stanbrough, Lucy and Coupe, Barnaby and Pilley, Vanessa and Benton, Tim and Falloon, Pete and Matthews, Tom K. and Hasnain, Saher and Heslop-Harrison, John S. and Beard, Simon and Pierce, Julie and Pretty, Jules and Zurek, Monika and Johnstone, Alexandra and Smith, Pete and Gunn, Neil and Watson, Molly and Pope, Edward and Tzachor, Asaf and Douglas, Caitlin and Reynolds, Christian and Ward, Neil and Fredenburgh, Jez and Pettinger, Clare and Quested, Tom and Cordero, Juan Pablo and Mitchell, Clive and Bewick, Carrie and Brown, Cameron and Brown, Christopher and Burgess, Paul J. and Challinor, Andy and Cottrell, Andrew and Crocker, Thomas and George, Thomas and Godfray, Charles J. and Hails, Rosie S. and Ingram, John and Lang, Tim and Lyon, Fergus and Lusher, Simon and MacMillan, Tom and Newton, Sue and Pearson, Simon and Pritchard, Sue and Sanders, Dale and Sanderson Bellamy, Angelina and Steven, Megan and Trickett, Alastair and Voysey, Andrew and Watson, Christine and Whitby, Darren and Whiteside, Kerry},
    doi = {10.3390/su152014783},
    issn = {2071-1050},
    journal = {Sustainability},
    month = {October},
    number = {20},
    pages = {14783},
    publisher = {MDPI AG},
    title = {Scoping Potential Routes to UK Civil Unrest via the Food System: Results of a Structured Expert Elicitation},
    url = {http://dx.doi.org/10.3390/su152014783},
    volume = {15},
    year = {2023}
}

@article{Kateris_2023_Agriculture_Part,
    author = {Kateris, Dimitrios and Bochtis, Dionysis},
    doi = {10.3390/app13074180},
    issn = {2076-3417},
    journal = {Applied Sciences},
    month = {March},
    number = {7},
    pages = {4180},
    publisher = {MDPI AG},
    title = {Applied Agri-Technologies for Agriculture 4.0—Part I},
    url = {http://dx.doi.org/10.3390/app13074180},
    volume = {13},
    year = {2023}
}

@article{Cielniak2023_Features_Inadynamic_Environment,
    author = {Vayakkattil, S. and Cielniak, G. and Calisti, M.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {3-14},
    title = {Plant Phenotyping Using DLT Method: Towards Retrieving the~Delicate Features in~a~Dynamic Environment},
    volume = {14136 LNAI},
    year = {2023}
}

@article{moraru2023mixed_Automated_Weeding_Platform,
    author = {Moraru, Ionut and Zhivkov, Tsvetan and Coutts, Shaun and Li, Dom and Sklar, Elizabeth I},
    title = {A Mixed-Autonomy approach for an Automated Weeding Platform},
    year = {2023}
}

@article{Specht_2023_Digital_Health_Age,
    author = {Specht, Bernhard and Jager, Hana and Garbaya, Samaher and Pincherle, Alessandro and Sarvari, Peiman Alipour and Khadraoui, Djamel and Schneider, Reinhard and Chavarriaga, Ricardo and Tayeb, Zied},
    doi = {10.1101/2023.11.04.23298084},
    month = {November},
    publisher = {Cold Spring Harbor Laboratory},
    title = {Multiple Sclerosis in the Digital Health Age: Challenges and Opportunities - A Systematic Review},
    url = {http://dx.doi.org/10.1101/2023.11.04.23298084},
    year = {2023}
}

@inproceedings{Paul_2023_Sheet_Metal_Forming,
    author = {Paul, Eldho and Bharti, Sahil and Uthama, Anandu and Boby, Riby Abraham and Krishnaswamy, Hariharan and Klimchik, Alexandr},
    booktitle = {Advances In Robotics - 6th International Conference of The Robotics Society},
    collection = {AIR 2023},
    doi = {10.1145/3610419.3610471},
    month = {July},
    pages = {1–6},
    publisher = {ACM},
    series = {AIR 2023},
    title = {Evaluation Of Deviations Due To Robot Configuration For Robot-based Incremental Sheet Metal Forming},
    url = {http://dx.doi.org/10.1145/3610419.3610471},
    year = {2023}
}

@article{Polvara2023_Continuously_Changing_Environments,
    author = {Hroob, I. and Molina, S. and Polvara, R. and Cielniak, G. and Hanheide, M.},
    journal = {Proceedings of the 11th European Conference on Mobile Robots, ECMR 2023},
    title = {Learned Long-Term Stability Scan Filtering for Robust Robot Localisation in Continuously Changing Environments},
    year = {2023}
}

@conference{11577_3469177_Lowcost_Embedded_Systems,
    author = {Pasti, Francesco and Bellotto, Nicola},
    booktitle = {Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023)},
    doi = {10.5220/0011797400003417},
    isbn = {978-989-758-634-7},
    pages = {282--293},
    title = {Evaluation of Computer Vision-Based Person Detection on Low-Cost Embedded Systems},
    url = {https://www.scitepress.org/Link.aspx?doi=10.5220/0011797400003417},
    volume = {5},
    year = {2023}
}

@article{cox2023visual_Generalise_Vtagexploiting_Perceptual,
    author = {Cox, Jonathan and Tsagkopoulos, Nikolaos and Rozsyp{\'a}lek, Zden{\v{e}}k and Krajn{\'\i}k, Tom{\'a}{\v{s}} and Sklar, Elizabeth and Hanheide, Marc},
    journal = {Computers and Electronics in Agriculture},
    pages = {108054},
    publisher = {Elsevier},
    title = {Visual teach and generalise (VTAG)—Exploiting perceptual aliasing for scalable autonomous robotic navigation in horticultural environments},
    volume = {212},
    year = {2023}
}

@article{Seemakurthy_2023_Generalised_Faster_Rcnn,
    author = {Seemakurthy, Karthik and Fox, Charles and Aptoula, Erchan and Bosilj, Petra},
    doi = {10.1609/aaai.v37i2.25312},
    issn = {2159-5399},
    journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
    month = {June},
    number = {2},
    pages = {2180–2190},
    publisher = {Association for the Advancement of Artificial Intelligence (AAAI)},
    title = {Domain Generalised Faster R-CNN},
    url = {http://dx.doi.org/10.1609/aaai.v37i2.25312},
    volume = {37},
    year = {2023}
}

@article{Tagarakis_2023_Digital_Agriculture,
    author = {Tagarakis, Aristotelis C. and Bochtis, Dionysis},
    doi = {10.3390/s23167255},
    issn = {1424-8220},
    journal = {Sensors},
    month = {August},
    number = {16},
    pages = {7255},
    publisher = {MDPI AG},
    title = {Sensors and Robotics for Digital Agriculture},
    url = {http://dx.doi.org/10.3390/s23167255},
    volume = {23},
    year = {2023}
}

@article{Cielniak2023_Continuously_Changing_Environments,
    author = {Hroob, I. and Molina, S. and Polvara, R. and Cielniak, G. and Hanheide, M.},
    journal = {Proceedings of the 11th European Conference on Mobile Robots, ECMR 2023},
    title = {Learned Long-Term Stability Scan Filtering for Robust Robot Localisation in Continuously Changing Environments},
    year = {2023}
}

@conference{11577_3475012.4_Human_Motion_Prediction,
    author = {Mghames, Sariah and Castri, Luca and Hanheide, Marc and Bellotto, Nicola},
    booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
    title = {A Neuro-Symbolic Approach for Enhanced Human Motion Prediction},
    year = {2023}
}

@article{Cielniak2023_Spraying_Evaluation_System,
    author = {Rogers, H. and De La Iglesia, B. and Zebin, T. and Cielniak, G. and Magri, B.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {26-37},
    title = {An Automated Precision Spraying Evaluation System},
    volume = {14136 LNAI},
    year = {2023}
}

@article{Kulathunga_2023_Multirotor_Aerial_Vehicles,
    author = {Kulathunga, Geesara and Klimchik, Alexandr},
    doi = {10.3390/rs15215237},
    issn = {2072-4292},
    journal = {Remote Sensing},
    month = {November},
    number = {21},
    pages = {5237},
    publisher = {MDPI AG},
    title = {Survey on Motion Planning for Multirotor Aerial Vehicles in Plan-Based Control Paradigm},
    url = {http://dx.doi.org/10.3390/rs15215237},
    volume = {15},
    year = {2023}
}

@conference{11577_3489820_Potato_Instance_Segmentation,
    author = {Hurst, B. and Bellotto, N. and Bosilj, P.},
    booktitle = {Proceedings of Towards Autonomous Robotic Systems (TAROS)},
    doi = {10.1007/978-3-031-43360-3_22},
    title = {An assessment of self-supervised learning for data efficient potato instance segmentation},
    year = {2023}
}

@conference{11577_3465415_Robot_Sensor_Data,
    author = {Castri, L. and Mghames, S. and Hanheide, M. and Bellotto, N.},
    booktitle = {Proceedings of the Conference on Causal Learning and Reasoning},
    title = {Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios},
    year = {2023}
}

@article{Das2023_Autonomous_Topological_Optimisation,
    author = {Zhu, Z. and Das, G. and Hanheide, M.},
    journal = {Proceedings of the ACM Symposium on Applied Computing},
    pages = {791-799},
    title = {Autonomous Topological Optimisation for Multi-robot Systems in Logistics},
    year = {2023}
}

@article{Cielniak2023_Deep_Learningbased_Crop,
    author = {de Silva, R. and Cielniak, G. and Wang, G. and Gao, J.},
    journal = {Journal of Field Robotics},
    title = {Deep learning-based crop row detection for infield navigation of agri-robots},
    year = {2023}
}

@article{Cavalaris_2023_Fruit_Fly_Control,
    author = {Cavalaris, Chris and Tagarakis, Aristotelis C. and Kateris, Dimitrios and Bochtis, Dionysis},
    doi = {10.3390/agriengineering5040118},
    issn = {2624-7402},
    journal = {AgriEngineering},
    month = {October},
    number = {4},
    pages = {1925–1942},
    publisher = {MDPI AG},
    title = {Cost Analysis of Using UAV Sprayers for Olive Fruit Fly Control},
    url = {http://dx.doi.org/10.3390/agriengineering5040118},
    volume = {5},
    year = {2023}
}

@inbook{Hurst_2023_Potato_Instance_Segmentation,
    author = {Hurst, Bradley and Bellotto, Nicola and Bosilj, Petra},
    booktitle = {Towards Autonomous Robotic Systems},
    doi = {10.1007/978-3-031-43360-3_22},
    isbn = {9783031433603},
    issn = {1611-3349},
    pages = {267–278},
    publisher = {Springer Nature Switzerland},
    title = {An Assessment of Self-supervised Learning for Data Efficient Potato Instance Segmentation},
    url = {http://dx.doi.org/10.1007/978-3-031-43360-3_22},
    year = {2023}
}

@article{Cielniak2023_Ltsnet_Endtoend_Unsupervised,
    author = {Hroob, I. and Molina, S. and Polvara, R. and Cielniak, G. and Hanheide, M.},
    journal = {arXiv},
    title = {LTS-NET: End-to-end Unsupervised Learning of Long-Term 3D Stable objects},
    year = {2023}
}

@article{moraru2023variable_Automated_Weeding_Platform,
    author = {Moraru, Ionut and Zhivkov, Tsvetan and Coutts, Shaun and Li, Dom and Sklar, Elizabeth I},
    journal = {arXiv preprint arXiv:2303.05461},
    title = {A Variable Autonomy approach for an Automated Weeding Platform},
    year = {2023}
}

@article{Cielniak2023_Shortterm_Dynamic_Behaviour,
    author = {Wagner, N. and Cielniak, G.},
    journal = {Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023},
    pages = {624-633},
    title = {Vision-based Monitoring of the Short-term Dynamic Behaviour of Plants for Automated Phenotyping},
    year = {2023}
}

@article{Kucner_2023_Survey_Of_Maps,
    author = {Kucner, Tomasz Piotr and Magnusson, Martin and Mghames, Sariah and Palmieri, Luigi and Verdoja, Francesco and Swaminathan, Chittaranjan Srinivas and Krajník, Tomáš and Schaffernicht, Erik and Bellotto, Nicola and Hanheide, Marc and Lilienthal, Achim J},
    doi = {10.1177/02783649231190428},
    issn = {1741-3176},
    journal = {The International Journal of Robotics Research},
    month = {August},
    number = {11},
    pages = {977–1006},
    publisher = {SAGE Publications},
    title = {Survey of maps of dynamics for mobile robots},
    url = {http://dx.doi.org/10.1177/02783649231190428},
    volume = {42},
    year = {2023}
}

@conference{11577_3482820_Discovery_In_Robotics,
    author = {Castri, Luca and Mghames, Sariah and Bellotto, Nicola},
    booktitle = {Proceedings of The First AAAI Bridge Program on Continual Causality},
    pages = {85--91},
    title = {From Continual Learning to Causal Discovery in Robotics},
    volume = {208},
    year = {2023}
}

@article{Hanheide2023_Multiagent_Spatial_Interactions,
    author = {Mghames, S. and Castri, L. and Hanheide, M. and Bellotto, N.},
    journal = {IEEE International Workshop on Robot and Human Communication, RO-MAN},
    pages = {1170-1175},
    title = {Qualitative Prediction of Multi-Agent Spatial Interactions},
    year = {2023}
}

@article{Cielniak2023_Forrobotic_Fruit_Picking,
    author = {Le Lou{\"e}dec, J. and Cielniak, G.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {148-158},
    title = {Key Point-Based Orientation Estimation of~Strawberries for~Robotic Fruit Picking},
    volume = {14253 LNCS},
    year = {2023}
}

@article{Hanheide2023_Ltsnet_Endtoend_Unsupervised,
    author = {Hroob, I. and Molina, S. and Polvara, R. and Cielniak, G. and Hanheide, M.},
    journal = {arXiv},
    title = {LTS-NET: End-to-end Unsupervised Learning of Long-Term 3D Stable objects},
    year = {2023}
}

@article{Hanheide2023_Robot_Sensor_Data,
    author = {Castri, L. and Mghames, S. and Hanheide, M. and Bellotto, N.},
    journal = {Proceedings of Machine Learning Research},
    pages = {243-258},
    title = {Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios},
    volume = {213},
    year = {2023}
}

@article{howard2023training_Autonomous_Systems,
    author = {Howard, Matthew and Wane, S and Mihaylova, L and Rose, D and Ray, P and Manning, L and Sklar, E},
    journal = {UK RAS Network},
    publisher = {UK-RAS},
    title = {Training the UK agri-food sector to employ robotics and autonomous systems},
    year = {2023}
}

@article{Osipov_2023_Point_Cloud_Registration,
    author = {Osipov, Alexander and Ostanin, Mikhail and Klimchik, Alexandr},
    doi = {10.3390/info14030149},
    issn = {2078-2489},
    journal = {Information},
    month = {February},
    number = {3},
    pages = {149},
    publisher = {MDPI AG},
    title = {Comparison of Point Cloud Registration Algorithms for Mixed-Reality Cross-Device Global Localization},
    url = {http://dx.doi.org/10.3390/info14030149},
    volume = {14},
    year = {2023}
}

@article{Popov_2023_Physical_Humanrobot_Interaction,
    author = {Popov, Dmitry and Pashkevich, Anatol and Klimchik, Alexandr},
    doi = {10.1016/j.engappai.2023.107141},
    issn = {0952-1976},
    journal = {Engineering Applications of Artificial Intelligence},
    month = {November},
    pages = {107141},
    publisher = {Elsevier BV},
    title = {Adaptive technique for physical human–robot interaction handling using proprioceptive sensors},
    url = {http://dx.doi.org/10.1016/j.engappai.2023.107141},
    volume = {126},
    year = {2023}
}

@article{Benos_2023_Systematic_Review,
    author = {Benos, Lefteris and Moysiadis, Vasileios and Kateris, Dimitrios and Tagarakis, Aristotelis C. and Busato, Patrizia and Pearson, Simon and Bochtis, Dionysis},
    doi = {10.3390/s23156776},
    issn = {1424-8220},
    journal = {Sensors},
    month = {July},
    number = {15},
    pages = {6776},
    publisher = {MDPI AG},
    title = {Human–Robot Interaction in Agriculture: A Systematic Review},
    url = {http://dx.doi.org/10.3390/s23156776},
    volume = {23},
    year = {2023}
}

@conference{11577_3498541_Efficient_Causal_Discovery,
    author = {Castri, Luca and Mghames, Sariah and Bellotto, Nicola},
    booktitle = {Proceedings of Italian Conference on Robotics and Intelligent Machines (I-RIM 3D)},
    doi = {10.48550/arXiv.2310.14925},
    title = {Efficient Causal Discovery for Robotics Applications},
    year = {2023}
}

@article{Harman_2023_Cyclic_Action_Graphs,
    author = {Harman, Helen and Simoens, Pieter},
    doi = {10.1007/s10115-023-01976-6},
    issn = {0219-3116},
    journal = {Knowledge and Information Systems},
    month = {October},
    number = {2},
    pages = {1257–1300},
    publisher = {Springer Science and Business Media LLC},
    title = {Cyclic Action Graphs for goal recognition problems with inaccurately initialised fluents},
    url = {http://dx.doi.org/10.1007/s10115-023-01976-6},
    volume = {66},
    year = {2023}
}

@article{Hanheide2023_Human_Motion_Prediction,
    author = {Mghames, S. and Castri, L. and Hanheide, M. and Bellotto, N.},
    journal = {Proceedings of the International Joint Conference on Neural Networks},
    title = {A Neuro-Symbolic Approach for Enhanced Human Motion Prediction},
    volume = {2023-June},
    year = {2023}
}

@conference{11577_3481942_Multiagent_Spatial_Interactions,
    author = {Mghames, Sariah and Castri, Luca and Hanheide, Marc and Bellotto, Nicola},
    booktitle = {Proceedings of the IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},
    title = {Qualitative Prediction of Multi-Agent Spatial Interactions},
    year = {2023}
}

@article{Angelopoulou_2023_Spaceborne_Prisma_Hyperspectral,
    author = {Angelopoulou, Theodora and Chabrillat, Sabine and Pignatti, Stefano and Milewski, Robert and Karyotis, Konstantinos and Brell, Maximilian and Ruhtz, Thomas and Bochtis, Dionysis and Zalidis, George},
    doi = {10.3390/rs15041106},
    issn = {2072-4292},
    journal = {Remote Sensing},
    month = {February},
    number = {4},
    pages = {1106},
    publisher = {MDPI AG},
    title = {Evaluation of Airborne HySpex and Spaceborne PRISMA Hyperspectral Remote Sensing Data for Soil Organic Matter and Carbonates Estimation},
    url = {http://dx.doi.org/10.3390/rs15041106},
    volume = {15},
    year = {2023}
}

@article{Cielniak2023_Deposit_Identification_System,
    author = {Rogers, H. and De La Iglesia, B. and Zebin, T. and Cielniak, G. and Magri, B.},
    journal = {IEEE International Conference on Automation Science and Engineering},
    title = {An Agricultural Precision Sprayer Deposit Identification System},
    volume = {2023-August},
    year = {2023}
}

@article{Hanheide2022,title = {Collection and Evaluation of a Long-Term 4D Agri-Robotic Dataset},journal = {arXiv},year = {2022},author = {Polvara, R. and Mellado, S.M. and Hroob, I. and Cielniak, G. and Hanheide, M.}}

@article{harman2022multi_Multiagent_Task_Allocation,
    author = {Harman, Helen and Sklar, Elizabeth I},
    journal = {Frontiers in Robotics and AI},
    pages = {864745},
    publisher = {Frontiers},
    title = {Multi-agent task allocation for harvest management},
    volume = {9},
    year = {2022}
}

@article{Popov_2022_Interaction_Parameters_Identification,
    author = {Dmitry Popov and Alexandr Klimchik and Anatol Pashkevich},
    doi = {10.1109/lra.2022.3188886},
    journal = {{IEEE} Robotics and Automation Letters},
    month = {oct},
    number = {4},
    pages = {8582--8589},
    publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
    title = {Robustness of Interaction Parameters Identification Technique for Collaborative Robots},
    url = {https://doi.org/10.1109%2Flra.2022.3188886},
    volume = {7},
    year = {2022}
}

@article{Das2022_Adversarial_Multiagent_Systems,
    author = {Wang, N. and Das, G.P. and Millard, A.G.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {179-189},
    title = {Learning Cooperative Behaviours in Adversarial Multi-agent Systems},
    volume = {13546 LNAI},
    year = {2022}
}

@article{pearson2022robotics_Robotics_And_Autonomous,
    author = {Pearson, Simon and Camacho-Villa, Tania Carolina and Valluru, Ravi and Gaju, Oorbessy and Rai, Mini C and Gould, Iain and Brewer, Steve and Sklar, Elizabeth},
    journal = {Current Robotics Reports},
    number = {2},
    pages = {57--64},
    publisher = {Springer International Publishing Cham},
    title = {Robotics and autonomous systems for net zero agriculture},
    volume = {3},
    year = {2022}
}

@article{Ostanin_2022_Full_Hand_Tracking,
    author = {Ostanin, Mikhail and Zaitsev, Stanislav and Sabirova, Adelia and Klimchik, Alexandr},
    doi = {10.1016/j.ifacol.2022.10.153},
    issn = {2405-8963},
    journal = {IFAC-PapersOnLine},
    number = {10},
    pages = {2791–2796},
    publisher = {Elsevier BV},
    title = {Interactive Industrial Robot Programming based on Mixed Reality and Full Hand Tracking},
    url = {http://dx.doi.org/10.1016/J.IFACOL.2022.10.153},
    volume = {55},
    year = {2022}
}

@inproceedings{harman2022multi_Multiagent_Task_Allocation_Multiagent_Task_Allocation,
    author = {Harman, Helen and Sklar, Elizabeth I},
    booktitle = {International Conference on Practical Applications of Agents and Multi-Agent Systems},
    organization = {Springer International Publishing Cham},
    pages = {217--228},
    title = {Multi-agent task allocation techniques for harvest team formation},
    year = {2022}
}

@article{Almaghout_2022_Deformable_Linear_Objects,
    author = {Almaghout, Karam and Klimchik, Alexandr},
    doi = {10.1016/j.ifacol.2022.10.079},
    issn = {2405-8963},
    journal = {IFAC-PapersOnLine},
    number = {10},
    pages = {2469–2474},
    publisher = {Elsevier BV},
    title = {Planar Shape Control of Deformable Linear Objects},
    url = {http://dx.doi.org/10.1016/J.IFACOL.2022.10.079},
    volume = {55},
    year = {2022}
}

@conference{11577_3460074_Human_Spatial_Interactions,
    abstract = {Exploiting robots for activities in human-shared environments, whether warehouses, shopping centres or hospitals, calls for such robots to understand the underlying physical interactions between nearby agents and objects. In particular, modelling cause-and-effect relations between the latter can help to predict unobserved human behaviours and anticipate the outcome of specific robot interventions. In this paper, we propose an application of causal discovery methods to model human-robot spatial interactions, trying to understand human behaviours from real-world sensor data in two possible scenarios: humans interacting with the environment, and humans interacting with obstacles. New methods and practical solutions are discussed to exploit, for the first time, a state-of-the-art causal discovery algorithm in some challenging human environments, with potential application in many service robotics scenarios. To demonstrate the utility of the causal models obtained from real-world datasets, we present a comparison between causal and non-causal prediction approaches. Our results show that the causal model correctly captures the underlying interactions of the considered scenarios and improves its prediction accuracy.},
    author = {Castri, Luca and Mghames, Sariah and Hanheide, Marc and Bellotto, Nicola},
    booktitle = {Lecture Notes in Computer Science - International Conference on Social Robotics (ICSR)},
    doi = {10.1007/978-3-031-24667-8_14},
    isbn = {978-3-031-24666-1},
    pages = {154--164},
    title = {Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions},
    volume = {13817},
    year = {2022}
}

Exploiting robots for activities in human-shared environments, whether warehouses, shopping centres or hospitals, calls for such robots to understand the underlying physical interactions between nearby agents and objects. In particular, modelling cause-and-effect relations between the latter can help to predict unobserved human behaviours and anticipate the outcome of specific robot interventions. In this paper, we propose an application of causal discovery methods to model human-robot spatial interactions, trying to understand human behaviours from real-world sensor data in two possible scenarios: humans interacting with the environment, and humans interacting with obstacles. New methods and practical solutions are discussed to exploit, for the first time, a state-of-the-art causal discovery algorithm in some challenging human environments, with potential application in many service robotics scenarios. To demonstrate the utility of the causal models obtained from real-world datasets, we present a comparison between causal and non-causal prediction approaches. Our results show that the causal model correctly captures the underlying interactions of the considered scenarios and improves its prediction accuracy.

@article{Cielniak2022_Openfield_Agricultural_Robotic,
    author = {Mayoral, J.C. and Grimstad, L. and From, P.J. and Cielniak, G.},
    journal = {International Conference on Human System Interaction, HSI},
    title = {Towards Safety in Open-field Agricultural Robotic Applications: A Method for Human Risk Assessment using Classifiers},
    volume = {2022-July},
    year = {2022}
}

@article{owen2022towards_Autonomous_Task_Allocation,
    author = {Owen, Amie and Harman, Helen and Sklar, Elizabeth and others},
    publisher = {UK-RAS},
    title = {Towards Autonomous Task Allocation Using a Robot Team in a Food Factory},
    year = {2022}
}

@article{Cielniak2022_Collection_And_Evaluation,
    author = {Polvara, R. and Mellado, S.M. and Hroob, I. and Cielniak, G. and Hanheide, M.},
    journal = {arXiv},
    title = {Collection and Evaluation of a Long-Term 4D Agri-Robotic Dataset},
    year = {2022}
}

@inproceedings{harman2022challenges_Agricultural_Workforce_Management,
    author = {Harman, Helen and Sklar, Elizabeth I},
    booktitle = {International Workshop on Multi-Agent Systems and Agent-Based Simulation},
    organization = {Springer International Publishing Cham},
    pages = {121--133},
    title = {Challenges for Multi-Agent Based Agricultural Workforce Management},
    year = {2022}
}

@inproceedings{Lampridi_2022_Systems_In_Agriculture,
    author = {Lampridi, Maria and Benos, Lefteris and Aidonis, Dimitrios and Kateris, Dimitrios and Tagarakis, Aristotelis C. and Platis, Ilias and Achillas, Charisios and Bochtis, Dionysis},
    booktitle = {The 13th EFITA International Conference},
    collection = {EFITA 2021},
    doi = {10.3390/engproc2021009046},
    month = {March},
    pages = {46},
    publisher = {MDPI},
    series = {EFITA 2021},
    title = {The Cutting Edge on Advances in ICT Systems in Agriculture},
    url = {http://dx.doi.org/10.3390/engproc2021009046},
    year = {2022}
}

@article{Das2022_Soil_Compaction_Mapping,
    author = {Roberts-Elliott, L. and Das, G.P. and Millard, A.G.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {251-265},
    title = {Agent-Based Simulation of~Multi-robot Soil Compaction Mapping},
    volume = {13546 LNAI},
    year = {2022}
}

@article{Cielniak2022_Strawberry_Trait_Automation,
    author = {James, K.M.F. and Sargent, D.J. and Whitehouse, A. and Cielniak, G.},
    journal = {Plants People Planet},
    number = {5},
    pages = {432-443},
    title = {High-throughput phenotyping for breeding targets?Current status and future directions of strawberry trait automation},
    volume = {4},
    year = {2022}
}

@article{Hanheide2022_Agricultural_Event_Prediction,
    author = {Pal, A. and Das, G. and Hanheide, M. and Leite, A.C. and From, P.J.},
    journal = {Agronomy},
    number = {6},
    title = {An Agricultural Event Prediction Framework towards Anticipatory Scheduling of Robot Fleets: General Concepts and Case Studies},
    volume = {12},
    year = {2022}
}

@article{Savin_2022_Flying_Tensegrity_Robots,
    author = {Savin, Sergei and Klimchik, Alexandr},
    doi = {10.3389/frobt.2022.812849},
    issn = {2296-9144},
    journal = {Frontiers in Robotics and AI},
    month = {March},
    publisher = {Frontiers Media SA},
    title = {Morphing-Enabled Path Planning for Flying Tensegrity Robots as a Semidefinite Program},
    url = {http://dx.doi.org/10.3389/FROBT.2022.812849},
    volume = {9},
    year = {2022}
}

@article{Gong_2022_Crop_Yield_Prediction,
    author = {Gong, Liyun and Yu, Miao and Cutsuridis, Vassilis and Kollias, Stefanos and Pearson, Simon},
    doi = {10.3390/horticulturae9010005},
    issn = {2311-7524},
    journal = {Horticulturae},
    month = {December},
    number = {1},
    pages = {5},
    publisher = {MDPI AG},
    title = {A Novel Model Fusion Approach for Greenhouse Crop Yield Prediction},
    url = {http://dx.doi.org/10.3390/horticulturae9010005},
    volume = {9},
    year = {2022}
}

@article{Das2022_Applications_Of_Robotic,
    author = {Ghalazman E., A. and Das, G.P. and Gould, I. and Zarafshan, P. and Rajendran S., V. and Heselden, J. and Badiee, A. and Wright, I. and Pearson, S.},
    journal = {Solar Energy Advancements in Agriculture and Food Production Systems},
    pages = {351-390},
    title = {Applications of robotic and solar energy in precision agriculture and smart farming},
    year = {2022}
}

@article{Tagarakis_2022_Orchard_Environments,
    author = {Tagarakis, Aristotelis C. and Filippou, Evangelia and Kalaitzidis, Damianos and Benos, Lefteris and Busato, Patrizia and Bochtis, Dionysis},
    doi = {10.3390/s22041571},
    issn = {1424-8220},
    journal = {Sensors},
    month = {February},
    number = {4},
    pages = {1571},
    publisher = {MDPI AG},
    title = {Proposing UGV and UAV Systems for 3D Mapping of Orchard Environments},
    url = {http://dx.doi.org/10.3390/s22041571},
    volume = {22},
    year = {2022}
}

@article{Cielniak2022_Statistical_Shape_Representations,
    author = {Heiwolt, K. and {\"O}ztireli, C. and Cielniak, G.},
    journal = {arXiv},
    title = {Statistical shape representations for temporal registration of plant components in 3D},
    year = {2022}
}

@article{Hanheide2022_Strawberry_Yield_Forecasting,
    author = {Onoufriou, G. and Hanheide, M. and Leontidis, G.},
    journal = {Sensors},
    number = {21},
    title = {EDLaaS:Fully Homomorphic Encryption over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting},
    volume = {22},
    year = {2022}
}

@inbook{Akhmetzyanov_2022_Free_Error_Compensation,
    author = {Akhmetzyanov, Aydar and Rassabin, Maksim and Maloletov, Alexander and Fadeev, Mikhail and Klimchik, Alexandr},
    booktitle = {Informatics in Control, Automation and Robotics},
    doi = {10.1007/978-3-030-92442-3_24},
    isbn = {9783030924423},
    issn = {1876-1119},
    pages = {479–496},
    publisher = {Springer International Publishing},
    title = {Model Free Error Compensation for Cable-Driven Robot Based on Deep Learning with Sim2real Transfer Learning},
    url = {http://dx.doi.org/10.1007/978-3-030-92442-3_24},
    year = {2022}
}

@article{Kulathunga_2022_Trajectory_Tracking_Approach,
    author = {Geesara Kulathunga and Hany Hamed and Dmitry Devitt and Alexandr Klimchik},
    doi = {10.1109/lra.2022.3151157},
    journal = {{IEEE} Robotics and Automation Letters},
    month = {apr},
    number = {2},
    pages = {4598--4605},
    publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
    title = {Optimization-Based Trajectory Tracking Approach for Multi-Rotor Aerial Vehicles in Unknown Environments},
    url = {https://doi.org/10.1109%2Flra.2022.3151157},
    volume = {7},
    year = {2022}
}

@article{Katikaridis_2022_Uavsupported_Route_Planning,
    author = {Katikaridis, Dimitrios and Moysiadis, Vasileios and Tsolakis, Naoum and Busato, Patrizia and Kateris, Dimitrios and Pearson, Simon and Sørensen, Claus Grøn and Bochtis, Dionysis},
    doi = {10.3390/agronomy12081937},
    issn = {2073-4395},
    journal = {Agronomy},
    month = {August},
    number = {8},
    pages = {1937},
    publisher = {MDPI AG},
    title = {UAV-Supported Route Planning for UGVs in Semi-Deterministic Agricultural Environments},
    url = {http://dx.doi.org/10.3390/agronomy12081937},
    volume = {12},
    year = {2022}
}

@article{Moysiadis_2022_Hand_Gesture_Recognition,
    author = {Moysiadis, Vasileios and Katikaridis, Dimitrios and Benos, Lefteris and Busato, Patrizia and Anagnostis, Athanasios and Kateris, Dimitrios and Pearson, Simon and Bochtis, Dionysis},
    doi = {10.3390/app12168160},
    issn = {2076-3417},
    journal = {Applied Sciences},
    month = {August},
    number = {16},
    pages = {8160},
    publisher = {MDPI AG},
    title = {An Integrated Real-Time Hand Gesture Recognition Framework for Human–Robot Interaction in Agriculture},
    url = {http://dx.doi.org/10.3390/app12168160},
    volume = {12},
    year = {2022}
}

@article{Hanheide2022_Tabletop_Yield_Forecasting,
    author = {Onoufriou, G. and Hanheide, M. and Leontidis, G.},
    journal = {arXiv},
    title = {Premonition Net, A Multi-Timeline Transformer Network Architecture Towards Strawberry Tabletop Yield Forecasting},
    year = {2022}
}

@article{Polvara2022_Range_Extension,
    author = {Szyrowski, T. and Khan, A. and Pemberton, R. and Sharma, S.K. and Singh, Y. and Polvara, R.},
    journal = {Journal of Marine Engineering and Technology},
    number = {2},
    pages = {65-72},
    title = {Range extension for electromagnetic detection of subsea power and telecommunication cables},
    volume = {21},
    year = {2022}
}

@inproceedings{salazar2022beyond_Practical_Object_Detection,
    author = {Salazar-Gomez, Adrian and Darbyshire, Madeleine and Gao, Junfeng and Sklar, Elizabeth I and Parsons, Simon},
    booktitle = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    organization = {IEEE},
    pages = {9232--9238},
    title = {Beyond mAP: Towards practical object detection for weed spraying in precision agriculture},
    year = {2022}
}

@article{harman2022multi_Multiagent_Task_Allocation_Multiagent_Task_Allocation_Multiagent_Task_Allocation,
    author = {Harman, Helen and Sklar, Elizabeth and others},
    publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
    title = {Multi-agent task allocation for fruit picker team formation},
    year = {2022}
}

@article{Al_Mdfaa_2022_Moving_Object,
    author = {Al Mdfaa, Mohamad and Kulathunga, Geesara and Klimchik, Alexandr},
    doi = {10.3390/rs14225756},
    issn = {2072-4292},
    journal = {Remote Sensing},
    month = {November},
    number = {22},
    pages = {5756},
    publisher = {MDPI AG},
    title = {3D-SiamMask: Vision-Based Multi-Rotor Aerial-Vehicle Tracking for a Moving Object},
    url = {http://dx.doi.org/10.3390/rs14225756},
    volume = {14},
    year = {2022}
}

@article{Cielniak2022_Reliable_Robotic_Monitoring,
    author = {Choi, T. and Would, O. and Salazar-Gomez, A. and Cielniak, G.},
    journal = {Proceedings - IEEE International Conference on Robotics and Automation},
    pages = {2266-2272},
    title = {Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies},
    year = {2022}
}

@article{Hanheide2022_Performance_In_Teleoperation,
    author = {Parsa, S. and Maior, H.A. and Thumwood, A.R.E. and Wilson, M.L. and Hanheide, M. and Esfahani, A.G.},
    journal = {Conference on Human Factors in Computing Systems - Proceedings},
    title = {The Impact of Motion Scaling and Haptic Guidance on Operators{'} Workload and Performance in Teleoperation},
    year = {2022}
}

@inproceedings{owen2022towards_Food_Production_Industry,
    author = {Owen, Amie and Harman, Helen and Sklar, Elizabeth I},
    booktitle = {International Conference on Practical Applications of Agents and Multi-Agent Systems},
    organization = {Springer International Publishing Cham},
    pages = {5--16},
    title = {Towards the Application of Multi-Agent Task Allocation to Hygiene Tasks in the Food Production Industry},
    year = {2022}
}

@article{Hanheide2022_Interactive_Movement_Primitives,
    author = {Mghames, S. and Hanheide, M.},
    journal = {IEEE International Conference on Automation Science and Engineering},
    pages = {493-498},
    title = {Environment-aware Interactive Movement Primitives for Object Reaching in Clutter},
    volume = {2022-August},
    year = {2022}
}

@article{Del_Duchetto_2022_Job_Longterm_Behavioural,
    author = {Del Duchetto, Francesco and Hanheide, Marc},
    doi = {10.1109/lra.2022.3178807},
    issn = {2377-3774},
    journal = {IEEE Robotics and Automation Letters},
    month = {July},
    number = {3},
    pages = {6934–6941},
    publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
    title = {Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot Interactions},
    url = {http://dx.doi.org/10.1109/LRA.2022.3178807},
    volume = {7},
    year = {2022}
}

@article{Cielniak2022_Infield_Navigation_Leveraging,
    author = {De Silva, R. and Cielniak, G. and Gao, J.},
    journal = {IEEE International Conference on Automation Science and Engineering},
    pages = {1212-1217},
    title = {Towards Infield Navigation: leveraging simulated data for crop row detection},
    volume = {2022-August},
    year = {2022}
}

@article{Kozlov_2022_Robot_Calibration_Approaches,
    author = {Kozlov, Pavel and Klimchik, Alexandr},
    doi = {10.1016/j.ifacol.2022.10.152},
    issn = {2405-8963},
    journal = {IFAC-PapersOnLine},
    number = {10},
    pages = {2785–2790},
    publisher = {Elsevier BV},
    title = {Experimental study on robot calibration approaches.},
    url = {http://dx.doi.org/10.1016/J.IFACOL.2022.10.152},
    volume = {55},
    year = {2022}
}

@article{zhivkov20225g_Farm_Evaluating_Wireless,
    author = {Zhivkov, Tsvetan and Sklar, Elizabeth I},
    journal = {arXiv preprint arXiv:2301.01600},
    title = {5G on the Farm: Evaluating Wireless Network Capabilities for Agricultural Robotics},
    year = {2022}
}

@article{Hanheide2022_Job_Longterm_Behavioural,
    author = {Duchetto, F.D. and Hanheide, M.},
    journal = {IEEE Robotics and Automation Letters},
    number = {3},
    pages = {6934-6941},
    title = {Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot Interactions},
    volume = {7},
    year = {2022}
}

@inproceedings{chapman2022using_Patientfacing_Research_Software,
    author = {Chapman, Martin and Abigail, G and Sassoon, Isabel and K{\"o}kciyan, Nadin and Sklar, Elizabeth I and Curcin, Vasa and others},
    booktitle = {2022 IEEE 18th International Conference on e-Science (e-Science)},
    organization = {IEEE},
    pages = {44--54},
    title = {Using microservices to design patient-facing research software},
    year = {2022}
}

@article{drake2022relationship_Connected_Health_Technologies,
    author = {Drake, Archie and Sassoon, Isabel and Balatsoukas, Panos and Porat, Talya and Ashworth, Mark and Wright, Ellen and Curcin, Vasa and Chapman, Martin and Kokciyan, Nadin and Modgil, Sanjay and others},
    journal = {Health Informatics Journal},
    number = {2},
    pages = {14604582221102373},
    publisher = {SAGE Publications Sage UK: London, England},
    title = {The relationship of socio-demographic factors and patient attitudes to connected health technologies: a survey of stroke survivors},
    volume = {28},
    year = {2022}
}

@article{Hanheide2022_Human_Spatial_Interactions,
    author = {Castri, L. and Mghames, S. and Hanheide, M. and Bellotto, N.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {154-164},
    title = {Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions},
    volume = {13817 LNAI},
    year = {2022}
}

@article{Das2022_Agricultural_Event_Prediction,
    author = {Pal, A. and Das, G. and Hanheide, M. and Leite, A.C. and From, P.J.},
    journal = {Agronomy},
    number = {6},
    title = {An Agricultural Event Prediction Framework towards Anticipatory Scheduling of Robot Fleets: General Concepts and Case Studies},
    volume = {12},
    year = {2022}
}

@article{Ostanin_2022_Humanrobot_Interaction_System,
    author = {Ostanin, M. A. and Klimchik, A. S.},
    doi = {10.35211/1990-5297-2022-9-268-62-66},
    issn = {1990-5297},
    journal = {Izvestia Volgograd State Technical University},
    number = {9},
    pages = {62–66},
    publisher = {Volgograd State Technical University},
    title = {HUMAN-ROBOT INTERACTION SYSTEM ARCHITECTURE BASED ON MIXED REALITY},
    url = {http://dx.doi.org/10.35211/1990-5297-2022-9-268-62-66},
    year = {2022}
}

@article{Zhao_2022_Nonlinear_Stiffness_Behavior,
    author = {Zhao, Wanda and Klimchik, Alexandr and Pashkevich, Anatol and Chablat, Damien},
    doi = {10.1016/j.mechmachtheory.2022.104783},
    issn = {0094-114X},
    journal = {Mechanism and Machine Theory},
    month = {June},
    pages = {104783},
    publisher = {Elsevier BV},
    title = {Non-linear stiffness behavior of planar serial robotic manipulators},
    url = {http://dx.doi.org/10.1016/j.mechmachtheory.2022.104783},
    volume = {172},
    year = {2022}
}

@article{darbyshire2022localising_Prototype_Weed_Sprayer,
    author = {Darbyshire, Madeleine and Salazar-Gomez, Adrian and Lennox, Callum and Gao, Junfeng and Sklar, Elizabeth and Parsons, Simon and others},
    publisher = {UK-RAS Network},
    title = {Localising weeds using a prototype weed sprayer},
    year = {2022}
}

@article{Cielniak2022_Control_Lyapunov_Function,
    author = {Atas, F. and Cielniak, G. and Grimstad, L.},
    journal = {arXiv},
    title = {Cloud Hopping; Navigating in 3D Uneven Environments via Supervoxels and Control Lyapunov Function},
    year = {2022}
}

@conference{11577_3471199_Industrial_Environments,
    author = {Ghidoni, Stefano and Terreran, Matteo and Evangelista, Daniele and Menegatti, Emanuele and Eitzinger, Christian and Villagrossi, Enrico and Pedrocchi, Nicola and Castaman, Nicola and Malecha, Marcin and Mghames, Sariah and Castri, Luca and Hanheide, Marc and Bellotto, Nicola},
    booktitle = {Workshop AI per l'industria},
    title = {From Human Perception and Action Recognition to Causal Understanding of Human-Robot Interaction in Industrial Environments},
    year = {2022}
}

@article{Yahyaoui_2022_Trustbased_Management,
    author = {Hamdi Yahyaoui and Zakaria Maamar and Mohamed Alkhafajiy and Hamid Al-Hamadi},
    doi = {10.1016/j.future.2022.06.003},
    journal = {Future Generation Computer Systems},
    month = {nov},
    pages = {182--192},
    publisher = {Elsevier {BV}},
    title = {Trust-based management in {IoT} federations},
    url = {https://doi.org/10.1016%2Fj.future.2022.06.003},
    volume = {136},
    year = {2022}
}

@article{Polvara2022_Collection_And_Evaluation,
    author = {Polvara, R. and Mellado, S.M. and Hroob, I. and Cielniak, G. and Hanheide, M.},
    journal = {arXiv},
    title = {Collection and Evaluation of a Long-Term 4D Agri-Robotic Dataset},
    year = {2022}
}

@article{Savin_2022_Tensegrity_Drone,
    author = {Savin, Sergei and Al Badr, Amer and Devitt, Dmitry and Fedorenko, Roman and Klimchik, Alexandr},
    doi = {10.3390/app12115588},
    issn = {2076-3417},
    journal = {Applied Sciences},
    month = {May},
    number = {11},
    pages = {5588},
    publisher = {MDPI AG},
    title = {Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone},
    url = {http://dx.doi.org/10.3390/app12115588},
    volume = {12},
    year = {2022}
}

@inbook{Klimchik_2022_Gravity_Compensators,
    author = {Klimchik, Alexandr and Pashkevich, Anatol},
    booktitle = {Gravity Compensation in Robotics},
    doi = {10.1007/978-3-030-95750-6_2},
    isbn = {9783030957506},
    issn = {2211-0992},
    pages = {27–71},
    publisher = {Springer International Publishing},
    title = {Stiffness Modeling for Gravity Compensators},
    url = {http://dx.doi.org/10.1007/978-3-030-95750-6_2},
    year = {2022}
}

@article{Anagnostis_2022_Energy_Storage_Modelling,
    author = {Anagnostis, Athanasios and Moustakidis, Serafeim and Papageorgiou, Elpiniki and Bochtis, Dionysis},
    doi = {10.3390/en15061959},
    issn = {1996-1073},
    journal = {Energies},
    month = {March},
    number = {6},
    pages = {1959},
    publisher = {MDPI AG},
    title = {A Hybrid Bimodal LSTM Architecture for Cascading Thermal Energy Storage Modelling},
    url = {http://dx.doi.org/10.3390/en15061959},
    volume = {15},
    year = {2022}
}

@article{Cielniak2022_Mobile_Robots,
    author = {Atas, F. and Cielniak, G. and Grimstad, L.},
    journal = {arXiv},
    title = {Elevation State-Space: Surfel-Based Navigation in Uneven Environments for Mobile Robots},
    year = {2022}
}

@article{castagna2022providing_Eqr_Argument_Scheme,
    author = {Castagna, Federico and Parsons, Simon and Sassoon, Isabel and Sklar, Elizabeth and others},
    publisher = {IOS Press},
    title = {Providing explanations via the EQR argument scheme},
    year = {2022}
}

@article{Popov_2022_Collaborative_Robots,
    author = {Popov, Dmitry and Klimchik, Alexandr and Pashkevich, Anatol},
    doi = {10.1016/j.ifacol.2022.10.076},
    issn = {2405-8963},
    journal = {IFAC-PapersOnLine},
    number = {10},
    pages = {2451–2456},
    publisher = {Elsevier BV},
    title = {Analytical Estimation of Interaction Force and its Application Point for Collaborative Robots},
    url = {http://dx.doi.org/10.1016/J.IFACOL.2022.10.076},
    volume = {55},
    year = {2022}
}

@article{Cielniak2022_Human_Motion_Patterns,
    author = {Molina, S. and Cielniak, G. and Duckett, T.},
    journal = {IEEE Transactions on Robotics},
    number = {2},
    pages = {1304-1318},
    title = {Robotic Exploration for Learning Human Motion Patterns},
    volume = {38},
    year = {2022}
}

@inproceedings{Kozlov_2022_Robot_Calibration_Approaches_Robot_Calibration_Approaches,
    author = {Kozlov, Pavel and Klimchik, Alexandr},
    booktitle = {Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics},
    doi = {10.5220/0011321800003271},
    pages = {516–523},
    publisher = {SCITEPRESS - Science and Technology Publications},
    title = {Simulation Study on Robot Calibration Approaches},
    url = {http://dx.doi.org/10.5220/0011321800003271},
    year = {2022}
}

@article{Kleftodimos_2021_Dairy_Cattle_Farms,
    author = {Kleftodimos, Georgios and Kyrgiakos, Leonidas Sotirios and Kleisiari, Christina and Tagarakis, Aristotelis C. and Bochtis, Dionysis},
    doi = {10.3390/su14010411},
    issn = {2071-1050},
    journal = {Sustainability},
    month = {December},
    number = {1},
    pages = {411},
    publisher = {MDPI AG},
    title = {Examining Farmers’ Adoption Decisions towards Precision-Agricultural Practices in Greek Dairy Cattle Farms},
    url = {http://dx.doi.org/10.3390/su14010411},
    volume = {14},
    year = {2021}
}

@article{Hanheide2021_Navigateandseek_A_Robotics,
    author = {Polvara, R. and Del Duchetto, F. and Neumann, G. and Hanheide, M.},
    journal = {arXiv},
    title = {Navigate-and-seek: A robotics framework for people localization in agricultural environments},
    year = {2021}
}

@article{Benos_2021_Collaborative_Agricultural_Tasks,
    author = {Benos, Lefteris and Kokkotis, Christos and Tsatalas, Themistoklis and Karampina, Evangeli and Tsaopoulos, Dimitrios and Bochtis, Dionysis},
    doi = {10.3390/app112411742},
    issn = {2076-3417},
    journal = {Applied Sciences},
    month = {December},
    number = {24},
    pages = {11742},
    publisher = {MDPI AG},
    title = {Biomechanical Effects on Lower Extremities in Human-Robot Collaborative Agricultural Tasks},
    url = {http://dx.doi.org/10.3390/app112411742},
    volume = {11},
    year = {2021}
}

@article{Camara_2021_Autonomous_Driving_Part,
    author = {Fanta Camara and Nicola Bellotto and Serhan Cosar and Florian Weber and Dimitris Nathanael and Matthias Althoff and Jingyuan Wu and Johannes Ruenz and Andre Dietrich and Gustav Markkula and Anna Schieben and Fabio Tango and Natasha Merat and Charles Fox},
    doi = {10.1109/tits.2020.3006767},
    journal = {{IEEE} Transactions on Intelligent Transportation Systems},
    month = {sep},
    number = {9},
    pages = {5453--5472},
    publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
    title = {Pedestrian Models for Autonomous Driving Part {II}: High-Level Models of Human Behavior},
    url = {https://doi.org/10.1109%2Ftits.2020.3006767},
    volume = {22},
    year = {2021}
}

@article{Hanheide2021_Preface,
    author = {Fox, C. and Gao, J. and Esfahani, A.G. and Saaj, M. and Hanheide, M. and Parsons, S.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {v},
    title = {Preface},
    volume = {13054 LNAI},
    year = {2021}
}

@article{Gong_2021_Learning_Based_Prediction,
    author = {Gong, Liyun and Yu, Miao and Jiang, Shouyong and Cutsuridis, Vassilis and Pearson, Simon},
    doi = {10.3390/s21134537},
    issn = {1424-8220},
    journal = {Sensors},
    month = {July},
    number = {13},
    pages = {4537},
    publisher = {MDPI AG},
    title = {Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN},
    url = {http://dx.doi.org/10.3390/s21134537},
    volume = {21},
    year = {2021}
}

@article{Le_Lou_dec_2021_Deep_Learningbased_Segmentation,
    author = {Justin Le Louëdec and Grzegorz Cielniak},
    doi = {10.1016/j.compag.2021.106374},
    journal = {Computers and Electronics in Agriculture},
    month = {nov},
    pages = {106374},
    publisher = {Elsevier {BV}},
    title = {3D shape sensing and deep learning-based segmentation of strawberries},
    url = {https://doi.org/10.1016%2Fj.compag.2021.106374},
    volume = {190},
    year = {2021}
}

@article{Tagarakis_2021_Fresh_Produce_Supply,
    author = {Tagarakis, Aristotelis C. and Benos, Lefteris and Kateris, Dimitrios and Tsotsolas, Nikolaos and Bochtis, Dionysis},
    doi = {10.3390/app11167596},
    issn = {2076-3417},
    journal = {Applied Sciences},
    month = {August},
    number = {16},
    pages = {7596},
    publisher = {MDPI AG},
    title = {Bridging the Gaps in Traceability Systems for Fresh Produce Supply Chains: Overview and Development of an Integrated IoT-Based System},
    url = {http://dx.doi.org/10.3390/app11167596},
    volume = {11},
    year = {2021}
}

@article{Al-khafajiy2021_Cognitive_Fog_Model,
    author = {Al-Khafajiy, M. and Otoum, S. and Baker, T. and Asim, M. and Maamar, Z. and Aloqaily, M. and Taylor, M. and Randles, M.},
    journal = {ACM Transactions on Internet Technology},
    number = {3},
    title = {Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model},
    volume = {21},
    year = {2021}
}

@article{Cielniak2021_Feature_Pyramid_Levels,
    author = {Zahidi, U.A. and Cielniak, G.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {245-257},
    title = {Active Learning for Crop-Weed Discrimination by Image Classification from Convolutional Neural Network?s Feature Pyramid Levels},
    volume = {12899 LNCS},
    year = {2021}
}

@article{gomez2021towards_Practical_Object_Detection,
    author = {Gomez, Adrian Salazar and Darbyshire, Madeleine and Gao, Junfeng and Sklar, Elizabeth I and Parsons, Simon},
    journal = {CoRR},
    title = {Towards practical object detection for weed spraying in precision agriculture},
    year = {2021}
}

@article{Cielniak2021_Fruit_Picking_Applications,
    author = {Wagner, N. and Kirk, R. and Hanheide, M. and Cielniak, G.},
    journal = {Proceedings - IEEE International Conference on Robotics and Automation},
    pages = {6818-6823},
    title = {Efficient and Robust Orientation Estimation of Strawberries for Fruit Picking Applications},
    volume = {2021-May},
    year = {2021}
}

@article{Polvara2021_Agricultural_Environments,
    author = {Polvara, R. and Del Duchetto, F. and Neumann, G. and Hanheide, M.},
    journal = {IEEE Robotics and Automation Letters},
    number = {4},
    pages = {6577-6584},
    title = {Navigate-and-Seek: A Robotics Framework for People Localization in Agricultural Environments},
    volume = {6},
    year = {2021}
}

@article{Hanheide2021_Deep_Learning_Method,
    author = {De Barrie, D. and Pandya, M. and Pandya, H. and Hanheide, M. and Elgeneidy, K.},
    journal = {Frontiers in Robotics and AI},
    title = {A Deep Learning Method for Vision Based Force Prediction of a Soft Fin Ray Gripper Using Simulation Data},
    volume = {8},
    year = {2021}
}

@article{jansen2021exploring_Exploring_Cocreative_Drawing,
    author = {Jansen, Chipp and Sklar, Elizabeth},
    journal = {Frontiers in Robotics and AI},
    pages = {577770},
    publisher = {Frontiers Media SA},
    title = {Exploring co-creative drawing workflows},
    volume = {8},
    year = {2021}
}

@article{Anagnostis_2021_Learning_Semantic_Segmentation,
    author = {Anagnostis, Athanasios and Tagarakis, Aristotelis C. and Kateris, Dimitrios and Moysiadis, Vasileios and Sørensen, Claus Grøn and Pearson, Simon and Bochtis, Dionysis},
    doi = {10.3390/s21113813},
    issn = {1424-8220},
    journal = {Sensors},
    month = {May},
    number = {11},
    pages = {3813},
    publisher = {MDPI AG},
    title = {Orchard Mapping with Deep Learning Semantic Segmentation},
    url = {http://dx.doi.org/10.3390/s21113813},
    volume = {21},
    year = {2021}
}

@article{Cielniak2021_Outdoor_Robot_Navigation,
    author = {Atas, F. and Grimstad, L. and Cielniak, G.},
    journal = {arXiv},
    title = {Evaluation of sampling-based optimizing planners for outdoor robot navigation},
    year = {2021}
}

@inproceedings{Tagarakis_2021_Collaborative_Agricultural_Environments,
    author = {Tagarakis, Aristotelis C. and Benos, Lefteris and Aivazidou, Eirini and Anagnostis, Athanasios and Kateris, Dimitrios and Bochtis, Dionysis},
    booktitle = {The 13th EFITA International Conference},
    collection = {EFITA 2021},
    doi = {10.3390/engproc2021009005},
    month = {November},
    pages = {5},
    publisher = {MDPI},
    series = {EFITA 2021},
    title = {Wearable Sensors for Identifying Activity Signatures in Human-Robot Collaborative Agricultural Environments},
    url = {http://dx.doi.org/10.3390/engproc2021009005},
    year = {2021}
}

@article{Salazar_Gomez_2021_Regression_Versus_Detection,
    author = {Adrian Salazar-Gomez and Simon Parsons and Erchan Aptoula and Petra Bosilj},
    doi = {10.1109/lra.2021.3062586},
    journal = {{IEEE} Robotics and Automation Letters},
    pages = {1--1},
    publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
    title = {Deep Regression versus Detection for Counting in Robotic Phenotyping},
    url = {https://doi.org/10.1109%2Flra.2021.3062586},
    year = {2021}
}

@article{Camara_2021_Autonomous_Driving_Part_Autonomous_Driving_Part,
    author = {Fanta Camara and Nicola Bellotto and Serhan Cosar and Dimitris Nathanael and Matthias Althoff and Jingyuan Wu and Johannes Ruenz and Andre Dietrich and Charles W. Fox},
    doi = {10.1109/tits.2020.3006768},
    journal = {{IEEE} Transactions on Intelligent Transportation Systems},
    month = {oct},
    number = {10},
    pages = {6131--6151},
    publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
    title = {Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking},
    url = {https://doi.org/10.1109%2Ftits.2020.3006768},
    volume = {22},
    year = {2021}
}

@inproceedings{Damindarov_2021_Depth_Camerabased_System,
    author = {Damindarov, R. and Fam, C. A. and Boby, R. A. and Fahim, M. and Klimchik, A. and Matsumaru, T.},
    booktitle = {2021 International Conference “Nonlinearity, Information and Robotics” (NIR)},
    doi = {10.1109/nir52917.2021.9666090},
    month = {August},
    pages = {1–7},
    publisher = {IEEE},
    title = {A depth camera-based system to enable touch-less interaction using hand gestures},
    url = {http://dx.doi.org/10.1109/NIR52917.2021.9666090},
    year = {2021}
}

@inproceedings{Kateris_2021_Rgbd_Perception,
    author = {Kateris, Dimitrios and Kalaitzidis, Damianos and Moysiadis, Vasileios and Tagarakis, Aristotelis C. and Bochtis, Dionysis},
    booktitle = {The 13th EFITA International Conference},
    collection = {EFITA 2021},
    doi = {10.3390/engproc2021009030},
    month = {December},
    pages = {30},
    publisher = {MDPI},
    series = {EFITA 2021},
    title = {Weed Mapping in Vineyards Using RGB-D Perception},
    url = {http://dx.doi.org/10.3390/engproc2021009030},
    year = {2021}
}

@article{Tagarakis_2021_Wireless_Sensing_System,
    author = {Tagarakis, Aristotelis C. and Kateris, Dimitrios and Berruto, Remigio and Bochtis, Dionysis},
    doi = {10.3390/app11135858},
    issn = {2076-3417},
    journal = {Applied Sciences},
    month = {June},
    number = {13},
    pages = {5858},
    publisher = {MDPI AG},
    title = {Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards},
    url = {http://dx.doi.org/10.3390/app11135858},
    volume = {11},
    year = {2021}
}

@article{Cielniak2021_Soft_Fruit_Mass,
    author = {Kirk, R. and Mangan, M. and Cielniak, G.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {223-233},
    title = {Non-destructive Soft Fruit Mass and~Volume Estimation for Phenotyping in~Horticulture},
    volume = {12899 LNCS},
    year = {2021}
}

@article{Polvara2021_Lidar_Slam_Systems,
    author = {Hroob, I. and Polvara, R. and Molina, S. and Cielniak, G. and Hanheide, M.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {168-177},
    title = {Benchmark of Visual and 3D Lidar SLAM Systems in Simulation Environment for~Vineyards},
    volume = {13054 LNAI},
    year = {2021}
}

@article{Aivazidou_2021_Sustainable_Water_Management,
    author = {Aivazidou, Eirini and Banias, Georgios and Lampridi, Maria and Vasileiadis, Giorgos and Anagnostis, Athanasios and Papageorgiou, Elpiniki and Bochtis, Dionysis},
    doi = {10.3390/su132413940},
    issn = {2071-1050},
    journal = {Sustainability},
    month = {December},
    number = {24},
    pages = {13940},
    publisher = {MDPI AG},
    title = {Smart Technologies for Sustainable Water Management: An Urban Analysis},
    url = {http://dx.doi.org/10.3390/su132413940},
    volume = {13},
    year = {2021}
}

@article{Al-khafajiy2021_Fog_Nodes,
    author = {Maamar, Z. and Al-Khafajiy, M. and Dohan, M.},
    journal = {Lecture Notes in Networks and Systems},
    pages = {174-186},
    title = {An IoT Application Business-Model on Top of Cloud and Fog Nodes},
    volume = {226 LNNS},
    year = {2021}
}

@article{Benos_2021_Comprehensive_Updated_Review,
    author = {Benos, Lefteris and Tagarakis, Aristotelis C. and Dolias, Georgios and Berruto, Remigio and Kateris, Dimitrios and Bochtis, Dionysis},
    doi = {10.3390/s21113758},
    issn = {1424-8220},
    journal = {Sensors},
    month = {May},
    number = {11},
    pages = {3758},
    publisher = {MDPI AG},
    title = {Machine Learning in Agriculture: A Comprehensive Updated Review},
    url = {http://dx.doi.org/10.3390/s21113758},
    volume = {21},
    year = {2021}
}

@article{fadfbe24978244b7a7629d13fed6ed72_Dialogue_Protocols_Doug,
    abstract = {This paper is intended to honour the memory of Douglas Walton (1942–2020), a Canadian philosopher of argumentation who died in January 2020. Walton{\textquoteright}s contributions to argumentation theory have had a very strong influence on Artificial Intelligence (Ai), particularly in the design of autonomous software agents able to reason and argue with one another, and in the design of protocols to govern such interactions. In this paper, we explore two of these contributions — argumentation schemes and dialogue protocols — by discussing how they may be applied to a pressing current research challenge in Ai: the automated assessment of explanations for automated decision-making systems.},
    author = {Peter McBurney and Simon Parsons},
    issn = {2631-9810},
    journal = {IfCoLoG Journal of Logics and their Applications},
    keywords = {Argument Scheme, Dialogue protocol, XAI},
    language = {English},
    month = {February},
    note = {Funding Information: †SP acknowledges funding from EPSRC grant EP/R033722/1, and thanks Nadin K{\"o}kciyan, Quratual-ain Mahesar, Isabel Sassoon and Elizabeth Sklar for many interesting conversations on the topic of explanations. Publisher Copyright: {\textcopyright} 2021, College Publications. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.},
    number = {1},
    pages = {263--290},
    publisher = {College Publications},
    title = {Argument schemes and dialogue protocols: Doug walton{\textquoteright}s legacy in artificial intelligence},
    volume = {8},
    year = {2021}
}

This paper is intended to honour the memory of Douglas Walton (1942–2020), a Canadian philosopher of argumentation who died in January 2020. Walton\textquoterights contributions to argumentation theory have had a very strong influence on Artificial Intelligence (Ai), particularly in the design of autonomous software agents able to reason and argue with one another, and in the design of protocols to govern such interactions. In this paper, we explore two of these contributions — argumentation schemes and dialogue protocols — by discussing how they may be applied to a pressing current research challenge in Ai: the automated assessment of explanations for automated decision-making systems.

@article{Maamar_2021_Internet_Of_Things,
    author = {Zakaria Maamar and Noura Faci and Mohammed Al-Khafajiy and Murtada Dohan},
    doi = {10.1016/j.iot.2021.100460},
    journal = {Internet of Things},
    month = {sep},
    pages = {100460},
    publisher = {Elsevier {BV}},
    title = {Time-centric and resource-driven composition for the Internet of Things},
    url = {https://doi.org/10.1016%2Fj.iot.2021.100460},
    year = {2021}
}

@article{Cielniak2021_Properties_Ofdynamic_Objects,
    author = {Wagner, N. and Cielniak, G.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {430-439},
    title = {Inference of Mechanical Properties of~Dynamic Objects Through Active Perception},
    volume = {13054 LNAI},
    year = {2021}
}

@article{salazar2021towards_Practical_Object_Detection,
    author = {Salazar-Gomez, Adrian and Darbyshire, Madeleine and Gao, Junfeng and Sklar, Elizabeth I and Parsons, Simon},
    journal = {arXiv preprint arXiv:2109.11048},
    title = {Towards practical object detection for weed spraying in precision agriculture},
    year = {2021}
}

@inproceedings{jansen2021predicting_Artist_Drawing_Activity,
    author = {Jansen, Chipp and Sklar, Elizabeth},
    booktitle = {Annual Conference Towards Autonomous Robotic Systems},
    organization = {Springer International Publishing Cham},
    pages = {217--227},
    title = {Predicting Artist Drawing Activity via Multi-camera Inputs for Co-creative Drawing},
    year = {2021}
}

@article{Das2021_Allocation_Ofparking_Spaces,
    author = {Ravikanna, R. and Hanheide, M. and Das, G. and Zhu, Z.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {337-348},
    title = {Maximising Availability of Transportation Robots Through Intelligent Allocation of~Parking Spaces},
    volume = {13054 LNAI},
    year = {2021}
}

@article{Das2021_Deadlock_Free_Framework,
    author = {Heselden, J.R. and Das, G.P.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {66-75},
    title = {CRH*: A Deadlock Free Framework for~Scalable Prioritised Path Planning in~Multi-robot Systems},
    volume = {13054 LNAI},
    year = {2021}
}

@article{Hanheide2021_Allocation_Ofparking_Spaces,
    author = {Ravikanna, R. and Hanheide, M. and Das, G. and Zhu, Z.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {337-348},
    title = {Maximising Availability of Transportation Robots Through Intelligent Allocation of~Parking Spaces},
    volume = {13054 LNAI},
    year = {2021}
}

@article{Marinoudi_2021_View_Of_Robotization,
    author = {Marinoudi, Vasso and Lampridi, Maria and Kateris, Dimitrios and Pearson, Simon and Sørensen, Claus Grøn and Bochtis, Dionysis},
    doi = {10.3390/su132112109},
    issn = {2071-1050},
    journal = {Sustainability},
    month = {November},
    number = {21},
    pages = {12109},
    publisher = {MDPI AG},
    title = {The Future of Agricultural Jobs in View of Robotization},
    url = {http://dx.doi.org/10.3390/su132112109},
    volume = {13},
    year = {2021}
}

@conference{11577_3455203_Task_Success_Classifiers,
    abstract = {Robots learning a new manipulation task from a small amount of demonstrations are increasingly demanded in different workspaces. A classifier model assessing the quality of actions can predict the successful completion of a task, which can be used by intelligent agents for action-selection. This paper presents a novel classifier that learns to classify task completion only from a few demonstrations. We carry out a comprehensive comparison of different neural classifiers, e.g. fully connected-based, fully convolutional-based, sequence2sequence-based, and domain adaptation-based classification. We also present a new dataset including five robot manipulation tasks, which is publicly available. We compared the performances of our novel classifier and the existing models using our dataset and the MIME dataset. The results suggest domain adaptation and timing-based features improve success prediction. Our novel model, i.e. fully convolutional neural network with domain adaptation and timing features, achieves an average classification accuracy of 97.3% and 95.5% across tasks in both datasets whereas state-of-the-art classifiers without domain adaptation and timing-features only achieve 82.4% and 90.3%, respectively.},
    author = {Mohtasib, A and Ghalamzan, Ea and Bellotto, N and Cuayahuitl, H},
    booktitle = {Proceedings of International Joint Conference on Neural Networks (IJCNN)},
    doi = {10.1109/IJCNN52387.2021.9534141},
    isbn = {978-1-6654-3900-8},
    keywords = {Deep Learning; Reward Learning; Task Success; Task Timing; Domain Adaptation; Robot Skill Learning},
    pages = {1--8},
    publisher = {IEEE},
    title = {Neural Task Success Classifiers for Robotic Manipulation from Few Real Demonstrations},
    volume = {2021-July},
    year = {2021}
}

Robots learning a new manipulation task from a small amount of demonstrations are increasingly demanded in different workspaces. A classifier model assessing the quality of actions can predict the successful completion of a task, which can be used by intelligent agents for action-selection. This paper presents a novel classifier that learns to classify task completion only from a few demonstrations. We carry out a comprehensive comparison of different neural classifiers, e.g. fully connected-based, fully convolutional-based, sequence2sequence-based, and domain adaptation-based classification. We also present a new dataset including five robot manipulation tasks, which is publicly available. We compared the performances of our novel classifier and the existing models using our dataset and the MIME dataset. The results suggest domain adaptation and timing-based features improve success prediction. Our novel model, i.e. fully convolutional neural network with domain adaptation and timing features, achieves an average classification accuracy of 97.3% and 95.5% across tasks in both datasets whereas state-of-the-art classifiers without domain adaptation and timing-features only achieve 82.4% and 90.3%, respectively.

@article{harman2021auction_Auctionbased_Task_Allocation,
    author = {Harman, Helen and Sklar, Elizabeth and others},
    title = {Auction-based task allocation mechanisms for managing fruit harvesting tasks},
    year = {2021}
}

@inproceedings{Almaghout_2021_Hybrid_Visionforce_Control,
    author = {Almaghout, Karam and Boby, Riby Abraham and Othman, Mostafa and Shaarawy, Abdelaziz and Klimchik, Alexandr},
    booktitle = {2021 International Conference “Nonlinearity, Information and Robotics” (NIR)},
    doi = {10.1109/nir52917.2021.9666138},
    month = {August},
    pages = {1–6},
    publisher = {IEEE},
    title = {Robotic Pick and Assembly Using Deep Learning and Hybrid Vision/Force Control},
    url = {http://dx.doi.org/10.1109/NIR52917.2021.9666138},
    year = {2021}
}

@inproceedings{jansen2021visually_Artists_Drawing,
    author = {Jansen, Chipp and Sklar, Elizabeth I},
    booktitle = {Proceedings of the 4th UK-RAS Conference: Robotics at Home (\# UKRAS21)},
    title = {Visually-based Prediction of Artist’s Drawing},
    year = {2021}
}

@inproceedings{harman2021practical_Allocating_Harvesting_Tasks,
    author = {Harman, Helen and Sklar, Elizabeth I},
    booktitle = {Advances in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection: 19th International Conference, PAAMS 2021, Salamanca, Spain, October 6--8, 2021, Proceedings 19},
    organization = {Springer International Publishing},
    pages = {114--126},
    title = {A practical application of market-based mechanisms for allocating harvesting tasks},
    year = {2021}
}

@article{Hanheide2021_Human_Motion_Prediction,
    author = {Palmieri, L. and Andrey, R. and Mainprice, J. and Hanheide, M. and Alahi, A. and Lilienthal, A. and Arras, K.O.},
    journal = {IEEE Robotics and Automation Letters},
    number = {3},
    pages = {5613-5617},
    title = {Guest Editorial: Introduction to the Special Issue on Long-Term Human Motion Prediction},
    volume = {6},
    year = {2021}
}

@article{Hanheide2021_Responsible_Development,
    author = {Rose, D.C. and Lyon, J. and de Boon, A. and Hanheide, M. and Pearson, S.},
    journal = {Nature Food},
    number = {5},
    pages = {306-309},
    title = {Responsible development of autonomous robotics in agriculture},
    volume = {2},
    year = {2021}
}

@article{Cielniak2021_Informative_Path_Planning,
    author = {Choi, T. and Cielniak, G.},
    journal = {arXiv},
    title = {Adaptive selection of informative path planning strategies via reinforcement learning},
    year = {2021}
}

@article{lujak2021agriculture_Agriculture_Fleet_Vehicle,
    author = {Lujak, Marin and Sklar, Elizabeth and Semet, Frederic},
    journal = {AI Communications},
    number = {1},
    pages = {55--71},
    publisher = {IOS Press},
    title = {Agriculture fleet vehicle routing: A decentralised and dynamic problem},
    volume = {34},
    year = {2021}
}

@inproceedings{Tagarakis_2021_Smart_Farming_System,
    author = {Tagarakis, Aristotelis C. and Dordas, Christos and Lampridi, Maria and Kateris, Dimitrios and Bochtis, Dionysis},
    booktitle = {The 13th EFITA International Conference},
    collection = {EFITA 2021},
    doi = {10.3390/engproc2021009010},
    month = {November},
    pages = {10},
    publisher = {MDPI},
    series = {EFITA 2021},
    title = {A Smart Farming System for Circular Agriculture},
    url = {http://dx.doi.org/10.3390/engproc2021009010},
    year = {2021}
}

@article{Hanheide2021_Dynamic_Behaviour_Prediction,
    author = {Nazari, K. and Mandill, W. and Hanheide, M. and Esfahani, A.G.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {284-293},
    title = {Tactile Dynamic Behaviour Prediction Based on Robot Action},
    volume = {13054 LNAI},
    year = {2021}
}

@article{Wang_2021_Visually_Guided_Flights,
    author = {Huatian Wang and Qinbing Fu and Hongxin Wang and Paul Baxter and Jigen Peng and Shigang Yue},
    doi = {10.1016/j.neunet.2020.12.008},
    journal = {Neural Networks},
    month = {apr},
    pages = {180--193},
    publisher = {Elsevier {BV}},
    title = {A bioinspired angular velocity decoding neural network model for visually guided flights},
    url = {https://doi.org/10.1016%2Fj.neunet.2020.12.008},
    volume = {136},
    year = {2021}
}

@article{Cielniak2021_Plant_Point_Clouds,
    author = {Heiwolt, K. and Duckett, T. and Cielniak, G.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {36-45},
    title = {Deep Semantic Segmentation of 3D Plant Point Clouds},
    volume = {13054 LNAI},
    year = {2021}
}

@article{Cielniak2021_Occlusions_With_Reidentification,
    author = {Kirk, R. and Mangan, M. and Cielniak, G.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {211-222},
    title = {Robust Counting of Soft Fruit Through Occlusions with Re-identification},
    volume = {12899 LNCS},
    year = {2021}
}

@article{Savin_2021_Orthogonal_Decomposition_Method,
    author = {Savin, Sergei and Balakhnov, Oleg and Khusainov, Ramil and Klimchik, Alexandr},
    doi = {10.3390/s21186312},
    issn = {1424-8220},
    journal = {Sensors},
    month = {September},
    number = {18},
    pages = {6312},
    publisher = {MDPI AG},
    title = {State Observer for Linear Systems with Explicit Constraints: Orthogonal Decomposition Method},
    url = {http://dx.doi.org/10.3390/s21186312},
    volume = {21},
    year = {2021}
}

@inbook{Harman_2021_Allocating_Harvesting_Tasks,
    author = {Harman, Helen and Sklar, Elizabeth I.},
    booktitle = {Advances in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection},
    doi = {10.1007/978-3-030-85739-4_10},
    isbn = {9783030857394},
    issn = {1611-3349},
    pages = {114–126},
    publisher = {Springer International Publishing},
    title = {A Practical Application of Market-Based Mechanisms for Allocating Harvesting Tasks},
    url = {http://dx.doi.org/10.1007/978-3-030-85739-4_10},
    year = {2021}
}

@inproceedings{gomez2021need_Communication_Can_Support,
    author = {Gomez, Adrian Salazar and Zhivkov, Tsvetan and Gao, Junfeng and Sklar, Elizabeth I and Parsons, Simon},
    booktitle = {UKRAS21 Conference “Robotics at Home},
    title = {The need for speed: How 5G communication can support AI in the field},
    year = {2021}
}

@article{Hanheide2021_Fruit_Picking_Applications,
    author = {Wagner, N. and Kirk, R. and Hanheide, M. and Cielniak, G.},
    journal = {Proceedings - IEEE International Conference on Robotics and Automation},
    pages = {6818-6823},
    title = {Efficient and Robust Orientation Estimation of Strawberries for Fruit Picking Applications},
    volume = {2021-May},
    year = {2021}
}

@article{Isakhani_2021_Micro_Aerial_Robots,
    author = {Hamid Isakhani and Nicola Bellotto and Qinbing Fu and Shigang Yue},
    doi = {10.1093/jcde/qwab040},
    journal = {Journal of Computational Design and Engineering},
    month = {aug},
    number = {5},
    pages = {1191--1203},
    publisher = {Oxford University Press ({OUP})},
    title = {Generative design and fabrication of a locust-inspired gliding wing prototype for micro aerial robots},
    url = {https://doi.org/10.1093%2Fjcde%2Fqwab040},
    volume = {8},
    year = {2021}
}

@article{Cielniak2021_Agricultural_Autonomy_Crop,
    author = {De Silva, R. and Cielniak, G. and Gao, J.},
    journal = {arXiv},
    title = {Towards agricultural autonomy: Crop row detection under varying field conditions using deep learning},
    year = {2021}
}

@article{Cielniak2021_Gaussian_Map_Predictions,
    author = {Lou{\"e}dec, J.L. and Cielniak, G.},
    journal = {32nd British Machine Vision Conference, BMVC 2021},
    title = {Gaussian map predictions for 3D surface feature localisation and counting},
    year = {2021}
}

@article{Achillas_2021_Supply_Chain_Management,
    author = {Achillas, Charisios and Bochtis, Dionysis},
    doi = {10.3390/en14196097},
    issn = {1996-1073},
    journal = {Energies},
    month = {September},
    number = {19},
    pages = {6097},
    publisher = {MDPI AG},
    title = {Supply Chain Management for Bioenergy and Bioresources: Bridging the Gap between Theory and Practice},
    url = {http://dx.doi.org/10.3390/en14196097},
    volume = {14},
    year = {2021}
}

@article{Cielniak2021_Lidar_Slam_Systems,
    author = {Hroob, I. and Polvara, R. and Molina, S. and Cielniak, G. and Hanheide, M.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {168-177},
    title = {Benchmark of Visual and 3D Lidar SLAM Systems in Simulation Environment for~Vineyards},
    volume = {13054 LNAI},
    year = {2021}
}

@article{Zhao_2021_Compliant_Serial_Manipulator,
    author = {Zhao, Wanda and Pashkevich, Anatol and Chablat, Damien and Klimchik, Alexandr},
    doi = {10.18178/ijmerr.10.4.169-176},
    issn = {2278-0149},
    journal = {International Journal of Mechanical Engineering and Robotics Research},
    pages = {169–176},
    publisher = {EJournal Publishing},
    title = {Mechanics of Compliant Serial Manipulator Composed of Dual-triangle Segments},
    url = {http://dx.doi.org/10.18178/IJMERR.10.4.169-176},
    year = {2021}
}

@article{Cielniak2021_Humanaware_Riskbased_Braking,
    author = {Mayoral, J.C. and Grimstad, L. and From, P.J. and Cielniak, G.},
    journal = {Proceedings - IEEE International Conference on Robotics and Automation},
    pages = {2435-2442},
    title = {Integration of a Human-aware Risk-based Braking System into an Open-Field Mobile Robot},
    volume = {2021-May},
    year = {2021}
}

@incollection{Tayeb_2021_Realtime_Reflex_System,
    author = {Zied Tayeb and Rohit Bose and Andrei Dragomir and Luke E. Osborn and Nitish V. Thakor and Gordon Cheng},
    booktitle = {{SpringerBriefs} in Electrical and Computer Engineering},
    doi = {10.1007/978-3-030-79287-9_5},
    pages = {47--58},
    publisher = {Springer International Publishing},
    title = {{EEG} Decoding of Pain Perception for a Real-Time Reflex System in Prostheses},
    url = {https://doi.org/10.1007%2F978-3-030-79287-9_5},
    year = {2021}
}

@article{kokciyan2021applying_Applying_Metalevel_Argumentation,
    author = {K{\"o}kciyan, Nadin and Sassoon, Isabel and Sklar, Elizabeth and Modgil, Sanjay and Parsons, Simon},
    journal = {IEEE Intelligent Systems},
    number = {2},
    pages = {64--71},
    publisher = {IEEE},
    title = {Applying metalevel argumentation frameworks to support medical decision making},
    volume = {36},
    year = {2021}
}

@article{Popov_2021_Multiple_Potential_Contact,
    author = {Dmitry Popov and Alexandr Klimchik and Anatol Pashkevich},
    doi = {10.1109/lra.2021.3095902},
    journal = {{IEEE} Robotics and Automation Letters},
    month = {oct},
    number = {4},
    pages = {7025--7032},
    publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
    title = {Real-Time Estimation of Multiple Potential Contact Locations and Forces},
    url = {https://doi.org/10.1109%2Flra.2021.3095902},
    volume = {6},
    year = {2021}
}

@article{Polvara_2021_Agricultural_Environments,
    author = {Polvara, Riccardo and Del Duchetto, Francesco and Neumann, Gerhard and Hanheide, Marc},
    doi = {10.1109/lra.2021.3094557},
    issn = {2377-3774},
    journal = {IEEE Robotics and Automation Letters},
    month = {October},
    number = {4},
    pages = {6577–6584},
    publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
    title = {Navigate-and-Seek: A Robotics Framework for People Localization in Agricultural Environments},
    url = {http://dx.doi.org/10.1109/LRA.2021.3094557},
    volume = {6},
    year = {2021}
}

@article{Hanheide2021_Lidar_Slam_Systems,
    author = {Hroob, I. and Polvara, R. and Molina, S. and Cielniak, G. and Hanheide, M.},
    journal = {arXiv},
    title = {Benchmark of visual and 3D lidar SLAM systems in simulation environment for vineyards},
    year = {2021}
}

@article{Cielniak2021_Soil_Moisture_Measurement,
    author = {Badiee, A. and Wallbank, J.R. and Fentanes, J.P. and Trill, E. and Scarlet, P. and Zhu, Y. and Cielniak, G. and Cooper, H. and Blake, J.R. and Evans, J.G. and Zreda, M. and K{\"o}hli, M. and Pearson, S.},
    journal = {Water Resources Research},
    number = {6},
    title = {Using Additional Moderator to Control the Footprint of a COSMOS Rover for Soil Moisture Measurement},
    volume = {57},
    year = {2021}
}

@article{dai2021small_Small_Datasets,
    author = {Dai, Dan and Gao, Junfeng and Parsons, Simon and Sklar, Elizabeth and others},
    publisher = {UK-RAS},
    title = {Small datasets for fruit detection with transfer learning},
    year = {2021}
}

@article{Anagnostis_2021_Recurrent_Neural_Networks,
    author = {Anagnostis, Athanasios and Benos, Lefteris and Tsaopoulos, Dimitrios and Tagarakis, Aristotelis and Tsolakis, Naoum and Bochtis, Dionysis},
    doi = {10.3390/app11052188},
    issn = {2076-3417},
    journal = {Applied Sciences},
    month = {March},
    number = {5},
    pages = {2188},
    publisher = {MDPI AG},
    title = {Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture},
    url = {http://dx.doi.org/10.3390/app11052188},
    volume = {11},
    year = {2021}
}

@article{korir2021current_Emergent_Economic_Impacts,
    author = {Korir, Lilian and Drake, Archie and Collison, Martin and Camacho-Villa, Tania Carolina and Sklar, Elizabeth and Pearson, Simon},
    journal = {arXiv preprint arXiv:2101.11551},
    title = {Current and emergent economic impacts of Covid-19 and Brexit on UK fresh produce and horticultural businesses},
    year = {2021}
}

@inbook{51d5d7a4816e4e32b224a6973a63e1b0,
  title     = "Implementing argument and explanation schemes in dialogue",
  keywords  = "Argumentation Schemes, Dialogue, Explanation",
  author    = "Isabel Sassoon and Nadin Kokciyan and Martin Chapman and Elizabeth Sklar and Vasa Curcin and Sanjay Modgil and Simon Parsons",
  year      = "2020",
  month     = aug,
  day       = "31",
  doi       = "10.3233/FAIA200539",
  language  = "English",
  series    = "Frontiers in Artificial Intelligence and Applications",
  publisher = "IOS Press BV",
  booktitle = "Computational models of argument",
  note      = "8th International Conference on Computational Models of Argument, COMMA 2020 ; Conference date: 08-09-2020 Through 11-09-2020",
}

@article{Lock_2020,     doi = {10.1145/3412325},        url = {https://doi.org/10.1145%2F3412325},      year = 2020,    month = {oct},  publisher = {Association for Computing Machinery ({ACM})},      volume = {13},  number = {4},   pages = {1--21},        author = {Jacobus C. Lock and Iain D. Gilchrist and Iain D. Gilchrist and Grzegorz Cielniak and Nicola Bellotto},       title = {Experimental Analysis of a Spatialised Audio Interface for People with Visual Impairments},    journal = {{ACM} Transactions on Accessible Computing}}

@article{Galimov_2020_Uav_Positioning_Mechanisms,
    author = {Musa Galimov and Roman Fedorenko and Alexander Klimchik},
    doi = {10.3390/s20133648},
    journal = {Sensors},
    month = {jun},
    number = {13},
    pages = {3648},
    publisher = {{MDPI} {AG}},
    title = {{UAV} Positioning Mechanisms in Landing Stations: Classification and Engineering Design Review},
    url = {https://doi.org/10.3390%2Fs20133648},
    volume = {20},
    year = {2020}
}

@inbook{5001511ed56b4b9997371257a2e5c905_Generate_Domainspecific_Explanations,
    abstract = {In argumentation theory, argument schemes are constructs to generalise common patterns of reasoning; whereas critical questions (CQs) capture the reasons why argument schemes might not generate arguments. Argument schemes together with CQs are widely used to instantiate arguments; however when it comes to making decisions, much less attention has been paid to the attacks among arguments. This paper provides a high-level description of the key elements necessary for the formalisation of argumentation frameworks such as argument schemes and CQs. Attack schemes are then introduced to represent attacks among arguments, which enable the definition of domain-specific attacks. One algorithm is articulated to operationalise the use of schemes to generate an argumentation framework, and another algorithm to support decision making by generating domain-specific explanations. Such algorithms can then be used by agents to make recommendations and to provide explanations for humans. The applicability of this approach is demonstrated within the context of a medical case study.},
    address = {Germany},
    author = {Nadin K{\"o}kciyan and Simon Parsons and Isabel Sassoon and Elizabeth Sklar and Sanjay Modgil},
    booktitle = {Multi-Agent Systems and Agreement Technologies - 17th European Conference, EUMAS 2020, and 7th International Conference, AT 2020, Revised Selected Papers},
    doi = {10.1007/978-3-030-66412-1_20},
    editor = {Nick Bassiliades and Georgios Chalkiadakis and {de Jonge}, Dave},
    isbn = {9783030664114},
    keywords = {Computational argumentation, Explainability, Human-agent systems},
    language = {English},
    note = {Funding Information: This work was supported by the UK Engineering & Physical Sciences Research Council (EPSRC) under grant #EP/P010105/1. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 17th European Conference on Multi-Agent Systems, EUMAS 2020, and 7th International Conference on Agreement Technologies, AT 2020 ; Conference date: 14-09-2020 Through 15-09-2020},
    pages = {319--337},
    publisher = {Springer Science and Business Media Deutschland GmbH},
    series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    title = {An Argumentation-Based Approach to Generate Domain-Specific Explanations},
    year = {2020}
}

In argumentation theory, argument schemes are constructs to generalise common patterns of reasoning; whereas critical questions (CQs) capture the reasons why argument schemes might not generate arguments. Argument schemes together with CQs are widely used to instantiate arguments; however when it comes to making decisions, much less attention has been paid to the attacks among arguments. This paper provides a high-level description of the key elements necessary for the formalisation of argumentation frameworks such as argument schemes and CQs. Attack schemes are then introduced to represent attacks among arguments, which enable the definition of domain-specific attacks. One algorithm is articulated to operationalise the use of schemes to generate an argumentation framework, and another algorithm to support decision making by generating domain-specific explanations. Such algorithms can then be used by agents to make recommendations and to provide explanations for humans. The applicability of this approach is demonstrated within the context of a medical case study.

@article{Hanheide2020_Movement_Primitives_Planning,
    author = {Mghames, S. and Hanheide, M. and Ghalamzan E., A.},
    journal = {IEEE International Conference on Intelligent Robots and Systems},
    pages = {2616-2623},
    title = {Interactive movement primitives: Planning to push occluding pieces for fruit picking},
    year = {2020}
}

@article{Yan_2020_Autonomous_Floor_Scrubber,
    author = {Zhi Yan and Simon Schreiberhuber and Georg Halmetschlager and Tom Duckett and Markus Vincze and Nicola Bellotto},
    doi = {10.1007/s11370-020-00324-9},
    journal = {Intelligent Service Robotics},
    month = {jun},
    number = {3},
    pages = {403--417},
    publisher = {Springer Science and Business Media {LLC}},
    title = {Robot perception of static and dynamic objects with an autonomous floor scrubber},
    url = {https://doi.org/10.1007%2Fs11370-020-00324-9},
    volume = {13},
    year = {2020}
}

@article{Bartlett_2020_Autism_Spectrum_Disorder,
    author = {Madeleine E Bartlett and Cristina Costescu and Paul Baxter and Serge Thill},
    doi = {10.3390/info11020081},
    journal = {Information},
    month = {feb},
    number = {2},
    pages = {81},
    publisher = {{MDPI} {AG}},
    title = {Requirements for Robotic Interpretation of Social Signals {\textquotedblleft}in the Wild{\textquotedblright}: Insights from Diagnostic Criteria of Autism Spectrum Disorder},
    url = {https://doi.org/10.3390%2Finfo11020081},
    volume = {11},
    year = {2020}
}

@article{Das2020_Incorporating_Spatial_Constraints,
    author = {Khan, M.W. and Das, G.P. and Hanheide, M. and Cielniak, G.},
    journal = {IEEE International Conference on Intelligent Robots and Systems},
    pages = {2440-2445},
    title = {Incorporating spatial constraints into a bayesian tracking framework for improved localisation in agricultural environments},
    year = {2020}
}

@article{zhivkov2020modelling_Modelling_Variable_Communication,
    author = {Zhivkov, Tsvetan and Sklar, Elizabeth and others},
    publisher = {UK-RAS},
    title = {Modelling variable communication signal strength for experiments with multi-robot teams},
    year = {2020}
}

@article{schneider2020application_Marketbased_Multirobot_Task,
    author = {Schneider, Eric and Poulton, Marcus and Drake, Archie and Smith, Leanne and Roussos, George and Parsons, Simon and Sklar, Elizabeth I},
    journal = {arXiv preprint arXiv:2003.05550},
    title = {The Application of Market-based Multi-Robot Task Allocation to Ambulance Dispatch},
    year = {2020}
}

@inproceedings{miyauchi2020study_Multirobot_Teams,
    author = {Miyauchi, Genki and Sklar, Elizabeth I},
    booktitle = {Towards Autonomous Robotic Systems: 21st Annual Conference, TAROS 2020, Nottingham, UK, September 16, 2020, Proceedings 21},
    organization = {Springer International Publishing},
    pages = {138--143},
    title = {A study assessing the impact of task duration on performance metrics for multi-robot teams},
    year = {2020}
}

@article{Cielniak2020_Broccoli_Head_Detection,
    author = {Louedec, J.L. and Montes, H.A. and Duckett, T. and Cielniak, G.},
    journal = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
    pages = {285-293},
    title = {Segmentation and detection from organised 3d point clouds: A case study in broccoli head detection},
    volume = {2020-June},
    year = {2020}
}

@article{Hanheide2020_Robot_Social_Abilities,
    author = {Del Duchetto, F. and Baxter, P. and Hanheide, M.},
    journal = {ACM/IEEE International Conference on Human-Robot Interaction},
    pages = {561-563},
    title = {Automatic assessment and learning of robot social abilities},
    year = {2020}
}

@inproceedings{Mikhel_2020_Stiffness_Model_Reduction,
    author = {Mikhel, Stanislav and Klimchik, Alexandr},
    booktitle = {2020 International Conference Nonlinearity, Information and Robotics (NIR)},
    doi = {10.1109/nir50484.2020.9290194},
    month = {December},
    pages = {1–6},
    publisher = {IEEE},
    title = {Algebraic approach to the stiffness model reduction for manipulators with double encoders},
    url = {http://dx.doi.org/10.1109/NIR50484.2020.9290194},
    year = {2020}
}

@article{lujak2020multi_Agrirobot_Fleets,
    author = {Lujak, Marin and Sklar, Elizabeth and Semet, Frederic},
    title = {On Multi-Agent Coordination of Agri-Robot Fleets},
    year = {2020}
}

@inproceedings{Popov_2020_Collaborative_Robotics,
    author = {Popov, Dmitry and Klimchik, Alexandr},
    booktitle = {Proceedings of the 3rd International Conference on Applications of Intelligent Systems},
    collection = {APPIS 2020},
    doi = {10.1145/3378184.3378192},
    month = {January},
    pages = {1–7},
    publisher = {ACM},
    series = {APPIS 2020},
    title = {Transfer Learning for Collision Localization in Collaborative Robotics},
    url = {http://dx.doi.org/10.1145/3378184.3378192},
    year = {2020}
}

@article{Benos_2020_Manual_Operations,
    author = {Benos, Lefteris and Tsaopoulos, Dimitrios and Bochtis, Dionysis},
    doi = {10.3390/app10061905},
    issn = {2076-3417},
    journal = {Applied Sciences},
    month = {March},
    number = {6},
    pages = {1905},
    publisher = {MDPI AG},
    title = {A Review on Ergonomics in Agriculture. Part I: Manual Operations},
    url = {http://dx.doi.org/10.3390/app10061905},
    volume = {10},
    year = {2020}
}

@inproceedings{huang2020understanding_Understanding_Human_Responses,
    author = {Huang, Zhuoling and Gomez, A and Bird, Richie and Kalsi, A and Jansen, Chipp and Liu, Zeyang and Miyauchi, Genki and Parsons, Simon and Sklar, E},
    booktitle = {Proceedings of the UKRAS20 Conference:“Robots into the Real World” Proceedings, Lincoln, England},
    pages = {89--91},
    title = {Understanding human responses to errors in a collaborative human-robot selective harvesting task},
    volume = {17},
    year = {2020}
}

@conference{11577_3373517_Realtime_Object_Detection,
    abstract = {Object detection plays a crucial role in the development of Electronic Travel Aids (ETAs), capable to guide a person with visual impairments towards a target object in an unknown indoor environment. In such a scenario, the object detector runs on a mobile device (e.g. smartphone) and needs to be fast, accurate, and, most importantly, lightweight. Nowadays, Deep Neural Networks (DNN) have become the state-of-the-art solution for object detection tasks, with many works improving speed and accuracy by proposing new architectures or extending existing ones. A common strategy is to use deeper networks to get higher performance, but that leads to a higher computational cost which makes it impractical to integrate them on mobile devices with limited computational power. In this work we compare different object detectors to find a suitable candidate to be implemented on ETAs, focusing on lightweight models capable of working in real-time on mobile devices with a good accuracy. In particular, we select two models: SSD Lite with Mobilenet V2 and Tiny-DSOD. Both models have been tested on the popular OpenImage dataset and a new dataset, named L-CAS Office dataset, collected to further test models’ performance and robustness in a real scenario inspired by the actual perception challenges of a user with visual impairments.},
    author = {Terreran, Matteo and Tramontano, Andrea G. and Lock, Jacobus C. and Ghidoni, Stefano and Bellotto, Nicola},
    booktitle = {IEEE International Conference on Image Processing, Applications and Systems, IPAS},
    doi = {10.1109/IPAS50080.2020.9334933},
    isbn = {978-1-7281-7575-1},
    keywords = {Object detection, real-time, electronic travel aid},
    pages = {89--95},
    title = {Real-time Object Detection using Deep Learning for helping People with Visual Impairments},
    year = {2020}
}

Object detection plays a crucial role in the development of Electronic Travel Aids (ETAs), capable to guide a person with visual impairments towards a target object in an unknown indoor environment. In such a scenario, the object detector runs on a mobile device (e.g. smartphone) and needs to be fast, accurate, and, most importantly, lightweight. Nowadays, Deep Neural Networks (DNN) have become the state-of-the-art solution for object detection tasks, with many works improving speed and accuracy by proposing new architectures or extending existing ones. A common strategy is to use deeper networks to get higher performance, but that leads to a higher computational cost which makes it impractical to integrate them on mobile devices with limited computational power. In this work we compare different object detectors to find a suitable candidate to be implemented on ETAs, focusing on lightweight models capable of working in real-time on mobile devices with a good accuracy. In particular, we select two models: SSD Lite with Mobilenet V2 and Tiny-DSOD. Both models have been tested on the popular OpenImage dataset and a new dataset, named L-CAS Office dataset, collected to further test models’ performance and robustness in a real scenario inspired by the actual perception challenges of a user with visual impairments.

@article{Hanheide2020_Rfid_Tags_Discovery,
    author = {Polvara, R. and Fernandez-Carmona, M. and Neumann, G. and Neumann, G. and Neumann, G. and Hanheide, M.},
    journal = {IEEE Robotics and Automation Letters},
    number = {3},
    pages = {4477-4484},
    title = {Next-Best-Sense: A Multi-Criteria Robotic Exploration Strategy for RFID Tags Discovery},
    volume = {5},
    year = {2020}
}

@phdthesis{zhivkov2020multi_Multirobot_Communication_Framework,
    author = {Zhivkov, Tsvetan and Sklar, Elizabeth and Miles, Simon},
    school = {King's College London},
    title = {A Multi-robot Communication Framework for the Analysis and Mitigation of Network Perturbations},
    year = {2020}
}

@inbook{79678fc574a54d1bb057caeaeaabe388_Pilot_Study,
    abstract = {Prior work has investigated the problem mining arguments from online reviews by classifying opinions based on the stance expressed explicitly or implicitly. An implicit opinion has the stance left unexpressed linguistically while an explicit opinion has the stance expressed explicitly. In this paper, we propose a bipartite graph-based approach to relate a given set of explicit opinions as simplified arguments for a given set of implicit opinions using three different features (a) sentence similarity, (b) sentiment and (c) target. Experiments are carried out on a manually annotated set of explicit-implicit opinions and show that unsupervised sentence representations can be used to accurately match arguments with their corresponding simplified versions.},
    author = {Pavithra Rajendran and Danushka Bollegala and Simon Parsons},
    booktitle = {Computational Linguistics - 16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019, Revised Selected Papers},
    day = {1},
    doi = {10.1007/978-981-15-6168-9_19},
    editor = {Le-Minh Nguyen and Satoshi Tojo and Xuan-Hieu Phan and K{\^o}iti Hasida},
    isbn = {9789811561672},
    keywords = {Argument mining, Argument simplification},
    language = {English},
    month = {January},
    note = {16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019 ; Conference date: 11-10-2019 Through 13-10-2019},
    pages = {218--230},
    publisher = {SPRINGER},
    series = {Communications in Computer and Information Science},
    title = {A Pilot Study on Argument Simplification in Stance-Based Opinions},
    year = {2020}
}

Prior work has investigated the problem mining arguments from online reviews by classifying opinions based on the stance expressed explicitly or implicitly. An implicit opinion has the stance left unexpressed linguistically while an explicit opinion has the stance expressed explicitly. In this paper, we propose a bipartite graph-based approach to relate a given set of explicit opinions as simplified arguments for a given set of implicit opinions using three different features (a) sentence similarity, (b) sentiment and (c) target. Experiments are carried out on a manually annotated set of explicit-implicit opinions and show that unsupervised sentence representations can be used to accurately match arguments with their corresponding simplified versions.

@article{Hanheide2020_Humanrobot_Spatial_Interaction,
    author = {Roberts-Elliott, L. and Fernandez-Carmona, M. and Hanheide, M.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {249-260},
    title = {Towards Safer Robot Motion: Using a Qualitative Motion Model to Classify Human-Robot Spatial Interaction},
    volume = {12228 LNAI},
    year = {2020}
}

@article{Papageorgiou_2020_Natural_Gas_Consumption,
    author = {Papageorgiou, Konstantinos and I. Papageorgiou, Elpiniki and Poczeta, Katarzyna and Bochtis, Dionysis and Stamoulis, George},
    doi = {10.3390/en13092317},
    issn = {1996-1073},
    journal = {Energies},
    month = {May},
    number = {9},
    pages = {2317},
    publisher = {MDPI AG},
    title = {Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System},
    url = {http://dx.doi.org/10.3390/en13092317},
    volume = {13},
    year = {2020}
}

@article{prakken2020implementing_Implementing_Argument,
    author = {Prakken, H and others},
    journal = {Computational Models of Argument: Proceedings of COMMA 2020},
    pages = {471},
    publisher = {IOS Press},
    title = {Implementing argument and explanation schemes in dialogue},
    volume = {326},
    year = {2020}
}

@article{Cielniak2020_Agricultural_Environments,
    author = {Le Louedec, J. and Li, B. and Cielniak, G.},
    journal = {VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications},
    pages = {682-689},
    title = {Evaluation of 3D vision systems for detection of small objects in agricultural environments},
    volume = {5},
    year = {2020}
}

@article{11577_3455204_Dnbsplidarbased_Human_Detection,
    author = {Yan, Z. and Duckett, T. and Bellotto, N.},
    doi = {10.1007/s10514-019-09883-y},
    journal = {AUTONOMOUS ROBOTS},
    pages = {147--164},
    title = {Online learning for 3D LiDAR-based human detection: experimental analysis of point cloud clustering and classification methods},
    volume = {44},
    year = {2020}
}

@article{Papageorgiou_2020_Cognitive_Map_Technique,
    author = {Papageorgiou, Konstantinos and Carvalho, Gustavo and Papageorgiou, Elpiniki I. and Bochtis, Dionysis and Stamoulis, George},
    doi = {10.3390/en13061427},
    issn = {1996-1073},
    journal = {Energies},
    month = {March},
    number = {6},
    pages = {1427},
    publisher = {MDPI AG},
    title = {Decision-Making Process for Photovoltaic Solar Energy Sector Development using Fuzzy Cognitive Map Technique},
    url = {http://dx.doi.org/10.3390/en13061427},
    volume = {13},
    year = {2020}
}

@article{canal2020building_Building_Trust,
    author = {Canal, Gerard and Borgo, Rita and Coles, Andrew and Drake, Archie and Huynh, Dong and Keller, Perry and Krivi{\'c}, Senka and Luff, Paul and Mahesar, Quratul-ain and Moreau, Luc and others},
    journal = {Computer Law \& Security Review},
    pages = {105489},
    publisher = {Elsevier Advanced Technology},
    title = {Building trust in human-machine partnerships},
    volume = {39},
    year = {2020}
}

@article{Bochtis_2020_Agricultural_Workforce_Crisis,
    author = {Bochtis, Dionysis and Benos, Lefteris and Lampridi, Maria and Marinoudi, Vasso and Pearson, Simon and Sørensen, Claus G.},
    doi = {10.3390/su12198212},
    issn = {2071-1050},
    journal = {Sustainability},
    month = {October},
    number = {19},
    pages = {8212},
    publisher = {MDPI AG},
    title = {Agricultural Workforce Crisis in Light of the COVID-19 Pandemic},
    url = {http://dx.doi.org/10.3390/su12198212},
    volume = {12},
    year = {2020}
}

@article{Cielniak2020_Agricultural_Robots,
    author = {Ponnambalam, V.R. and Fentanes, J.P. and Das, G. and Cielniak, G. and Gjevestad, J.G.O. and From, P.J.},
    journal = {2020 6th International Conference on Control, Automation and Robotics, ICCAR 2020},
    pages = {214-220},
    title = {Agri-Cost-Maps - Integration of Environmental Constraints into Navigation Systems for Agricultural Robots},
    year = {2020}
}

@inproceedings{kokciyan2020argumentation_Generate_Domainspecific_Explanations,
    author = {K{\"o}kciyan, Nadin and Parsons, Simon and Sassoon, Isabel and Sklar, Elizabeth and Modgil, Sanjay},
    booktitle = {Multi-Agent Systems and Agreement Technologies: 17th European Conference, EUMAS 2020, and 7th International Conference, AT 2020, Thessaloniki, Greece, September 14-15, 2020, Revised Selected Papers 17},
    organization = {Springer International Publishing},
    pages = {319--337},
    title = {An argumentation-based approach to generate domain-specific explanations},
    year = {2020}
}

@inproceedings{balatsoukas2020wild_Wild_Pilot_Usability,
    author = {Balatsoukas, Panagiotis and Sassoon, Isabel and Chapman, Martin and Kokciyan, Nadin and Drake, Archie and Modgil, Sanjay and Ashworth, Mark and Curcin, Vasa and Sklar, Elizabeth and Parsons, Simon},
    booktitle = {2020 IEEE International Conference on Healthcare Informatics (ICHI)},
    organization = {IEEE},
    pages = {1--3},
    title = {In the wild pilot usability assessment of a connected health system for stroke self management},
    year = {2020}
}

@article{Hanheide2020_Dialogue_Interactivity_Development,
    author = {Baxter, P. and Del Duchetto, F. and Hanheide, M.},
    journal = {Advances in Intelligent Systems and Computing},
    pages = {147-160},
    title = {Engaging Learners in Dialogue Interactivity Development for Mobile Robots},
    volume = {946 AISC},
    year = {2020}
}

@article{Cielniak2020_Incorporating_Spatial_Constraints,
    author = {Khan, M.W. and Das, G.P. and Hanheide, M. and Cielniak, G.},
    journal = {IEEE International Conference on Intelligent Robots and Systems},
    pages = {2440-2445},
    title = {Incorporating spatial constraints into a bayesian tracking framework for improved localisation in agricultural environments},
    year = {2020}
}

@article{Lock_2020_Spatialised_Audio_Interface,
    author = {Lock, Jacobus C. and Gilchrist, Iain D. and Gilchrist, Iain D. and Cielniak, Grzegorz and Bellotto, Nicola},
    doi = {10.1145/3412325},
    issn = {1936-7236},
    journal = {ACM Transactions on Accessible Computing},
    month = {October},
    number = {4},
    pages = {1–21},
    publisher = {Association for Computing Machinery (ACM)},
    title = {Experimental Analysis of a Spatialised Audio Interface for People with Visual Impairments},
    url = {http://dx.doi.org/10.1145/3412325},
    volume = {13},
    year = {2020}
}

@article{Co_ar_2020_Enrichme_Perception,
    author = {Coşar, Serhan and Fernandez-Carmona, Manuel and Agrigoroaie, Roxana and Pages, Jordi and Ferland, François and Zhao, Feng and Yue, Shigang and Bellotto, Nicola and Tapus, Adriana},
    doi = {10.1007/s12369-019-00614-y},
    issn = {1875-4805},
    journal = {International Journal of Social Robotics},
    month = {February},
    number = {3},
    pages = {779–805},
    publisher = {Springer Science and Business Media LLC},
    title = {ENRICHME: Perception and Interaction of an Assistive Robot for the Elderly at Home},
    url = {http://dx.doi.org/10.1007/S12369-019-00614-Y},
    volume = {12},
    year = {2020}
}

@inproceedings{huang2020design_Automatic_Strawberry_Harvest,
    author = {Huang, Zhuoling and Sklar, Elizabeth and Parsons, Simon},
    booktitle = {Companion of the 2020 ACM/IEEE international conference on human-robot interaction},
    pages = {567--569},
    title = {Design of automatic strawberry harvest robot suitable in complex environments},
    year = {2020}
}

@inproceedings{Kulathunga_2020_Long_Range_Trajectory,
    author = {Kulathunga, Geesara and Fedorenko, Roman and Kopylov, Sergey and Klimehik, Alexandr},
    booktitle = {2020 5th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)},
    doi = {10.1109/acirs49895.2020.9162605},
    month = {July},
    pages = {145–153},
    publisher = {IEEE},
    title = {Real-Time Long Range Trajectory Replanning for MAVs in the Presence of Dynamic Obstacles},
    url = {http://dx.doi.org/10.1109/ACIRS49895.2020.9162605},
    year = {2020}
}

@article{Cielniak2020_Deep_Learning_Networks,
    author = {Kirk, R. and Cielniak, G. and Mangan, M.},
    journal = {Sensors (Switzerland)},
    number = {1},
    title = {L*a*b*Fruits: A rapid and robust outdoor fruit detection system combining bio-inspired features with one-stage deep learning networks},
    volume = {20},
    year = {2020}
}

@article{Rodias_2020_Circular_Economy_Framework,
    author = {Rodias, Efthymios and Aivazidou, Eirini and Achillas, Charisios and Aidonis, Dimitrios and Bochtis, Dionysis},
    doi = {10.3390/en14010159},
    issn = {1996-1073},
    journal = {Energies},
    month = {December},
    number = {1},
    pages = {159},
    publisher = {MDPI AG},
    title = {Water-Energy-Nutrients Synergies in the Agrifood Sector: A Circular Economy Framework},
    url = {http://dx.doi.org/10.3390/en14010159},
    volume = {14},
    year = {2020}
}

@article{Bochtis2020_Tree_Leaves_Identification,
    author = {Anagnostis, A. and Asiminari, G. and Papageorgiou, E. and Bochtis, D.},
    journal = {Applied Sciences (Switzerland)},
    number = {2},
    title = {A convolutional neural networks based method for anthracnose infected walnut tree leaves identification},
    volume = {10},
    year = {2020}
}

@article{Cielniak2020_Autonomous_Robotic_Harvesting,
    author = {Montes, H.A. and Le Louedec, J. and Cielniak, G. and Duckett, T.},
    journal = {IEEE International Conference on Intelligent Robots and Systems},
    pages = {10483-10488},
    title = {Real-time detection of broccoli crops in 3D point clouds for autonomous robotic harvesting},
    year = {2020}
}

@article{Hanheide2020_Inverse_Dynamics_Learning,
    author = {Shaj, V. and Becker, P. and B{\"u}chler, D. and Pandya, H. and van Duijkeren, N. and Taylor, C.J. and Hanheide, M. and Neumann, G.},
    journal = {Proceedings of Machine Learning Research},
    pages = {765-781},
    title = {Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning},
    volume = {155},
    year = {2020}
}

@inbook{ba53e387bceb4bcb8fcf199f23cda804_Automatic_Strawberry_Harvest,
    abstract = {Strawberries are an important cash crop that are grown worldwide. They are also a labour-intensive crop, with harvesting a particularly labour-intensive task because the fruit needs careful handling. This project investigates collaborative human-robot strawberry harvesting, where interacting with a human potentially increases the adaptability of a robot to work in more complex environments. The project mainly concentrates on two aspects of the problem: the identification of the fruit and the picking of the fruit.},
    author = {Zhuoling Huang and Elizabeth Sklar and Simon Parsons},
    booktitle = {HRI 2020 - Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction},
    day = {23},
    doi = {10.1145/3371382.3377443},
    keywords = {Agricultural robot, Human robot interaction, Learning from human},
    language = {English},
    month = {March},
    note = {15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020 ; Conference date: 23-03-2020 Through 26-03-2020},
    pages = {567--569},
    publisher = {IEEE Computer Society},
    series = {ACM/IEEE International Conference on Human-Robot Interaction},
    title = {Design of automatic strawberry harvest robot suitable in complex environments},
    year = {2020}
}

Strawberries are an important cash crop that are grown worldwide. They are also a labour-intensive crop, with harvesting a particularly labour-intensive task because the fruit needs careful handling. This project investigates collaborative human-robot strawberry harvesting, where interacting with a human potentially increases the adaptability of a robot to work in more complex environments. The project mainly concentrates on two aspects of the problem: the identification of the fruit and the picking of the fruit.

@article{Hanheide2020_Interactive_Movement_Primitives,
    author = {Mghames, S. and Hanheide, M. and Amir Ghalamzan, E.},
    journal = {arXiv},
    title = {Planning Actions by Interactive Movement Primitives: pushing occluding pieces to pick a ripe fruit},
    year = {2020}
}

@article{Cielniak2020_Flow_Forecasting_Models,
    author = {Vintr, T. and Yan, Z. and Eyisoy, K. and Kubis, F. and Blaha, J. and Ulrich, J. and Swaminathan, C.S. and Molina, S. and Kucner, T.P. and Magnusson, M. and Cielniak, G. and Faigl, J. and Duckett, T. and Lilienthal, A.J. and Krajnik, T.},
    journal = {IEEE International Conference on Intelligent Robots and Systems},
    pages = {11197-11204},
    title = {Natural criteria for comparison of pedestrian flow forecasting models},
    year = {2020}
}

@article{Polvara2020_Simtoreal_Quadrotor_Landing,
    author = {Polvara, R. and Patacchiola, M. and Hanheide, M. and Neumann, G.},
    journal = {Robotics},
    number = {1},
    title = {Sim-to-Real quadrotor landing via sequential deep Q-Networks and domain randomization},
    volume = {9},
    year = {2020}
}

@article{Hanheide2020_Incorporating_Spatial_Constraints,
    author = {Khan, M.W. and Das, G.P. and Hanheide, M. and Cielniak, G.},
    journal = {IEEE International Conference on Intelligent Robots and Systems},
    pages = {2440-2445},
    title = {Incorporating spatial constraints into a bayesian tracking framework for improved localisation in agricultural environments},
    year = {2020}
}

@article{Wu_2020_Fleet_Coordination_System,
    author = {Wu, Caicong and Chen, Zhibo and Wang, Dongxu and Song, Bingbing and Liang, Yajie and Yang, Lili and Bochtis, Dionysis D.},
    doi = {10.3390/en13040775},
    issn = {1996-1073},
    journal = {Energies},
    month = {February},
    number = {4},
    pages = {775},
    publisher = {MDPI AG},
    title = {A Cloud-Based In-Field Fleet Coordination System for Multiple Operations},
    url = {http://dx.doi.org/10.3390/en13040775},
    volume = {13},
    year = {2020}
}

@inproceedings{huang2020toward_Intelligent_Farming,
    author = {Huang, Zhuoling and Miyauchi, Genki and Salazar Gomez, Adrian and Bird, Richie and Kalsi, Amar Singh and Jansen, Chipp and Liu, Zeyang and Parsons, Simon and Sklar, Elizabeth},
    booktitle = {Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction},
    pages = {263--265},
    title = {Toward Robot Co-Labourers for Intelligent Farming},
    year = {2020}
}

@article{Hanheide2020_Continuous_Engagement_Assessment,
    author = {Del Duchetto, F. and Baxter, P. and Hanheide, M.},
    journal = {Frontiers in Robotics and AI},
    title = {Are You Still With Me? Continuous Engagement Assessment From a Robot{'}s Point of View},
    volume = {7},
    year = {2020}
}

@article{Anagnostis_2020_Gas_Consumption_Forecasting,
    author = {Anagnostis, Athanasios and Papageorgiou, Elpiniki and Bochtis, Dionysis},
    doi = {10.3390/su12166409},
    issn = {2071-1050},
    journal = {Sustainability},
    month = {August},
    number = {16},
    pages = {6409},
    publisher = {MDPI AG},
    title = {Application of Artificial Neural Networks for Natural Gas Consumption Forecasting},
    url = {http://dx.doi.org/10.3390/su12166409},
    volume = {12},
    year = {2020}
}

@article{Osborn_2020_Sensory_Stimulation_Enhances,
    author = {Luke E Osborn and Keqin Ding and Mark A Hays and Rohit Bose and Mark M Iskarous and Andrei Dragomir and Zied Tayeb and György M L{\`{e}}vay and Christopher L Hunt and Gordon Cheng and Robert S Armiger and Anastasios Bezerianos and Matthew S Fifer and Nitish V Thakor},
    doi = {10.1101/2020.05.22.20109330},
    month = {may},
    publisher = {Cold Spring Harbor Laboratory},
    title = {Sensory stimulation enhances phantom limb perception and movement decoding},
    url = {https://doi.org/10.1101%2F2020.05.22.20109330},
    year = {2020}
}

@article{Banias_2020_Central_Macedonia_Greece,
    author = {Banias, Georgios and Batsioula, Maria and Achillas, Charisios and Patsios, Sotiris I. and Kontogiannopoulos, Konstantinos N. and Bochtis, Dionysis and Moussiopoulos, Nicolas},
    doi = {10.3390/su12198221},
    issn = {2071-1050},
    journal = {Sustainability},
    month = {October},
    number = {19},
    pages = {8221},
    publisher = {MDPI AG},
    title = {A Life Cycle Analysis Approach for the Evaluation of Municipal Solid Waste Management Practices: The Case Study of the Region of Central Macedonia, Greece},
    url = {http://dx.doi.org/10.3390/su12198221},
    volume = {12},
    year = {2020}
}

@article{Hanheide2020_Grasping_Collisionfree_Manipulation,
    author = {Parsa, S. and Kamale, D. and Mghames, S. and Nazari, K. and Pardi, T. and Srinivasan, A.R. and Neumann, G. and Hanheide, M. and Amir, G.E.},
    journal = {IEEE International Conference on Automation Science and Engineering},
    pages = {1552-1557},
    title = {Haptic-guided shared control grasping: Collision-free manipulation},
    volume = {2020-August},
    year = {2020}
}

@article{Polvara2020_Rfid_Tags_Discovery,
    author = {Polvara, R. and Fernandez-Carmona, M. and Neumann, G. and Neumann, G. and Neumann, G. and Hanheide, M.},
    journal = {IEEE Robotics and Automation Letters},
    number = {3},
    pages = {4477-4484},
    title = {Next-Best-Sense: A Multi-Criteria Robotic Exploration Strategy for RFID Tags Discovery},
    volume = {5},
    year = {2020}
}

@article{Bochtis2020_Metric_Map_Generation,
    author = {Zhou, K. and Jensen, A.L. and Bochtis, D. and N?rremark, M. and Kateris, D. and S?rensen, C.G.},
    journal = {Agronomy},
    number = {1},
    title = {Metric map generation for autonomous field operations},
    volume = {10},
    year = {2020}
}

@article{Benos_2020_Mechanized_Operations,
    author = {Benos, Lefteris and Tsaopoulos, Dimitrios and Bochtis, Dionysis},
    doi = {10.3390/app10103484},
    issn = {2076-3417},
    journal = {Applied Sciences},
    month = {May},
    number = {10},
    pages = {3484},
    publisher = {MDPI AG},
    title = {A Review on Ergonomics in Agriculture. Part II: Mechanized Operations},
    url = {http://dx.doi.org/10.3390/app10103484},
    volume = {10},
    year = {2020}
}

@article{Bosilj_2020_Estimating_Soil_Aggregate,
    author = {Petra Bosilj and Iain Gould and Tom Duckett and Grzegorz Cielniak},
    doi = {10.1016/j.biosystemseng.2020.07.012},
    journal = {Biosystems Engineering},
    month = {oct},
    pages = {63--77},
    publisher = {Elsevier {BV}},
    title = {Estimating soil aggregate size distribution from images using pattern spectra},
    url = {https://doi.org/10.1016%2Fj.biosystemseng.2020.07.012},
    volume = {198},
    year = {2020}
}

@article{Ravichandar_2020_Learning_From_Demonstration,
    author = {Ravichandar, Harish and Polydoros, Athanasios S. and Chernova, Sonia and Billard, Aude},
    doi = {10.1146/annurev-control-100819-063206},
    issn = {2573-5144},
    journal = {Annual Review of Control, Robotics, and Autonomous Systems},
    month = {May},
    number = {1},
    pages = {297–330},
    publisher = {Annual Reviews},
    title = {Recent Advances in Robot Learning from Demonstration},
    url = {http://dx.doi.org/10.1146/annurev-control-100819-063206},
    volume = {3},
    year = {2020}
}

@inbook{7ce5fbb2932a4a65937ef39993212f88_Wild_Pilot_Usability,
    abstract = {This paper reports on the findings of a pilot study for the formative {"}in the wild{"}assessment of the usability of CONSULT, a research-led connected health system for stroke self-management and prevention. CONSULT integrates data from commercial wellness sensors, electronic health records and clinical guidelines and enables users to monitor their vital signs to support self-monitoring and provision of tailored advice. The CONSULT system includes a dashboard and a chatbot. To assess the usability of our system, six volunteers were recruited to interact with CONSULT over a period of seven days. System logs confirmed that participants interacted with the CONSULT system throughout. CONSULT's ability to integrate data from different sensors was an aspect of this systems that all participants liked and kept them motivated to track their vital signs. The study also revealed several usability issues that designers of this type of systems should consider. Some of the most prevalent issues were: information overload, data misinterpretation, need for more anthropomorphic conversational capabilities for the chatbot; lack of visibility of the data transmission status. This paper concludes with reflections on the importance of these findings when assessing the usability of connected health systems, like CONSULT.},
    author = {Panagiotis Balatsoukas and Isabel Sassoon and Martin Chapman and Nadin Kokciyan and Archie Drake and Sanjay Modgil and Mark Ashworth and Vasa Curcin and Elizabeth Sklar and Simon Parsons},
    booktitle = {2020 IEEE International Conference on Healthcare Informatics, ICHI 2020},
    doi = {10.1109/ICHI48887.2020.9374338},
    keywords = {connected health, human factors, usability},
    language = {English},
    month = {November},
    note = {Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 8th IEEE International Conference on Healthcare Informatics, ICHI 2020 ; Conference date: 30-11-2020 Through 03-12-2020},
    publisher = {Institute of Electrical and Electronics Engineers Inc.},
    series = {2020 IEEE International Conference on Healthcare Informatics, ICHI 2020},
    title = {In the wild pilot usability assessment of a connected health system for stroke self management},
    year = {2020}
}

This paper reports on the findings of a pilot study for the formative "in the wild"assessment of the usability of CONSULT, a research-led connected health system for stroke self-management and prevention. CONSULT integrates data from commercial wellness sensors, electronic health records and clinical guidelines and enables users to monitor their vital signs to support self-monitoring and provision of tailored advice. The CONSULT system includes a dashboard and a chatbot. To assess the usability of our system, six volunteers were recruited to interact with CONSULT over a period of seven days. System logs confirmed that participants interacted with the CONSULT system throughout. CONSULT's ability to integrate data from different sensors was an aspect of this systems that all participants liked and kept them motivated to track their vital signs. The study also revealed several usability issues that designers of this type of systems should consider. Some of the most prevalent issues were: information overload, data misinterpretation, need for more anthropomorphic conversational capabilities for the chatbot; lack of visibility of the data transmission status. This paper concludes with reflections on the importance of these findings when assessing the usability of connected health systems, like CONSULT.

@inbook{Akhmetzyanov_2020_Deep_Reinforcement_Learning,
    author = {Akhmetzyanov, Aydar and Yagfarov, Rauf and Gafurov, Salimzhan and Ostanin, Mikhail and Klimchik, Alexandr},
    booktitle = {Human Interaction, Emerging Technologies and Future Applications II},
    doi = {10.1007/978-3-030-44267-5_25},
    isbn = {9783030442675},
    issn = {2194-5365},
    pages = {168–174},
    publisher = {Springer International Publishing},
    title = {Continuous Control in Deep Reinforcement Learning with Direct Policy Derivation from Q Network},
    url = {http://dx.doi.org/10.1007/978-3-030-44267-5_25},
    year = {2020}
}

@article{Bochtis2020_Rural_Electrification_Investments,
    author = {Kyriakarakos, G. and Balafoutis, A.T. and Bochtis, D.},
    journal = {Sustainability (Switzerland)},
    number = {8},
    title = {Proposing a paradigm shift in rural electrification investments in Sub-Saharan Africa through Agriculture},
    volume = {12},
    year = {2020}
}

@article{Hanheide2020_Taxonomy_Of_Trustrelevant,
    author = {Tolmeijer, S. and Weiss, A. and Hanheide, M. and Lindner, F. and Powers, T.M. and Dixon, C. and Tielman, M.L.},
    journal = {ACM/IEEE International Conference on Human-Robot Interaction},
    pages = {3-12},
    title = {Taxonomy of trust-relevant failures and mitigation strategies},
    year = {2020}
}

@article{11577_3454981_Rgbd_Video_Sequences,
    author = {Coppola, C. and Cosar, S. and Faria, D. R. and Bellotto, N.},
    doi = {10.1007/s12369-019-00541-y},
    journal = {INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS},
    number = {1},
    pages = {201--215},
    title = {Social Activity Recognition on Continuous RGB-D Video Sequences},
    url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065185036&doi=10.1007/s12369-019-00541-y&partnerID=40&md5=232dcfd3074f5a713c6b30bcaa4effff},
    volume = {12},
    year = {2020}
}

@article{Das2020_Robust_Robot_Navigation,
    author = {Binch, A. and Das, G.P. and Pulido Fentanes, J. and Hanheide, M.},
    journal = {Proceedings - IEEE International Conference on Robotics and Automation},
    pages = {3937-3943},
    title = {Context Dependant Iterative Parameter Optimisation for Robust Robot Navigation},
    year = {2020}
}

@article{Hanheide2020_Gripper_Workspace_Spheres,
    author = {Sorour, M. and Elgeneidy, K. and Hanheide, M. and Abdalmjed, M. and Srinivasan, A. and Neumann, G.},
    journal = {Proceedings - IEEE International Conference on Robotics and Automation},
    pages = {1539-1545},
    title = {Enhancing Grasp Pose Computation in Gripper Workspace Spheres},
    year = {2020}
}

@inproceedings{Onoufriou_2020_Time_Series_Forecasting,
    author = {George Onoufriou and   and Marc Hanheide and Georgios Leontidis and},
    booktitle = {{UKRAS}20 Conference: {\textquotedblleft}Robots into the real world{\textquotedblright} Proceedings},
    doi = {10.31256/qm1fu7l},
    month = {may},
    publisher = {{EPSRC} {UK}-{RAS} Network},
    title = {The Augmented Agronomist Pipeline and Time Series Forecasting},
    url = {https://doi.org/10.31256%2Fqm1fu7l},
    year = {2020}
}

@inproceedings{Specht_2020_Emg_Signals_Decoding,
    author = {Bernhard Specht and Zied Tayeb and Emannual Dean and Rahil Soroushmojdehi and Gordon Cheng},
    booktitle = {2020 {IEEE} International Conference on Robotics and Automation ({ICRA})},
    doi = {10.1109/icra40945.2020.9197550},
    month = {may},
    publisher = {{IEEE}},
    title = {Real-Time Robot Reach-To-Grasp Movements Control Via {EOG} and {EMG} Signals Decoding},
    url = {https://doi.org/10.1109%2Ficra40945.2020.9197550},
    year = {2020}
}

@article{11577_3455205.2_Robot_Thermal_Camera,
    abstract = {Human re-identification is an important feature of domestic service robots, in particular for elderly monitoring and assistance, because it allows them to perform personalized tasks and human-robot interactions. However vision-based re- identification systems are subject to limitations due to human pose and poor lighting conditions. This paper presents a new re-identification method for service robots using thermal images. In robotic applications, as the number and size of thermal datasets is limited, it is hard to use approaches that require huge amount of training samples. We propose a re-identification system that can work using only a small amount of data. During training, we perform entropy-based sampling to obtain a thermal dictionary for each person. Then, a symbolic representation is produced by converting each video into sequences of dictionary elements. Finally, we train a classifier using this symbolic representation and geometric distribution within the new representation domain. The experiments are performed on a new thermal dataset for human re-identification, which includes various situations of human motion, poses and occlusion, and which is made publicly available for research purposes. The proposed approach has been tested on this dataset and its improvements over standard approaches have been demonstrated.},
    author = {Cosar, S. and Bellotto, N.},
    doi = {10.1007/s10846-019-01026-w},
    journal = {JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS},
    keywords = {Service robots; Re-identification; Elderly care; Thermal camera; Occlusion; Body motion},
    number = {1},
    pages = {85--102},
    publisher = {SPRINGER},
    title = {Human Re-Identification with a Robot Thermal Camera Using Entropy-Based Sampling},
    volume = {98},
    year = {2020}
}

Human re-identification is an important feature of domestic service robots, in particular for elderly monitoring and assistance, because it allows them to perform personalized tasks and human-robot interactions. However vision-based re- identification systems are subject to limitations due to human pose and poor lighting conditions. This paper presents a new re-identification method for service robots using thermal images. In robotic applications, as the number and size of thermal datasets is limited, it is hard to use approaches that require huge amount of training samples. We propose a re-identification system that can work using only a small amount of data. During training, we perform entropy-based sampling to obtain a thermal dictionary for each person. Then, a symbolic representation is produced by converting each video into sequences of dictionary elements. Finally, we train a classifier using this symbolic representation and geometric distribution within the new representation domain. The experiments are performed on a new thermal dataset for human re-identification, which includes various situations of human motion, poses and occlusion, and which is made publicly available for research purposes. The proposed approach has been tested on this dataset and its improvements over standard approaches have been demonstrated.

@article{Martindale_2020_United_Kingdom_Food,
    author = {Martindale, Wayne and Wright, Isobel and Korir, Lilian and Opiyo, Arnold M. and Karanja, Benard and Nyalala, Samuel and Kumar, Mahesh and Pearson, Simon and Swainson, Mark},
    doi = {10.1108/eor-06-2023-0005},
    issn = {2631-3952},
    journal = {Emerald Open Research},
    month = {April},
    number = {6},
    publisher = {Emerald},
    title = {Framing food security and food loss statistics for incisive supply chain improvement and knowledge transfer between Kenyan, Indian and United Kingdom food manufacturers},
    url = {http://dx.doi.org/10.1108/eor-06-2023-0005},
    volume = {1},
    year = {2020}
}

@article{Hanheide2020_Simtoreal_Quadrotor_Landing,
    author = {Polvara, R. and Patacchiola, M. and Hanheide, M. and Neumann, G.},
    journal = {Robotics},
    number = {1},
    title = {Sim-to-Real quadrotor landing via sequential deep Q-Networks and domain randomization},
    volume = {9},
    year = {2020}
}

@inbook{11577_3454982_Continuous_Game_Theory,
    abstract = {Autonomous Vehicles (AVs) must interact with other road users. They must understand and adapt to complex pedestrian behaviour, especially during crossings where priority is not clearly defined. This includes feedback effects such as modelling a pedestrian’s likely behaviours resulting from changes in the AVs behaviour. For example, whether a pedestrian will yield if the AV accelerates, and vice versa. To enable such automated interactions, it is necessary for the AV to possess a statistical model of the pedestrian’s responses to its own actions. A previous work demonstrated a proof-ofconcept method to fit parameters to a simplified model based on data from a highly artificial discrete laboratory task with human subjects. The method was based on LIDAR-based person tracking, game theory, and Gaussian process analysis. The present study extends this method to enable analysis of more realistic continuous human experimental data. It shows for the first time how game-theoretic predictive parameters can be fit into pedestrians natural and continuous motion during road-crossings, and how predictions can be made about their interactions with AV controllers in similar real-world settings.},
    author = {Camara, F. and Cosar, S. and Bellotto, N. and Merat, N. and Fox, C. W.},
    booktitle = {Human Factors in Intelligent Vehicles},
    pages = {1--20},
    publisher = {River Publishers},
    title = {Continuous game theory pedestrian modelling method for autonomous vehicles},
    year = {2020}
}

Autonomous Vehicles (AVs) must interact with other road users. They must understand and adapt to complex pedestrian behaviour, especially during crossings where priority is not clearly defined. This includes feedback effects such as modelling a pedestrian’s likely behaviours resulting from changes in the AVs behaviour. For example, whether a pedestrian will yield if the AV accelerates, and vice versa. To enable such automated interactions, it is necessary for the AV to possess a statistical model of the pedestrian’s responses to its own actions. A previous work demonstrated a proof-ofconcept method to fit parameters to a simplified model based on data from a highly artificial discrete laboratory task with human subjects. The method was based on LIDAR-based person tracking, game theory, and Gaussian process analysis. The present study extends this method to enable analysis of more realistic continuous human experimental data. It shows for the first time how game-theoretic predictive parameters can be fit into pedestrians natural and continuous motion during road-crossings, and how predictions can be made about their interactions with AV controllers in similar real-world settings.

@article{Lampridi_2020_Mechanized_Agricultural_Operations,
    author = {Lampridi, Maria and Kateris, Dimitrios and Sørensen, Claus Grøn and Bochtis, Dionysis},
    doi = {10.3390/en13030769},
    issn = {1996-1073},
    journal = {Energies},
    month = {February},
    number = {3},
    pages = {769},
    publisher = {MDPI AG},
    title = {Energy Footprint of Mechanized Agricultural Operations},
    url = {http://dx.doi.org/10.3390/en13030769},
    volume = {13},
    year = {2020}
}

@article{Hanheide2020_Robust_Robot_Navigation,
    author = {Binch, A. and Das, G.P. and Pulido Fentanes, J. and Hanheide, M.},
    journal = {Proceedings - IEEE International Conference on Robotics and Automation},
    pages = {3937-3943},
    title = {Context Dependant Iterative Parameter Optimisation for Robust Robot Navigation},
    year = {2020}
}

@article{Achillas_2020_Circular_Business_Models,
    author = {Achillas, Charisios and Bochtis, Dionysis},
    doi = {10.3390/su122310142},
    issn = {2071-1050},
    journal = {Sustainability},
    month = {December},
    number = {23},
    pages = {10142},
    publisher = {MDPI AG},
    title = {Toward a Green, Closed-Loop, Circular Bioeconomy: Boosting the Performance Efficiency of Circular Business Models},
    url = {http://dx.doi.org/10.3390/su122310142},
    volume = {12},
    year = {2020}
}

@article{Ostanin_2020_Multi_Robots_Interactive,
    author = {M. Ostanin and R. Yagfarov and D. Devitt and A. Akhmetzyanov and A. Klimchik},
    doi = {10.1080/00207543.2020.1834640},
    journal = {International Journal of Production Research},
    month = {nov},
    pages = {1--13},
    publisher = {Informa {UK} Limited},
    title = {Multi robots interactive control using mixed reality},
    url = {https://doi.org/10.1080%2F00207543.2020.1834640},
    year = {2020}
}

@article{Bosilj_2020_Estimating_Soil_Aggregate_Estimating_Soil_Aggregate,
    author = {Bosilj, Petra and Gould, Iain and Duckett, Tom and Cielniak, Grzegorz},
    doi = {10.1016/j.biosystemseng.2020.07.012},
    issn = {1537-5110},
    journal = {Biosystems Engineering},
    month = {October},
    pages = {63–77},
    publisher = {Elsevier BV},
    title = {Estimating soil aggregate size distribution from images using pattern spectra},
    url = {http://dx.doi.org/10.1016/j.biosystemseng.2020.07.012},
    volume = {198},
    year = {2020}
}

@article{Zhou_2020_Field_Operations_Efficiency,
    author = {Zhou, Kun and Bochtis, Dionysis and Jensen, Allan Leck and Kateris, Dimitrios and Sørensen, Claus Grøn},
    doi = {10.3390/app10010329},
    issn = {2076-3417},
    journal = {Applied Sciences},
    month = {January},
    number = {1},
    pages = {329},
    publisher = {MDPI AG},
    title = {Introduction of a New Index of Field Operations Efficiency},
    url = {http://dx.doi.org/10.3390/app10010329},
    volume = {10},
    year = {2020}
}

@article{Hanheide2020_Abstract_Visual_Programming,
    author = {Brown, O. and Roberts-Elliott, L. and Del Duchetto, F. and Hanheide, M. and Baxter, P.},
    journal = {ACM/IEEE International Conference on Human-Robot Interaction},
    pages = {154-156},
    title = {Abstract visual programming of social robots for novice users},
    year = {2020}
}

@article{sassoon2020implementing_Argument_And_Explanation,
    author = {SASSOON, Isabel and KOKCIYAN, Nadin and CHAPMAN, Martin and SKLAR, Elizabeth and CURCIN, Vasa and MODGIL, Sanjay and PARSONS, Simon},
    title = {Implementing Argument and Explanation},
    year = {2020}
}

@article{Osborn_2020_Sensory_Stimulation_Enhances_Sensory_Stimulation_Enhances,
    author = {Osborn, Luke E and Ding, Keqin and Hays, Mark A and Bose, Rohit and Iskarous, Mark M and Dragomir, Andrei and Tayeb, Zied and Lévay, György M and Hunt, Christopher L and Cheng, Gordon and Armiger, Robert S and Bezerianos, Anastasios and Fifer, Matthew S and Thakor, Nitish V},
    doi = {10.1088/1741-2552/abb861},
    issn = {1741-2552},
    journal = {Journal of Neural Engineering},
    month = {October},
    number = {5},
    pages = {056006},
    publisher = {IOP Publishing},
    title = {Sensory stimulation enhances phantom limb perception and movement decoding},
    url = {http://dx.doi.org/10.1088/1741-2552/abb861},
    volume = {17},
    year = {2020}
}

@inproceedings{Zhao_2020_Planar_Rotation_Mechanism,
    author = {Zhao, Wanda and Pashkevich, Anatol and Klimchik, Alexandr and Chablat, D.},
    booktitle = {Volume 10: 44th Mechanisms and Robotics Conference (MR)},
    collection = {IDETC-CIE2020},
    doi = {10.1115/detc2020-22076},
    month = {August},
    publisher = {American Society of Mechanical Engineers},
    series = {IDETC-CIE2020},
    title = {The Stability and Stiffness Analysis of a Dual-Triangle Planar Rotation Mechanism},
    url = {http://dx.doi.org/10.1115/DETC2020-22076},
    year = {2020}
}

@inproceedings{Kulathunga_2020_Multirotor_Aerial_Vehicles,
    author = {Kulathunga, Geesara and Fedorenko, Roman and Klimchik, Alexandr},
    booktitle = {2020 International Conference on Unmanned Aircraft Systems (ICUAS)},
    doi = {10.1109/icuas48674.2020.9214019},
    month = {September},
    pages = {1213–1220},
    publisher = {IEEE},
    title = {Regions of Interest Segmentation from LiDAR Point Cloud for Multirotor Aerial Vehicles},
    url = {http://dx.doi.org/10.1109/ICUAS48674.2020.9214019},
    year = {2020}
}

@article{11577_3455194.4_Enrichme_Perception,
    abstract = {Recent technological advances enabled modern robots to become part of our daily life. In particular, assistive robotics emerged as an exciting research topic that can provide solutions to improve the quality of life of elderly and vulnerable people. This paper introduces the robotic platform developed in the ENRICHME project, with particular focus on its innovative perception and interaction capabilities. The project's main goal is to enrich the day-to-day experience of elderly people at home with technologies that enable health monitoring, complementary care, and social support. The paper presents several modules created to provide cognitive stimulation services for elderly users with mild cognitive impairments. The ENRICHME robot was tested in three pilot sites around Europe (Poland, Greece, and UK) and proven to be an effective assistant for the elderly at home.},
    author = {Cosar, Serhan and Fernandez-Carmona, Manuel and Agrigoroaie, Roxana and Pages, Jordi and Ferland, Francois and Zhao, Feng and Yue, Shigang and Bellotto, Nicola and Tapus, Adriana},
    doi = {10.1007/s12369-019-00614-y},
    journal = {INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS},
    keywords = {Assistive robotics; Robot perception; Human-robot interaction},
    number = {3},
    pages = {779--805},
    publisher = {SPRINGER},
    title = {ENRICHME: Perception and Interaction of an Assistive Robot for the Elderly at Home},
    volume = {12},
    year = {2020}
}

Recent technological advances enabled modern robots to become part of our daily life. In particular, assistive robotics emerged as an exciting research topic that can provide solutions to improve the quality of life of elderly and vulnerable people. This paper introduces the robotic platform developed in the ENRICHME project, with particular focus on its innovative perception and interaction capabilities. The project's main goal is to enrich the day-to-day experience of elderly people at home with technologies that enable health monitoring, complementary care, and social support. The paper presents several modules created to provide cognitive stimulation services for elderly users with mild cognitive impairments. The ENRICHME robot was tested in three pilot sites around Europe (Poland, Greece, and UK) and proven to be an effective assistant for the elderly at home.

@article{Das2020_Agricultural_Robots,
    author = {Ponnambalam, V.R. and Fentanes, J.P. and Das, G. and Cielniak, G. and Gjevestad, J.G.O. and From, P.J.},
    journal = {2020 6th International Conference on Control, Automation and Robotics, ICCAR 2020},
    pages = {214-220},
    title = {Agri-Cost-Maps - Integration of Environmental Constraints into Navigation Systems for Agricultural Robots},
    year = {2020}
}

@article{Moysiadis_2020_Planning_Aspects,
    author = {Moysiadis, Vasileios and Tsolakis, Naoum and Katikaridis, Dimitris and Sørensen, Claus G. and Pearson, Simon and Bochtis, Dionysis},
    doi = {10.3390/app10103453},
    issn = {2076-3417},
    journal = {Applied Sciences},
    month = {May},
    number = {10},
    pages = {3453},
    publisher = {MDPI AG},
    title = {Mobile Robotics in Agricultural Operations: A Narrative Review on Planning Aspects},
    url = {http://dx.doi.org/10.3390/app10103453},
    volume = {10},
    year = {2020}
}

@article{Al-khafajiy2020_Trust_Management_Approach,
    author = {Al-khafajiy, M. and Baker, T. and Asim, M. and Guo, Z. and Ranjan, R. and Longo, A. and Puthal, D. and Taylor, M.},
    journal = {Journal of Parallel and Distributed Computing},
    pages = {1-16},
    title = {COMITMENT: A Fog Computing Trust Management Approach},
    volume = {137},
    year = {2020}
}

@inproceedings{huang2020experiment_Simulated_Agricultural_Task,
    author = {Huang, Zhuoling and Miyauchi, Genki and Gomez, Adrian Salazar and Bird, Richie and Kalsi, Amar Singh and Jansen, Chipp and Liu, Zeyang and Parsons, Simon and Sklar, Elizabeth},
    booktitle = {Towards Autonomous Robotic Systems: 21st Annual Conference, TAROS 2020, Nottingham, UK, September 16, 2020, Proceedings 21},
    organization = {Springer International Publishing},
    pages = {221--233},
    title = {An experiment on human-robot interaction in a simulated agricultural task},
    year = {2020}
}

@article{Angelopoulou_2020_Proximal_Sensing_Spectroscopy,
    author = {Angelopoulou, Theodora and Balafoutis, Athanasios and Zalidis, George and Bochtis, Dionysis},
    doi = {10.3390/su12020443},
    issn = {2071-1050},
    journal = {Sustainability},
    month = {January},
    number = {2},
    pages = {443},
    publisher = {MDPI AG},
    title = {From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review},
    url = {http://dx.doi.org/10.3390/su12020443},
    volume = {12},
    year = {2020}
}

@article{Pearson_2019, title={Are Distributed Ledger Technologies the panacea for food traceability?}, volume={20}, ISSN={2211-9124}, url={http://dx.doi.org/10.1016/j.gfs.2019.02.002}, DOI={10.1016/j.gfs.2019.02.002}, journal={Global Food Security}, publisher={Elsevier BV}, author={Pearson, Simon and May, David and Leontidis, Georgios and Swainson, Mark and Brewer, Steve and Bidaut, Luc and Frey, Jeremy G. and Parr, Gerard and Maull, Roger and Zisman, Andrea}, year={2019}, month=mar, pages={145–149} }

@article{Hanheide2019_Gripper_Workspace_Spheres,
    author = {Sorour, M. and Elgeneidy, K. and Srinivasan, A. and Hanheide, M. and Neumann, G.},
    journal = {IEEE International Conference on Intelligent Robots and Systems},
    pages = {1541-1547},
    title = {Grasping Unknown Objects Based on Gripper Workspace Spheres},
    year = {2019}
}

@article{Al-khafajiy2019_Internetofthings_Application,
    author = {Maamar, Z. and Baker, T. and Faci, N. and Al-Khafajiy, M. and Ugljanin, E. and Atif, Y. and Sellami, M.},
    journal = {Cognitive Systems Research},
    pages = {233-245},
    title = {Weaving cognition into the internet-of-things: Application to water leaks},
    volume = {56},
    year = {2019}
}

@article{Bochtis2019_Robotics_And_Labour,
    author = {Marinoudi, V. and S?rensen, C.G. and Pearson, S. and Bochtis, D.},
    journal = {Biosystems Engineering},
    pages = {111-121},
    title = {Robotics and labour in agriculture. A context consideration},
    volume = {184},
    year = {2019}
}

@inbook{298d5b31334a4991b18d5ffc51652022_Managing_Chronic_Conditions,
    abstract = {This paper describes work to assess the feasibility of using a decision support tool to help patients with chronic conditions, in particular stroke, manage their condition in collaboration with their carers and the health care professionals who are looking after them. The system contains several novel elements, in particular: the integration of data from commercial wellness sensors, electronic health records and clinical guidelines; the use of computational argumentation to track the source of data and to resolve conflicts and make recommendations; and argumentation-based dialogue to support interaction with patients. The proposed approach is implemented as an application that can run on smart devices (e.g. tablets). The users have personalised dashboards where they can visualise their health data, and interact with a conversational chatbot providing further explanations about their overall well-being.},
    author = {Nadin Kokciyan and Martin Chapman and Panagiotis Balatsoukas and Isabel Sassoon and Kai Essers and Mark Ashworth and Vasa Curcin and Sanjay Modgil and Simon Parsons and Sklar, {Elizabeth I}},
    booktitle = {The 17th World Congress of Medical and Health Informatics},
    doi = {https://doi.org/10.3233/SHTI190302},
    language = {English},
    title = {A Collaborative Decision Support Tool for Managing Chronic Conditions},
    year = {2019}
}

This paper describes work to assess the feasibility of using a decision support tool to help patients with chronic conditions, in particular stroke, manage their condition in collaboration with their carers and the health care professionals who are looking after them. The system contains several novel elements, in particular: the integration of data from commercial wellness sensors, electronic health records and clinical guidelines; the use of computational argumentation to track the source of data and to resolve conflicts and make recommendations; and argumentation-based dialogue to support interaction with patients. The proposed approach is implemented as an application that can run on smart devices (e.g. tablets). The users have personalised dashboards where they can visualise their health data, and interact with a conversational chatbot providing further explanations about their overall well-being.

@article{Cielniak2019_Active_Object_Search,
    author = {Lock, J.C. and Cielniak, G. and Bellotto, N.},
    journal = {VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications},
    pages = {476-485},
    title = {Active object search with a mobile device for people with visual impairments},
    volume = {4},
    year = {2019}
}

@article{Bochtis2019_Long_Shortterm_Memory,
    author = {Anagnostis, A. and Papageorgiou, E. and Dafopoulos, V. and Bochtis, D.},
    journal = {10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019},
    title = {Applying Long Short-Term Memory Networks for natural gas demand prediction},
    year = {2019}
}

@article{Lampridi_2019_Concepts_And_Methods,
    author = {Lampridi, Maria and Sørensen, Claus and Bochtis, Dionysis},
    doi = {10.3390/su11185120},
    issn = {2071-1050},
    journal = {Sustainability},
    month = {September},
    number = {18},
    pages = {5120},
    publisher = {MDPI AG},
    title = {Agricultural Sustainability: A Review of Concepts and Methods},
    url = {http://dx.doi.org/10.3390/su11185120},
    volume = {11},
    year = {2019}
}

@inproceedings{jansen2019co_Cocreative_Physical_Drawing,
    author = {Jansen, Chipp and Sklar, Elizabeth},
    booktitle = {ICRA-X Robots Art Program at IEEE International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada},
    pages = {2},
    title = {Co-creative physical drawing systems},
    year = {2019}
}

@inproceedings{Tayeb_2019_Emgcontrolled_Hand_Prostheses,
    author = {Zied Tayeb and Philipp Jakovleski and Zhong Chen and Jannick Lippert and Pablo Lanillos and Dongheui Lee and Gordon Cheng},
    booktitle = {2019 9th International {IEEE}/{EMBS} Conference on Neural Engineering ({NER})},
    doi = {10.1109/ner.2019.8717124},
    month = {mar},
    publisher = {{IEEE}},
    title = {Enabling the sense of touch in {EMG}-controlled hand prostheses using vibro-tactile stimulation},
    url = {https://doi.org/10.1109%2Fner.2019.8717124},
    year = {2019}
}

@article{Bochtis2019_Organic_Fertiliser_Application,
    author = {Rodias, E.C. and Sopegno, A. and Berruto, R. and Bochtis, D.D. and Cavallo, E. and Busato, P.},
    journal = {Biosystems Engineering},
    pages = {233-243},
    title = {A combined simulation and linear programming method for scheduling organic fertiliser application},
    volume = {178},
    year = {2019}
}

@inproceedings{chapman2019computational_Computational_Argumentationbased_Clinical,
    author = {Chapman, Martin and Balatsoukas, Panagiotis and Ashworth, Mark and Curcin, Vasa and K{\"o}kciyan, Nadin and Essers, Kai and Sassoon, Isabel and Modgil, Sanjay and Parsons, Simon and Sklar, Elizabeth I},
    booktitle = {Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems},
    pages = {2345--2347},
    title = {Computational argumentation-based clinical decision support},
    year = {2019}
}

@conference{11577_3498540_Activis_Mobile_Object,
    abstract = {The ActiVis project aims to deliver a mobile system that is able to guide a person with visual impairments towards a target object or area in an unknown indoor environment. For this, it uses new developments in object detection, mobile computing, action generation and human-computer interfacing to interpret the user's surroundings and present effective guidance directions. Our approach to direction generation uses a Partially Observable Markov Decision Process (POMDP) to track the system's state and output the optimal location to be investigated. This system includes an object detector and an audio-based guidance interface to provide a complete active search pipeline. The ActiVis system was evaluated in a set of experiments showing better performance than a simpler unguided case.},
    author = {Lock, J. C. and Tramontano, A. G. and Ghidoni, S. and Bellotto, N.},
    booktitle = {Proceedings of the 20th International Conference on Image Analysis and Processing (ICIAP)},
    doi = {10.1007/978-3-030-30645-8_59},
    isbn = {978-3-030-30644-1},
    keywords = {Active vision; Vision impairment; Object detection},
    pages = {649--660},
    publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},
    title = {ActiVis: Mobile object detection and active guidance for people with visual impairments},
    volume = {11752 LNCS},
    year = {2019}
}

The ActiVis project aims to deliver a mobile system that is able to guide a person with visual impairments towards a target object or area in an unknown indoor environment. For this, it uses new developments in object detection, mobile computing, action generation and human-computer interfacing to interpret the user's surroundings and present effective guidance directions. Our approach to direction generation uses a Partially Observable Markov Decision Process (POMDP) to track the system's state and output the optimal location to be investigated. This system includes an object detector and an audio-based guidance interface to provide a complete active search pipeline. The ActiVis system was evaluated in a set of experiments showing better performance than a simpler unguided case.

@article{Klimchik2019_Parallel_Robotic_Manipulators,
    author = {Klimchik, A. and Chablat, D. and Pashkevich, A.},
    journal = {CISM International Centre for Mechanical Sciences, Courses and Lectures},
    pages = {355-362},
    title = {Advancement of MSA-Technique for Stiffness Modeling of Serial and Parallel Robotic Manipulators},
    volume = {584},
    year = {2019}
}

@article{Cielniak2019_Boneconduction_Audio_Interface,
    author = {Lock, J.C. and Gilchrist, I.D. and Cielniak, G. and Bellotto, N.},
    journal = {Communications in Computer and Information Science},
    pages = {542-553},
    title = {Bone-conduction audio interface to guide people with visual impairments},
    volume = {1122 CCIS},
    year = {2019}
}

@article{Das2019_Biological_Goal_Seeking,
    author = {Kerr, E.P. and Vance, P. and Kerr, D. and Coleman, S.A. and Das, G.P. and McGinnity, T.M. and Moeys, D.P. and Delbr{\"u}ck, T.},
    journal = {Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018},
    pages = {1602-1607},
    title = {Biological Goal Seeking},
    year = {2019}
}

@article{Bochtis2019_Optimal_Energy_Performance,
    author = {Rodias, E.C. and Lampridi, M. and Sopegno, A. and Berruto, R. and Banias, G. and Bochtis, D.D. and Busato, P.},
    journal = {Biosystems Engineering},
    pages = {11-27},
    title = {Optimal energy performance on allocating energy crops},
    volume = {181},
    year = {2019}
}

@article{Bochtis2019_Emulation_Modelling_Approach,
    author = {Bechtsis, D. and Moisiadis, V. and Tsolakis, N. and Vlachos, D. and Bochtis, D.},
    journal = {Communications in Computer and Information Science},
    pages = {177-190},
    title = {Unmanned Ground Vehicles in Precision Farming Services: An Integrated Emulation Modelling Approach},
    volume = {953},
    year = {2019}
}

@article{Das2019_Machine_Learning_Approaches,
    author = {Das, G.P. and Vance, P.J. and Kerr, D. and Coleman, S.A. and McGinnity, T.M. and Liu, J.K.},
    journal = {Neurocomputing},
    pages = {101-112},
    title = {Computational modelling of salamander retinal ganglion cells using machine learning approaches},
    volume = {325},
    year = {2019}
}

@article{Bochtis2019_Intelligent_Data_Mining,
    author = {Pantazi, X.E. and Moshou, D. and Bochtis, D.},
    journal = {Intelligent Data Mining and Fusion Systems in Agriculture},
    pages = {1-330},
    title = {Intelligent data mining and fusion systems in agriculture},
    year = {2019}
}

@article{Al-khafajiy2019_Fog_Computing_Framework,
    author = {Al-Khafajiy, M. and Baker, T. and Al-Libawy, H. and Waraich, A. and Chalmers, C. and Alfandi, O.},
    journal = {Proceedings - International Conference on Developments in eSystems Engineering, DeSE},
    pages = {71-77},
    title = {Fog computing framework for internet of things applications},
    volume = {2018-September},
    year = {2019}
}

@incollection{kokciyan2019collaborative_Managing_Chronic_Conditions,
    author = {K{\"o}kciyan, Nadin and Chapman, Martin and Balatsoukas, Panagiotis and Sassoon, Isabel and Essers, Kai and Ashworth, Mark and Curcin, Vasa and Modgil, Sanjay and Parsons, Simon and Sklar, Elizabeth I},
    booktitle = {MEDINFO 2019: Health and Wellbeing e-Networks for All},
    pages = {644--648},
    publisher = {IOS Press},
    title = {A collaborative decision support tool for managing chronic conditions},
    year = {2019}
}

@article{Angelopoulou_2019_Soil_Organic_Carbon,
    author = {Angelopoulou, Theodora and Tziolas, Nikolaos and Balafoutis, Athanasios and Zalidis, George and Bochtis, Dionysis},
    doi = {10.3390/rs11060676},
    issn = {2072-4292},
    journal = {Remote Sensing},
    month = {March},
    number = {6},
    pages = {676},
    publisher = {MDPI AG},
    title = {Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review},
    url = {http://dx.doi.org/10.3390/rs11060676},
    volume = {11},
    year = {2019}
}

@article{Al-khafajiy2019_Remote_Health_Monitoring,
    author = {Al-khafajiy, M. and Baker, T. and Chalmers, C. and Asim, M. and Kolivand, H. and Fahim, M. and Waraich, A.},
    journal = {Multimedia Tools and Applications},
    number = {17},
    pages = {24681-24706},
    title = {Remote health monitoring of elderly through wearable sensors},
    volume = {78},
    year = {2019}
}

@article{Senft_2019_Teaching_Robots_Social,
    author = {Emmanuel Senft and S{\'{e}}verin Lemaignan and Paul E. Baxter and Madeleine Bartlett and Tony Belpaeme},
    doi = {10.1126/scirobotics.aat1186},
    journal = {Science Robotics},
    month = {oct},
    number = {35},
    publisher = {American Association for the Advancement of Science ({AAAS})},
    title = {Teaching robots social autonomy from in situ human guidance},
    url = {https://doi.org/10.1126%2Fscirobotics.aat1186},
    volume = {4},
    year = {2019}
}

@article{Tayeb_2019_Deep_Neural_Networks,
    author = {Tayeb, Zied and Fedjaev, Juri and Ghaboosi, Nejla and Richter, Christoph and Everding, Lukas and Qu, Xingwei and Wu, Yingyu and Cheng, Gordon and Conradt, Jörg},
    doi = {10.3390/s19010210},
    issn = {1424-8220},
    journal = {Sensors},
    month = {January},
    number = {1},
    pages = {210},
    publisher = {MDPI AG},
    title = {Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals},
    url = {http://dx.doi.org/10.3390/s19010210},
    volume = {19},
    year = {2019}
}

@article{Al-khafajiy2019_Enabling_High_Performance,
    author = {Al-Khafajiy, M. and Baker, T. and Waraich, A. and Alfandi, O. and Hussien, A.},
    journal = {Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA},
    title = {Enabling high performance fog computing through fog-2-fog coordination model},
    volume = {2019-November},
    year = {2019}
}

@article{Bochtis2019_Agricultural_Machinery_Fleets,
    author = {Tieppo, R.C. and Romanelli, T.L. and Milan, M. and S?rensen, C.A.G. and Bochtis, D.},
    journal = {Computers and Electronics in Agriculture},
    pages = {282-292},
    title = {Modeling cost and energy demand in agricultural machinery fleets for soybean and maize cultivated using a no-tillage system},
    volume = {156},
    year = {2019}
}

@article{Al_khafajiy_2019_Smart_Hospital_Emergency,
    author = {Mohammed Al-khafajiy and Hoshang Kolivand and Thar Baker and David Tully and Atif Waraich},
    doi = {10.1007/s11042-019-7274-4},
    journal = {Multimedia Tools and Applications},
    month = {feb},
    number = {14},
    pages = {20087--20111},
    publisher = {Springer Science and Business Media {LLC}},
    title = {Smart hospital emergency system},
    url = {https://doi.org/10.1007%2Fs11042-019-7274-4},
    volume = {78},
    year = {2019}
}

@article{Klimchik_2019_Matrix_Structural_Analysis,
    author = {Alexandr Klimchik and Anatol Pashkevich and Damien Chablat},
    doi = {10.1016/j.mechmachtheory.2018.11.023},
    journal = {Mechanism and Machine Theory},
    month = {mar},
    pages = {365--394},
    publisher = {Elsevier {BV}},
    title = {Fundamentals of manipulator stiffness modeling using matrix structural analysis},
    url = {https://doi.org/10.1016%2Fj.mechmachtheory.2018.11.023},
    volume = {133},
    year = {2019}
}

@inbook{18df5e4bfef747b1aa0132c5a62d0b85_Citizen_Support_Aggregation,
    abstract = {In the context of Digital Democracy, online participation platforms have emerged as innovative tools that enable citizens to participate in the decision making of their nation, region, or local government. Users can issue proposals and arguments in favour or against them and they can also support other people's arguments. This paper proposes two alternative support aggregation methods and applies them into debates conducted in the Decidim platform.},
    address = {Netherlands},
    author = {Marc Serramia and Jordi Ganzer and Maite Lopez-Sanchez and Rodr{\'i}guez-Aguilar, {Juan A.} and Natalia Criado and Simon Parsons and Patricio Escobar and Marc Fern{\'a}ndez},
    booktitle = {Artificial Intelligence Research and Development - Proceedings of the 22nd International Conference of the Catalan Association for Artificial Intelligence, CCIA 2019},
    day = {6},
    doi = {10.3233/FAIA190102},
    editor = {Jordi Sabater-Mir and Vicenc Torra and Isabel Aguilo and Manuel Gonzalez-Hidalgo},
    keywords = {AI application, Argumentation, Debate, E-governance, Information fusion, Participation platform},
    language = {English},
    month = {September},
    note = {22nd International Conference of the Catalan Association for Artificial Intelligence, CCIA 2019 ; Conference date: 23-10-2019 Through 25-10-2019},
    pages = {9--18},
    publisher = {IOS Press},
    series = {Frontiers in Artificial Intelligence and Applications},
    title = {Citizen support aggregation methods for participatory platforms},
    year = {2019}
}

In the context of Digital Democracy, online participation platforms have emerged as innovative tools that enable citizens to participate in the decision making of their nation, region, or local government. Users can issue proposals and arguments in favour or against them and they can also support other people's arguments. This paper proposes two alternative support aggregation methods and applies them into debates conducted in the Decidim platform.

@inproceedings{sassoon2019explainable_Explainable_Argumentation,
    author = {Sassoon, Isabel and K{\"o}kciyan, Nadin and Sklar, Elizabeth and Parsons, Simon},
    booktitle = {Explainable, Transparent Autonomous Agents and Multi-Agent Systems: First International Workshop, EXTRAAMAS 2019, Montreal, QC, Canada, May 13--14, 2019, Revised Selected Papers 1},
    organization = {Springer International Publishing},
    pages = {186--202},
    title = {Explainable argumentation for wellness consultation},
    year = {2019}
}

@inproceedings{zhang2019cross_Static_Taskallocation_Domains,
    author = {Zhang, Dingdian and Schneider, Eric and Sklar, Elizabeth},
    booktitle = {Towards Autonomous Robotic Systems: 20th Annual Conference, TAROS 2019, London, UK, July 3--5, 2019, Proceedings, Part II 20},
    organization = {Springer International Publishing},
    pages = {261--272},
    title = {A cross-landscape evaluation of multi-robot team performance in static task-allocation domains},
    year = {2019}
}

@misc{drake2019pp31_Dash_Policy_Opportunities,
    author = {Drake, Archie and Pollitt, Alex and Smith, Leanne and Sklar, Elizabeth},
    publisher = {BMJ Publishing Group Ltd and the British Association for Accident~…},
    title = {PP31 Data awareness for sending help (DASH): policy opportunities \& challenges},
    year = {2019}
}

@article{Polvara2019_Deep_Reinforcement_Learning,
    author = {Polvara, R. and Sharma, S. and Wan, J. and Manning, A. and Sutton, R.},
    journal = {Robotica},
    number = {11},
    pages = {1867-1882},
    title = {Autonomous Vehicular Landings on the Deck of an Unmanned Surface Vehicle using Deep Reinforcement Learning},
    volume = {37},
    year = {2019}
}

@article{chapman2019semi_Connecting_Proprietary_Ehr,
    author = {Chapman, Martin and Curcin, Vasa and Sklar, Elizabeth I},
    journal = {arXiv preprint arXiv:1911.12254},
    title = {A semi-autonomous approach to connecting proprietary EHR standards to FHIR},
    year = {2019}
}

@article{Pearson2019_Change_Microwave_Radiometer,
    author = {Zhu, Y. and Zheng, Y. and Fang, S. and Zou, Y. and Pearson, S.},
    journal = {Advances in Space Research},
    number = {1},
    pages = {750-765},
    title = {Analysis of the brightness temperature features of the lunar surface using 37?GHz channel data from the Chang{'}E-2 microwave radiometer},
    volume = {63},
    year = {2019}
}

@article{Hanheide2019_Making_The_Case,
    author = {Fernandez Carmona, M. and Parekh, T. and Hanheide, M.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {449-453},
    title = {Making the case for human-aware navigation in warehouses},
    volume = {11650 LNAI},
    year = {2019}
}

@article{Al-khafajiy2019_Things_Volume_Sjiot,
    author = {Boukadi, K. and Faci, N. and Maamar, Z. and Ugljanin, E. and Sellami, M. and Baker, T. and Al-Khafajiy, M.},
    journal = {Internet of Things (Netherlands)},
    title = {Corrigendum to Norm-based and commitment-driven agentification of the Internet of Things [Internet of Things volume 6 (2019) 100042](S2542660518301999)(10.1016/j.iot.2019.02.002)},
    volume = {7},
    year = {2019}
}

@article{Liu_2019_Dynamic_Facial_Movements,
    author = {Daqi Liu and Nicola Bellotto and Shigang Yue},
    doi = {10.1109/tnnls.2019.2927274},
    journal = {{IEEE} Transactions on Neural Networks and Learning Systems},
    pages = {1--10},
    publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
    title = {Deep Spiking Neural Network for Video-Based Disguise Face Recognition Based on Dynamic Facial Movements},
    url = {https://doi.org/10.1109%2Ftnnls.2019.2927274},
    year = {2019}
}

@article{Harman_2019_Hierarchical_Continual_Planning,
    author = {Helen Harman and Keshav Chintamani and Pieter Simoens},
    doi = {10.3390/s19224856},
    journal = {Sensors},
    month = {nov},
    number = {22},
    pages = {4856},
    publisher = {{MDPI} {AG}},
    title = {Robot Assistance in Dynamic Smart Environments{\textemdash}A Hierarchical Continual Planning in the Now Framework},
    url = {https://doi.org/10.3390%2Fs19224856},
    volume = {19},
    year = {2019}
}

@inproceedings{zhivkov2019mrcomm_Multirobot_Communication_Testbed,
    author = {Zhivkov, Tsvetan and Schneider, Eric and Sklar, Elizabeth},
    booktitle = {Towards Autonomous Robotic Systems: 20th Annual Conference, TAROS 2019, London, UK, July 3--5, 2019, Proceedings, Part II 20},
    organization = {Springer International Publishing},
    pages = {346--357},
    title = {MRComm: Multi-robot communication testbed},
    year = {2019}
}

@article{Bochtis2019_Ict_Innovations,
    author = {S?rensen, C.A.G. and Kateris, D. and Bochtis, D.},
    journal = {Communications in Computer and Information Science},
    pages = {1-19},
    title = {ICT Innovations and Smart Farming},
    volume = {953},
    year = {2019}
}

@article{Al-khafajiy2019_Cognitive_Computing_Meets,
    author = {Maamar, Z. and Baker, T. and Faci, N. and Ugljanin, E. and Atif, Y. and Al-Khafajiy, M. and Sellami, M.},
    journal = {ICSOFT 2018 - Proceedings of the 13th International Conference on Software Technologies},
    pages = {741-746},
    title = {Cognitive computing meets the internet of things},
    year = {2019}
}

@article{Al-khafajiy2019_Improving_Fog_Computing,
    author = {Al-khafajiy, M. and Baker, T. and Al-Libawy, H. and Maamar, Z. and Aloqaily, M. and Jararweh, Y.},
    journal = {Future Generation Computer Systems},
    pages = {266-280},
    title = {Improving fog computing performance via Fog-2-Fog collaboration},
    volume = {100},
    year = {2019}
}

@inproceedings{henning2019combining_Argument_Mining_Methods,
    author = {Henning, Andrew and Young, Anthony P and Sklar, Elizabeth and Miles, Simon and Black, Elizabeth},
    booktitle = {AI$^3$@ AI* IA},
    pages = {135--139},
    title = {Combining Classification-centered and Relation-based Argument Mining Methods.},
    year = {2019}
}

@article{Harman_2019_Performing_Goal_Recognition,
    author = {Harman, Helen and Simoens, Pieter},
    doi = {10.3390/s19122741},
    issn = {1424-8220},
    journal = {Sensors},
    month = {June},
    number = {12},
    pages = {2741},
    publisher = {MDPI AG},
    title = {Action Graphs for Performing Goal Recognition Design on Human-Inhabited Environments},
    url = {http://dx.doi.org/10.3390/s19122741},
    volume = {19},
    year = {2019}
}

@article{Bochtis2019_Woody_Biomass_Supplychain,
    author = {Rodias, E. and Berruto, R. and Bochtis, D. and Sopegno, A. and Busato, P.},
    journal = {Energies},
    number = {15},
    title = {Green, yellow, and woody biomass supply-chain management: A review},
    volume = {14},
    year = {2019}
}

@article{Bosilj_2019_Crops_Versus_Weeds,
    author = {Bosilj, Petra and Aptoula, Erchan and Duckett, Tom and Cielniak, Grzegorz},
    doi = {10.1002/rob.21869},
    issn = {1556-4967},
    journal = {Journal of Field Robotics},
    month = {March},
    number = {1},
    pages = {7–19},
    publisher = {Wiley},
    title = {Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture},
    url = {http://dx.doi.org/10.1002/rob.21869},
    volume = {37},
    year = {2019}
}

@article{Cielniak2019_Selective_Broccoli_Harvesting,
    author = {Montes, H.A. and Cielniak, G. and Duckett, T.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {448-459},
    title = {Model-Based 3D Point Cloud Segmentation for Automated Selective Broccoli Harvesting},
    volume = {11649 LNAI},
    year = {2019}
}

@conference{11577_3455029_Active_Object_Search,
    author = {Lock, J. C. and Cielniak, G. and Bellotto, N.},
    booktitle = {Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019)},
    doi = {10.5220/0007582304760485},
    pages = {476--485},
    title = {Active object search with a mobile device for people with visual impairments},
    url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068222941&doi=10.5220/0007582304760485&partnerID=40&md5=053e50bb98ee968d44a5c6adb5d918b5},
    volume = {4},
    year = {2019}
}

@inbook{72d40af78d454e1ebceb70a181d08db8_Mobile_Robots,
    abstract = {This paper reports some preliminary work on learning on a physical robot. In particular, we report on an experiment to learn how to strike a ball to hit a target on the ground. We compare learning based just on previous trials with the robot with learning based on those trials plus additional data learnt using a generative adversarial network (GAN). We find that the additional data generated by the GAN improves the performance of the robot.},
    address = {Germany},
    author = {Todd Flyr and Simon Parsons},
    booktitle = {Towards Autonomous Robotic Systems - 20th Annual Conference, TAROS 2019, Proceedings},
    doi = {10.1007/978-3-030-23807-0_17},
    editor = {Kaspar Althoefer and Jelizaveta Konstantinova and Ketao Zhang},
    isbn = {9783030238063},
    language = {English},
    note = {20th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2019 ; Conference date: 03-07-2019 Through 05-07-2019},
    pages = {197--208},
    publisher = {Springer Verlag},
    series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    title = {Towards Adversarial Training for Mobile Robots},
    year = {2019}
}

This paper reports some preliminary work on learning on a physical robot. In particular, we report on an experiment to learn how to strike a ball to hit a target on the ground. We compare learning based just on previous trials with the robot with learning based on those trials plus additional data learnt using a generative adversarial network (GAN). We find that the additional data generated by the GAN improves the performance of the robot.

@article{1d52c1a3eee34259ba7c60fe8b3cc803_Deception_Using_Theory,
    abstract = {Agreement, cooperation and trust would be straightforward if deception did not ever occur in communicative interactions. Humans have deceived one another since the species began. Do machines deceive one another or indeed humans? If they do, how may we detect this? To detect machine deception, arguably requires a model of how machines may deceive, and how such deception may be identified. Theory of Mind (ToM) provides the opportunity to create intelligent machines that are able to model the minds of other agents. The future implications of a machine that has the capability to understand other minds (human or artificial) and that also has the reasons and intentions to deceive others are dark from an ethical perspective. Being able to understand the dishonest and unethical behaviour of such machines is crucial to current research in AI. In this paper, we present a high-level approach for modelling machine deception using ToM under factors of uncertainty and we propose an implementation of this model in an Agent-Oriented Programming Language (AOPL). We show that the Multi-Agent Systems (MAS) paradigm can be used to integrate concepts from two major theories of deception, namely Information Manipulation Theory 2 (IMT2) and Interpersonal Deception Theory (IDT), and how to apply these concepts in order to build a model of computational deception that takes into account ToM. To show how agents use ToM in order to deceive, we define an epistemic agent mechanism using BDI-like architectures to analyse deceptive interactions between deceivers and their potential targets and we also explain the steps in which the model can be implemented in an AOPL. To the best of our knowledge, this work is one of the first attempts in AI that (i) uses ToM along with components of IMT2 and IDT in order to analyse deceptive interactions and (ii) implements such a model.},
    author = {Stefan Sarkadi and Panisson, {Alison R.} and Bordini, {Rafael H.} and McBurney, {Peter John} and Parsons, {Simon Dominic} and Chapman, {Martin David}},
    doi = {10.3233/AIC-190615},
    issn = {0921-7126},
    journal = {AI COMMUNICATIONS},
    keywords = {agent-oriented programming languages, Deception, machine deception, theory of mind},
    language = {English},
    number = {4},
    pages = {287--302},
    publisher = {IOS Press},
    title = {Modelling Deception using Theory of Mind in Multi-Agent Systems},
    volume = {32},
    year = {2019}
}

Agreement, cooperation and trust would be straightforward if deception did not ever occur in communicative interactions. Humans have deceived one another since the species began. Do machines deceive one another or indeed humans? If they do, how may we detect this? To detect machine deception, arguably requires a model of how machines may deceive, and how such deception may be identified. Theory of Mind (ToM) provides the opportunity to create intelligent machines that are able to model the minds of other agents. The future implications of a machine that has the capability to understand other minds (human or artificial) and that also has the reasons and intentions to deceive others are dark from an ethical perspective. Being able to understand the dishonest and unethical behaviour of such machines is crucial to current research in AI. In this paper, we present a high-level approach for modelling machine deception using ToM under factors of uncertainty and we propose an implementation of this model in an Agent-Oriented Programming Language (AOPL). We show that the Multi-Agent Systems (MAS) paradigm can be used to integrate concepts from two major theories of deception, namely Information Manipulation Theory 2 (IMT2) and Interpersonal Deception Theory (IDT), and how to apply these concepts in order to build a model of computational deception that takes into account ToM. To show how agents use ToM in order to deceive, we define an epistemic agent mechanism using BDI-like architectures to analyse deceptive interactions between deceivers and their potential targets and we also explain the steps in which the model can be implemented in an AOPL. To the best of our knowledge, this work is one of the first attempts in AI that (i) uses ToM along with components of IMT2 and IDT in order to analyse deceptive interactions and (ii) implements such a model.

@article{Bochtis2019_Voicedriven_Fleet_Management,
    author = {Achillas, C. and Bochtis, D. and Aidonis, D. and Marinoudi, V. and Folinas, D.},
    journal = {Information Processing in Agriculture},
    number = {4},
    pages = {471-478},
    title = {Voice-driven fleet management system for agricultural operations},
    volume = {6},
    year = {2019}
}

@inproceedings{balatsoukas2019user_Datadriven_Selfmanagement_Decision,
    author = {Balatsoukas, Panos and Porat, Talya and Sassoon, Isabel and Essers, Kai and Kokciyan, Nadin and Chapman, Martin and Drake, Archie and Modgil, Sanjay and Ashworth, Mark and Sklar, Elizabeth and others},
    booktitle = {IEEE EUROCON 2019-18th International Conference on Smart Technologies},
    organization = {IEEE},
    pages = {1--6},
    title = {User involvement in the design of a data-driven self-management decision support tool for stroke survivors},
    year = {2019}
}

@article{Lampridi_2019_Precision_Arable_Farming,
    author = {Lampridi, Maria G. and Kateris, Dimitrios and Vasileiadis, Giorgos and Marinoudi, Vasso and Pearson, Simon and Sørensen, Claus G. and Balafoutis, Athanasios and Bochtis, Dionysis},
    doi = {10.3390/agronomy9040175},
    issn = {2073-4395},
    journal = {Agronomy},
    month = {April},
    number = {4},
    pages = {175},
    publisher = {MDPI AG},
    title = {A Case-Based Economic Assessment of Robotics Employment in Precision Arable Farming},
    url = {http://dx.doi.org/10.3390/agronomy9040175},
    volume = {9},
    year = {2019}
}

@inbook{Savin_2019_Region_Zmp_Trajectory,
    author = {Savin, Sergei and Khusainov, Ramil and Klimchik, Alexandr},
    booktitle = {Proceedings of 14th International Conference on Electromechanics and Robotics “Zavalishin’s Readings”},
    doi = {10.1007/978-981-13-9267-2_11},
    isbn = {9789811392672},
    issn = {2190-3026},
    month = {August},
    pages = {125–136},
    publisher = {Springer Singapore},
    title = {Admissible Region ZMP Trajectory Generation for Bipedal Robots Walking Over Uneven Terrain},
    url = {http://dx.doi.org/10.1007/978-981-13-9267-2_11},
    year = {2019}
}

@article{Papageorgiou_2019_Cognitive_Mapbased_Sustainable,
    author = {Papageorgiou, Konstantinos and Singh, Pramod K. and Papageorgiou, Elpiniki and Chudasama, Harpalsinh and Bochtis, Dionysis and Stamoulis, George},
    doi = {10.3390/su12010305},
    issn = {2071-1050},
    journal = {Sustainability},
    month = {December},
    number = {1},
    pages = {305},
    publisher = {MDPI AG},
    title = {Fuzzy Cognitive Map-Based Sustainable Socio-Economic Development Planning for Rural Communities},
    url = {http://dx.doi.org/10.3390/su12010305},
    volume = {12},
    year = {2019}
}

@inbook{f945ec4ce04e4b809156dd6c9de95210_Clinical_Decision_Support,
    abstract = {This demonstration highlights the design of the Consult system, a modular decision-support system (DSS) intended to help patients suffering from chronic conditions self-manage their treatments. The system takes input from multiple sources, including commercial wellness sensors and a patient's electronic health record, to inform a computational argumentation engine that constructs weighted opinions using these inputs and knowledge about their sources, and uses an interaction agent driven by argumentation-based dialogue to respond to user queries.},
    author = {Martin Chapman and Panagiotis Balatsoukas and Nadin K{\"o}kciyan and Kai Essers and Isabel Sassoon and Mark Ashworth and Vasa Curcin and Sanjay Modgil and Simon Parsons and Sklar, {Elizabeth I.}},
    booktitle = {Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems},
    day = {17},
    isbn = {9781450363099},
    keywords = {Argumentation, Decision-support tool, Human-agent interaction},
    language = {English},
    month = {May},
    note = {18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 ; Conference date: 13-05-2019 Through 17-05-2019},
    pages = {2345--2347},
    publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
    title = {Computational Argumentation-based Clinical Decision Support},
    volume = {4},
    year = {2019}
}

This demonstration highlights the design of the Consult system, a modular decision-support system (DSS) intended to help patients suffering from chronic conditions self-manage their treatments. The system takes input from multiple sources, including commercial wellness sensors and a patient's electronic health record, to inform a computational argumentation engine that constructs weighted opinions using these inputs and knowledge about their sources, and uses an interaction agent driven by argumentation-based dialogue to respond to user queries.

@article{Al-khafajiy2019_Syst_Res_Sjcogsys,
    author = {Maamar, Z. and Baker, T. and Faci, N. and Al-Khafajiy, M. and Ugljanin, E. and Atif, Y. and Sellami, M.},
    journal = {Cognitive Systems Research},
    pages = {54},
    title = {Corrigendum to ?Weaving cognition into the internet-of-things: Application to water leaks? [Cogn. Syst. Res. 56 (2019)233?245](S1389041718303292)(10.1016/j.cogsys.2019.04.001)},
    volume = {58},
    year = {2019}
}

@inproceedings{zhivkov2019establishing_Establishing_Continuous_Communication,
    author = {Zhivkov, Tsvetan and Schneider, Eric and Sklar, Elizabeth and others},
    organization = {UK-RAS19 Conference},
    title = {Establishing continuous communication through dynamic team behaviour switching},
    year = {2019}
}

@inbook{a3e5a83380a747799ee035ade11af485_Datadriven_Selfmanagement_Decision,
    abstract = {Many chronic conditions can be better managed bypatients themselves with the use of decision support tools. Thisbecomes even more necessary in the case of multimorbidity (i.e.presence of multiple chronic diseases) or in conditions whereseveral underlying risk factors need to be managed and monitoredin order to avoid relapse or the reoccurrence of an event, like inthe case of stroke. However, despite the fact that these decisionsupport systems are becoming prevalent, little is known about thebest practices in designing for end-users - patients and theircarers. The aim of the present paper is to report on the process ofinvolving users to inform the design of a novel data-driven selfmanagementmobile decision support tool for stroke survivors,called CONSULT. User involvement was facilitated through theuse of a two-phase participatory design approach. During bothphases a total of 44 stakeholders participated, including strokesurvivors, carers, healthcare professionals and researchers. Thepaper documents the findings of the participatory design process,in the form of design recommendations, and describes theirimplications for user interface design.},
    author = {Panagiotis Balatsoukas and Talya Porat and Sassoon, {Isabel Karen} and Kai Essers and Nadin Kokciyan and Chapman, {Martin David} and Archie Drake and Sanjay Modgil and Mark Ashworth and Sklar, {Elizabeth Ida} and Parsons, {Simon Dominic} and Vasa Curcin},
    booktitle = {18th IEEE International Conference on Smart Technologies},
    doi = {10.1109/EUROCON.2019.8861812},
    language = {English},
    publisher = {IEEE Computer Society Press},
    title = {User involvement in the design of a data-driven self-management decision support tool for stroke survivors},
    year = {2019}
}

Many chronic conditions can be better managed bypatients themselves with the use of decision support tools. Thisbecomes even more necessary in the case of multimorbidity (i.e.presence of multiple chronic diseases) or in conditions whereseveral underlying risk factors need to be managed and monitoredin order to avoid relapse or the reoccurrence of an event, like inthe case of stroke. However, despite the fact that these decisionsupport systems are becoming prevalent, little is known about thebest practices in designing for end-users - patients and theircarers. The aim of the present paper is to report on the process ofinvolving users to inform the design of a novel data-driven selfmanagementmobile decision support tool for stroke survivors,called CONSULT. User involvement was facilitated through theuse of a two-phase participatory design approach. During bothphases a total of 44 stakeholders participated, including strokesurvivors, carers, healthcare professionals and researchers. Thepaper documents the findings of the participatory design process,in the form of design recommendations, and describes theirimplications for user interface design.

@article{Bochtis2019_Robot_Operating_System,
    author = {Tsolakis, N. and Bechtsis, D. and Bochtis, D.},
    journal = {Agronomy},
    number = {7},
    title = {Agros: A robot operating system based emulation tool for agricultural robotics},
    volume = {9},
    year = {2019}
}

@article{Das2019_Probabilistic_Movement_Primitives,
    author = {Shyam, R.B.A. and Lightbody, P. and Das, G. and Liu, P. and Gomez-Gonzalez, S. and Neumann, G.},
    journal = {IEEE International Conference on Intelligent Robots and Systems},
    pages = {2666-2671},
    title = {Improving Local Trajectory Optimisation using Probabilistic Movement Primitives},
    year = {2019}
}

@article{Boukadi_2019_Internet_Of_Things,
    author = {Boukadi, Khouloud and Faci, Noura and Maamar, Zakaria and Ugljanin, Emir and Sellami, Mohamed and Baker, Thar and Al-Khafajiy, Mohammed},
    doi = {10.1016/j.iot.2019.02.002},
    issn = {2542-6605},
    journal = {Internet of Things},
    month = {June},
    pages = {100042},
    publisher = {Elsevier BV},
    title = {Norm-based and commitment-driven agentification of the Internet of Things},
    url = {http://dx.doi.org/10.1016/j.iot.2019.02.002},
    volume = {6},
    year = {2019}
}

@misc{82bfb5774f484e0899cfeb49061cffeb_Wellness_Consultation,
    author = {Sassoon, {Isabel Karen} and Sklar, {Elizabeth Ida} and Nadin Kokciyan and Parsons, {Simon Dominic}},
    day = {14},
    language = {English},
    month = {May},
    publisher = {EXTRAAMAS },
    title = {Explainable Argumentation for Wellness Consultation},
    type = {Other},
    year = {2019}
}

@article{Al-khafajiy2019_Optimizing_Project_Delivery,
    author = {Hussien, A. and Tucker, M. and Cotgrave, A. and Al-Khafajiy, M. and Shamsa, T.B.},
    journal = {Proceedings - International Conference on Developments in eSystems Engineering, DeSE},
    pages = {1006-1013},
    title = {Optimizing project delivery through augmented reality and agile methodologies},
    volume = {October-2019},
    year = {2019}
}

@conference{11577_3455030_Dataset_For_Action,
    abstract = {The development of autonomous robots for agriculture depends on a successful approach to recognize user needs as well as datasets reflecting the characteristics of the domain. Available datasets for 3D Action Recognition generally feature controlled lighting and framing while recording subjects from the front. They mostly reflect good recording conditions and therefore fail to account for the highly variable conditions the robot would have to work with in the field, e.g. when providing in-field logistic support for human fruit pickers as in our scenario. Existing work on Intention Recognition mostly labels plans or actions as intentions, but neither of those fully capture the extend of human intent. In this work, we argue for a holistic view on human Intention Recognition and propose a set of recording conditions, gestures and behaviors that better reflect the environment and conditions an agricultural robot might find itself in. We demonstrate the utility of the dataset by means of evaluating two human detection methods: Bounding boxes and skeleton extraction.},
    author = {Gabriel, A. and Cosar, S. and Bellotto, N. and Baxter, P.},
    booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    doi = {10.1007/978-3-030-23807-0_30},
    isbn = {978-3-030-23806-3},
    keywords = {Action Recognition; Agricultural robotics; Dataset; Human-robot interaction; Intention recognition},
    pages = {362--374},
    publisher = {Springer Verlag},
    title = {A Dataset for Action Recognition in the Wild},
    volume = {11649},
    year = {2019}
}

The development of autonomous robots for agriculture depends on a successful approach to recognize user needs as well as datasets reflecting the characteristics of the domain. Available datasets for 3D Action Recognition generally feature controlled lighting and framing while recording subjects from the front. They mostly reflect good recording conditions and therefore fail to account for the highly variable conditions the robot would have to work with in the field, e.g. when providing in-field logistic support for human fruit pickers as in our scenario. Existing work on Intention Recognition mostly labels plans or actions as intentions, but neither of those fully capture the extend of human intent. In this work, we argue for a holistic view on human Intention Recognition and propose a set of recording conditions, gestures and behaviors that better reflect the environment and conditions an agricultural robot might find itself in. We demonstrate the utility of the dataset by means of evaluating two human detection methods: Bounding boxes and skeleton extraction.

@article{Cielniak2019_Pedestrian_Flow_Patterns,
    author = {Molina, S. and Cielniak, G. and Duckett, T.},
    journal = {Proceedings - IEEE International Conference on Robotics and Automation},
    pages = {9725-9731},
    title = {Go with the flow: Exploration and mapping of pedestrian flow patterns from partial observations},
    volume = {2019-May},
    year = {2019}
}

@article{Cielniak2019_Assisted_Loop_Closure,
    author = {Zaganidis, A. and Zerntev, A. and Duckett, T. and Cielniak, G.},
    journal = {IEEE International Conference on Intelligent Robots and Systems},
    pages = {4562-4568},
    title = {Semantically Assisted Loop Closure in SLAM Using NDT Histograms},
    year = {2019}
}

@article{Bochtis2019_Precision_Arable_Farming,
    author = {Lampridi, M.G. and Kateris, D. and Vasileiadis, G. and Marinoudi, V. and Pearson, S. and S?rensen, C.G. and Balafoutis, A. and Bochtis, D.},
    journal = {Agronomy},
    number = {4},
    title = {A case-based economic assessment of robotics employment in precision arable farming},
    volume = {9},
    year = {2019}
}

@article{Zhu_2019_Henan_Province_China,
    author = {Zhu, Yongchao and Li, Xuan and Pearson, Simon and Wu, Dongli and Sun, Ruijing and Johnson, Sarah and Wheeler, James and Fang, Shibo},
    doi = {10.3390/w11020248},
    issn = {2073-4441},
    journal = {Water},
    month = {January},
    number = {2},
    pages = {248},
    publisher = {MDPI AG},
    title = {Evaluation of Fengyun-3C Soil Moisture Products Using In-Situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China},
    url = {http://dx.doi.org/10.3390/w11020248},
    volume = {11},
    year = {2019}
}

@conference{11577_3455195_Boneconduction_Audio_Interface,
    abstract = {The ActiVis project’s aim is to build a mobile guidance aid to help people with limited vision find objects in an unknown environment. This system uses bone-conduction headphones to transmit audio signals to the user and requires an effective non-visual interface. To this end, we propose a new audio-based interface that uses a spatialised signal to convey a target’s position on the horizontal plane. The vertical position on the median plan is given by adjusting the tone’s pitch to overcome the audio localisation limitations of bone-conduction headphones. This interface is validated through a set of experiments with blindfolded and visually impaired participants.},
    author = {Lock, J. C. and Gilchrist, I. D. and Cielniak, G. and Bellotto, N.},
    booktitle = {Communications in Computer and Information Science},
    doi = {10.1007/978-981-15-1301-5_43},
    isbn = {978-981-15-1300-8},
    keywords = {Bone-conduction; Human-machine interface; Spatialised sound; Varying pitch; Vision impairment},
    pages = {542--553},
    publisher = {Springer},
    title = {Bone-conduction audio interface to guide people with visual impairments},
    volume = {1122},
    year = {2019}
}

The ActiVis project’s aim is to build a mobile guidance aid to help people with limited vision find objects in an unknown environment. This system uses bone-conduction headphones to transmit audio signals to the user and requires an effective non-visual interface. To this end, we propose a new audio-based interface that uses a spatialised signal to convey a target’s position on the horizontal plane. The vertical position on the median plan is given by adjusting the tone’s pitch to overcome the audio localisation limitations of bone-conduction headphones. This interface is validated through a set of experiments with blindfolded and visually impaired participants.

@incollection{Fu_2019_Vehicle_Driving_Scenarios,
    author = {Qinbing Fu and Nicola Bellotto and Huatian Wang and F. Claire Rind and Hongxin Wang and Shigang Yue},
    booktitle = {{IFIP} Advances in Information and Communication Technology},
    doi = {10.1007/978-3-030-19823-7_5},
    pages = {67--79},
    publisher = {Springer International Publishing},
    title = {A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios},
    url = {https://doi.org/10.1007%2F978-3-030-19823-7_5},
    year = {2019}
}

@article{Hanheide2019_Tour_Guide_Robot,
    author = {Duchetto, F.D. and Baxter, P. and Hanheide, M.},
    journal = {2019 28th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2019},
    title = {Lindsey the Tour Guide Robot - Usage Patterns in a Museum Long-Term Deployment},
    year = {2019}
}

@article{Al-khafajiy2019_Iotfog_Optimal_Workload,
    author = {Al-Khafajiy, M. and Baker, T. and Waraich, A. and Al-Jumeily, D. and Hussain, A.},
    journal = {Proceedings - 11th IEEE/ACM International Conference on Utility and Cloud Computing Companion, UCC Companion 2018},
    pages = {349-352},
    title = {Iot-fog optimal workload via fog offloading},
    year = {2019}
}

@article{Bochtis2019_Microstructural_Surface_Topology,
    author = {Tsongas, K. and Tzetzis, D. and Karantzalis, A. and Banias, G. and Exarchos, D. and Ahmadkhaniha, D. and Zanella, C. and Matikas, T. and Bochtis, D.},
    journal = {Applied Sciences (Switzerland)},
    number = {14},
    title = {Microstructural, surface topology and nanomechanical characterization of electrodeposited Ni-P/SiC nanocomposite coatings},
    volume = {9},
    year = {2019}
}

@article{Al-khafajiy2019_Iot_Networking_Paradigms,
    author = {Al-Khafajiy, M. and Ghareeb, S. and Al-Jumeily, R. and Almurshedi, R. and Hussien, A. and Baker, T. and Jararweh, Y.},
    journal = {Proceedings - International Conference on Developments in eSystems Engineering, DeSE},
    pages = {943-949},
    title = {A holistic study on emerging IoT networking paradigms},
    volume = {October-2019},
    year = {2019}
}

@inbook{Baxter_2019_Dialogue_Interactivity_Development,
    author = {Baxter, Paul and Del Duchetto, Francesco and Hanheide, Marc},
    booktitle = {Educational Robotics in the Context of the Maker Movement},
    doi = {10.1007/978-3-030-18141-3_12},
    isbn = {9783030181413},
    issn = {2194-5365},
    month = {December},
    pages = {147–160},
    publisher = {Springer International Publishing},
    title = {Engaging Learners in Dialogue Interactivity Development for Mobile Robots},
    url = {http://dx.doi.org/10.1007/978-3-030-18141-3_12},
    year = {2019}
}

@inbook{Bosilj_2019_Soil_Size_Distribution,
    author = {Bosilj, Petra and Gould, Iain and Duckett, Tom and Cielniak, Grzegorz},
    booktitle = {Mathematical Morphology and Its Applications to Signal and Image Processing},
    doi = {10.1007/978-3-030-20867-7_32},
    isbn = {9783030208677},
    issn = {1611-3349},
    pages = {415–427},
    publisher = {Springer International Publishing},
    title = {Pattern Spectra from Different Component Trees for Estimating Soil Size Distribution},
    url = {http://dx.doi.org/10.1007/978-3-030-20867-7_32},
    year = {2019}
}

@article{Pearson2018_Effect_Of_High,
    author = {Al-Said, F. and Hadley, P. and Pearson, S. and Khan, M.M. and Iqbal, Q.},
    journal = {Pakistan Journal of Agricultural Sciences},
    number = {1},
    pages = {95-101},
    title = {Effect of high temperature and exposure duration on stem elongation of iceberg lettuce},
    volume = {55},
    year = {2018}
}

@article{Hanheide2018_Lightweight_Navigation_System,
    author = {L{\'a}zaro, M.T. and Grisetti, G. and Iocchi, L. and Fentanes, J.P. and Hanheide, M.},
    journal = {Advances in Intelligent Systems and Computing},
    pages = {295-306},
    title = {A Lightweight Navigation System for Mobile Robots},
    volume = {694},
    year = {2018}
}

@book{FoxDataBook_Science_For_Transport,
    address = {Berlin Heidelberg},
    author = {Fox, Charles},
    publisher = {Springer, Textbooks in Earth Sciences, Geography and Environment},
    title = {Data Science for Transport: a self study guide with computer exercises},
    year = {2018}
}

@inproceedings{Maamar_2018_Cognitive_Computing_Meets,
    author = {Maamar, Zakaria and Baker, Thar and Faci, Noura and Ugljanin, Emir and Atif, Yacine and Al-Khafajiy, Mohammed and Sellami, Mohamed},
    booktitle = {Proceedings of the 13th International Conference on Software Technologies},
    doi = {10.5220/0006877507750780},
    pages = {775–780},
    publisher = {SCITEPRESS - Science and Technology Publications},
    title = {Cognitive Computing Meets the Internet of Things},
    url = {http://dx.doi.org/10.5220/0006877507750780},
    year = {2018}
}

@article{Liakos_2018_Agriculture_A_Review,
    author = {Liakos, Konstantinos and Busato, Patrizia and Moshou, Dimitrios and Pearson, Simon and Bochtis, Dionysis},
    doi = {10.3390/s18082674},
    issn = {1424-8220},
    journal = {Sensors},
    month = {August},
    number = {8},
    pages = {2674},
    publisher = {MDPI AG},
    title = {Machine Learning in Agriculture: A Review},
    url = {http://dx.doi.org/10.3390/s18082674},
    volume = {18},
    year = {2018}
}

@article{Bosilj_2018_Precision_Agriculture,
    author = {Petra Bosilj and Tom Duckett and Grzegorz Cielniak},
    doi = {10.1109/lra.2018.2848305},
    journal = {{IEEE} Robotics and Automation Letters},
    month = {oct},
    number = {4},
    pages = {2950--2956},
    publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
    title = {Analysis of Morphology-Based Features for Classification of Crop and Weeds in Precision Agriculture},
    url = {https://doi.org/10.1109%2Flra.2018.2848305},
    volume = {3},
    year = {2018}
}

@article{Pearson2018_Realtime_Robotic_Motion,
    author = {Liu, P. and Elgeneidy, K. and Pearson, S. and Huda, M.N. and Neumann, G.},
    journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    pages = {481-483},
    title = {Towards real-time robotic motion planning for grasping in cluttered and uncertain environments},
    volume = {10965 LNAI},
    year = {2018}
}

@article{maleki2018performance_Compressor_Fouling_Condition,
    author = {Maleki, Sepehr and Cruz-Manzo, Samuel and Bingham, Chris and Panov, Vili and others},
    publisher = {Global Power and Propulsion Society},
    title = {Performance analysis and prediction of compressor fouling condition for a twin-shaft engine},
    year = {2018}
}

@inproceedings{Baxter_2018_Safe_Humanrobot_Interaction,
    author = {Paul Baxter and Grzegorz Cielniak and Marc Hanheide and P{\aa}l From},
    booktitle = {Companion of the 2018 {ACM}/{IEEE} International Conference on Human-Robot Interaction  - {HRI} {\textquotesingle}18},
    doi = {10.1145/3173386.3177072},
    publisher = {{ACM} Press},
    title = {Safe Human-Robot Interaction in Agriculture},
    url = {https://doi.org/10.1145%2F3173386.3177072},
    year = {2018}
}

@article{Das2018_System_Identification_Techniques,
    author = {Vance, P.J. and Das, G.P. and Kerr, D. and Coleman, S.A. and McGinnity, T.M. and Gollisch, T. and Liu, J.K.},
    journal = {IEEE Transactions on Neural Networks and Learning Systems},
    number = {5},
    pages = {1796-1808},
    title = {Bioinspired approach to modeling retinal ganglion cells using system identification techniques},
    volume = {29},
    year = {2018}
}

@article{Bochtis2018_Printed_Circuit_Boards,
    author = {Exarchos, D.A. and Dalla, P.T. and Tzetzis, D. and Karantzalis, A.E. and Bochtis, D. and Matikas, T.E.},
    journal = {Proceedings of SPIE - The International Society for Optical Engineering},
    title = {Towards miniaturization of electronics by developing and characterizing hyperfine solder powders used in printed circuit boards},
    volume = {10601},
    year = {2018}
}

@article{Cielniak2018_Connected_Attribute_Morphology,
    author = {Bosilj, P. and Duckett, T. and Cielniak, G.},
    journal = {Computers in Industry},
    pages = {226-240},
    title = {Connected attribute morphology for unified vegetation segmentation and classification in precision agriculture},
    volume = {98},
    year = {2018}
}

@techreport{drake2018data_Ambulance_Dispatch,
    author = {Drake, Archie and Sklar, E and Smith, L and Parsons, S and Schneider, E},
    institution = {tech. rep., Policy Institute at King’s College London},
    title = {Data for Ambulance Dispatch},
    year = {2018}
}

@article{cohen2018characterization_Abstract_Argumentation,
    author = {Cohen, Andrea and Parsons, Simon and Sklar, Elizabeth I and McBurney, Peter},
    journal = {International Journal of Approximate Reasoning},
    pages = {76--104},
    publisher = {Elsevier},
    title = {A characterization of types of support between structured arguments and their relationship with support in abstract argumentation},
    volume = {94},
    year = {2018}
}

@inproceedings{Baxter_2018_Providing_Cognitive_Assistance,
    author = {Paul Baxter and Peter Lightbody and Marc Hanheide},
    booktitle = {Companion of the 2018 {ACM}/{IEEE} International Conference on Human-Robot Interaction  - {HRI} {\textquotesingle}18},
    doi = {10.1145/3173386.3177070},
    publisher = {{ACM} Press},
    title = {Robots Providing Cognitive Assistance in Shared Workspaces},
    url = {https://doi.org/10.1145%2F3173386.3177070},
    year = {2018}
}

@article{Klimchik2018_Matrix_Structural_Analysis,
    author = {Klimchik, A. and Pashkevich, A.},
    journal = {IFAC-PapersOnLine},
    number = {11},
    pages = {710-715},
    title = {Stiffness modelling of serial under-constrained manipulators using matrix structural analysis},
    volume = {51},
    year = {2018}
}

@article{Polvara2018_Unmanned_Surface_Vehicles,
    author = {Polvara, R. and Sharma, S. and Wan, J. and Manning, A. and Sutton, R.},
    journal = {Journal of Navigation},
    number = {1},
    pages = {241-256},
    title = {Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles},
    volume = {71},
    year = {2018}
}

@article{2018_Resource_Management_Challenges,
    author = {Aldawsari, Bandar and Baker, Thar and Asim, Muhammad and Maamar, Zakaria and Al-Jumeily, Dhiya and Alkhafajiy, Mohammed},
    doi = {10.32010/26166127.2018.1.1.51.65},
    issn = {2616-6127},
    journal = {Azerbaijan Journal of High Performance Computing},
    month = {August},
    number = {1},
    pages = {51–65},
    publisher = {Azerbaijan State Oil and Industry University},
    title = {A Survey of Resource Management Challenges in Multi-cloud Environment: Taxonomy and Empirical Analysis},
    url = {http://dx.doi.org/10.32010/26166127.2018.1.1.51.65},
    volume = {1},
    year = {2018}
}

@article{Klimchik2018_Planar_Rrr_Robot,
    author = {Popov, D. and Klimchik, A.},
    journal = {IFAC-PapersOnLine},
    number = {11},
    pages = {734-739},
    title = {Optimal Planar 3RRR Robot Assembly Mode and Actuation Scheme for Machining Applications},
    volume = {51},
    year = {2018}
}

@inproceedings{porat2018stakeholders_Collaborative_Decision_Support,
    author = {Porat, Talya and Kokciyan, Nadin and Sassoon, Isabel and Young, Anthony P and Chapman, Martin and Ashworth, Mark and Modgil, Sanjay and Parsons, Simon and Sklar, Elizabeth and Curcin, Vasa},
    booktitle = {AMIA 2018 Annual Symposium},
    title = {Stakeholders' views on a collaborative decision support system to promote multimorbidity self-management: barriers, facilitators and design implications},
    year = {2018}
}

@article{Klimchik2018_Industrial_Robot,
    author = {Ostanin, M. and Popov, D. and Klimchik, A.},
    journal = {IFAC-PapersOnLine},
    number = {11},
    pages = {72-77},
    title = {Programming by Demonstration Using Two-Step Optimization for Industrial Robot.},
    volume = {51},
    year = {2018}
}

@article{Tayeb_2018_Deep_Neural_Networks,
    author = {Zied Tayeb and Juri Fedjaev and Nejla Ghaboosi and Christoph Richter and Lukas Everding and Xingwei Qu and Yingyu Wu and Gordon Cheng and Jörg Conradt},
    doi = {10.20944/preprints201809.0481.v1},
    month = {sep},
    publisher = {{MDPI} {AG}},
    title = {Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from {EEG} Signals},
    url = {https://doi.org/10.20944%2Fpreprints201809.0481.v1},
    year = {2018}
}

@article{Cielniak2018_Krigingbased_Robotic_Exploration,
    author = {Fentanes, J.P. and Badiee, A. and Duckett, T. and Evans, J. and Pearson, S. and Cielniak, G.},
    journal = {arXiv},
    title = {Kriging-based robotic exploration for soil moisture mapping using a cosmic-ray sensor},
    year = {2018}
}

@inproceedings{cruz2018analysis_Gas_Generator_Turbine,
    author = {Cruz-Manzo, Samuel and Maleki, Sepehr and Panov, Vili and Agbonzikilo, Festus and Zhang, Yu and Latimer, Anthony},
    booktitle = {ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition},
    organization = {American Society of Mechanical Engineers},
    pages = {V006T05A010--V006T05A010},
    title = {Analysis of Performance of a Twin-Shaft Gas Turbine During Hot-End Damage in the Gas Generator Turbine},
    year = {2018}
}

@article{Pearson2018_Rasberry_Robotic,
    author = {From, P.J. and Grimstad, L. and Hanheide, M. and Pearson, S. and Cielniak, G.},
    journal = {Mechanical Engineering},
    number = {6},
    pages = {14-18},
    title = {RASberry: Robotic and autonomous systems: For berry production},
    volume = {140},
    year = {2018}
}

@article{Fentanes_2018_Soil_Compaction_Mapping,
    author = {Jaime Pulido Fentanes and Iain Gould and Tom Duckett and Simon Pearson and Grzegorz Cielniak},
    doi = {10.1109/lra.2018.2849567},
    journal = {{IEEE} Robotics and Automation Letters},
    month = {oct},
    number = {4},
    pages = {3066--3072},
    publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
    title = {3-D Soil Compaction Mapping Through Kriging-Based Exploration With a Mobile Robot},
    url = {https://doi.org/10.1109%2Flra.2018.2849567},
    volume = {3},
    year = {2018}
}

@article{Hanheide2018_Dof_Pedestrian_Trajectory,
    author = {Sun, L. and Yan, Z. and Mellado, S.M. and Hanheide, M. and Duckett, T.},
    journal = {Proceedings - IEEE International Conference on Robotics and Automation},
    pages = {5942-5948},
    title = {3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data},
    year = {2018}
}

@inproceedings{Buyval_2018_Dynamic_Cost_Map,
    author = {Buyval, Alexander and Gabdullin, Aidar and Klimchik, Alexander},
    booktitle = {Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics},
    doi = {10.5220/0006901702480254},
    pages = {248–254},
    publisher = {SCITEPRESS - Science and Technology Publications},
    title = {Model Predictive Path Integral Control for Car Driving with Dynamic Cost Map},
    url = {http://dx.doi.org/10.5220/0006901702480254},
    year = {2018}
}

@article{Klimchik2018_Double_Encoders_Accuracy,
    author = {Klimchik, A. and Pashkevich, A.},
    journal = {IFAC-PapersOnLine},
    number = {11},
    pages = {740-745},
    title = {Robotic manipulators with double encoders: accuracy improvement based on advanced stiffness modeling and intelligent control},
    volume = {51},
    year = {2018}
}

@inproceedings{kokciyan2018towards_Chronic_Conditions,
    author = {Kokciyan, Nadin and Sassoon, Isabel and Young, Anthony P and Chapman, Martin and Porat, Talya and Ashworth, Mark and Curcin, Vasa and Modgil, Sanjay and Parsons, Simon and Sklar, Elizabeth},
    booktitle = {Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence},
    title = {Towards an argumentation system for supporting patients in self-managing their chronic conditions},
    year = {2018}
}

@inproceedings{markkula2018models_Models_Of_Human,
    author = {Markkula, Gustav and Romano, Richard and Madigan, Ruth and Fox, Charles W and Giles, Oscar and Merat, Natasha},
    booktitle = {Proceedings of the Transportation Research Board annual conference},
    pdf = {http://5m.duckdns.org/pubs//Markkula2018models.pdf},
    title = {Models of human decision-making as tools for estimating and optimising impacts of vehicle automation},
    year = {2018}
}

@article{Hanheide2018_Safe_Humanrobot_Interaction,
    author = {Baxter, P. and Cielniak, G. and Hanheide, M. and From, P.},
    journal = {ACM/IEEE International Conference on Human-Robot Interaction},
    pages = {59-60},
    title = {Safe Human-Robot Interaction in Agriculture},
    year = {2018}
}

@inproceedings{Mamedov_2018_Compliance_Error_Compensation,
    author = {Mamedov, Shamil and Popov, Dmitry and Mikhel, Stanislav and Klimchik, Alexandr},
    booktitle = {Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics},
    doi = {10.5220/0006905701800191},
    pages = {180–191},
    publisher = {SCITEPRESS - Science and Technology Publications},
    title = {Compliance Error Compensation based on Reduced Model for Industrial Robots},
    url = {http://dx.doi.org/10.5220/0006905701800191},
    year = {2018}
}

@article{Polvara_2018_Unmanned_Surface_Vehicle,
    author = {Polvara, Riccardo and Sharma, Sanjay and Wan, Jian and Manning, Andrew and Sutton, Robert},
    doi = {10.3390/drones2020015},
    issn = {2504-446X},
    journal = {Drones},
    month = {April},
    number = {2},
    pages = {15},
    publisher = {MDPI AG},
    title = {Vision-Based Autonomous Landing of a Quadrotor on the Perturbed Deck of an Unmanned Surface Vehicle},
    url = {http://dx.doi.org/10.3390/drones2020015},
    volume = {2},
    year = {2018}
}

@article{Johan_From_2018_Berry_Production,
    author = {P{\aa}l Johan From and Lars Grimstad and Marc Hanheide and Simon Pearson and Grzegorz Cielniak},
    doi = {10.1115/1.2018-jun-6},
    journal = {Mechanical Engineering},
    month = {jun},
    number = {06},
    pages = {S14},
    publisher = {{ASME} International},
    title = {{RASberry} - Robotic and Autonomous Systems for Berry Production},
    url = {https://doi.org/10.1115%2F1.2018-jun-6},
    volume = {140},
    year = {2018}
}

@inproceedings{sassoon2018towards_Multiagent_Decision_Support,
    author = {Sassoon, Isabel and K{\"o}kciyan, Nadin and Parsons, Simon and Sklar, Elizabeth I},
    booktitle = {Workshop on Dialogue, Explanation and Argumentation at Human Agent Interaction (HAI)},
    title = {Towards the use of Commitments in Multi-agent decision support systems},
    year = {2018}
}

@inproceedings{Lightbody_2018_Tabletop_Manipulation_Tasks,
    author = {Peter Lightbody and Paul Baxter and Marc Hanheide},
    booktitle = {Companion of the 2018 {ACM}/{IEEE} International Conference on Human-Robot Interaction  - {HRI} {\textquotesingle}18},
    doi = {10.1145/3173386.3177045},
    publisher = {{ACM} Press},
    title = {Studying Table-Top Manipulation Tasks},
    url = {https://doi.org/10.1145%2F3173386.3177045},
    year = {2018}
}

@article{Klimchik2018_Rough_Terrains_Simulation,
    author = {Bulichev, O. and Klimchik, A. and Mavridis, N.},
    journal = {2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017},
    pages = {290-295},
    title = {Optimization of centipede robot body designs through evolutionary algorithms and multiple rough terrains simulation},
    volume = {2018-January},
    year = {2018}
}

@conference{adcf2d9f48e8441a85aa0eb4b5ccd9ac_Multiagent_Decision_Support,
    abstract = {Given the increased interest and requirements to explain decisions made by agents there is potential for approaches and techniques from various domains to provide novel approaches to supporting dialogue and explainability within the HAI context.The International Workshop on Dialogue, Explanation and Argumentation in Human-Agent Interaction (DEXAHAI) will focus on the approaches, concepts and applications relevant to supporting dialogue and explainability in the Human-Agent Interaction (HAI) context. We focus on the following questions: How to develop methods to facilitate dialogue between humans and agents?How to derive arguments given such dialogues?How to explain the decisions made by the intelligent systems and how humans interact with such systems?},
    author = {Sassoon, {Isabel Karen} and Nadin Kokciyan and Parsons, {Simon Dominic} and Sklar, {Elizabeth Ida}},
    day = {15},
    language = {English},
    month = {December},
    note = {The International Workshop on Dialogue, Explanation and Argumentation in Human-Agent Interaction (DEXAHAI) ; Conference date: 15-12-2018 Through 15-12-2018},
    title = {Towards the use of Commitments in Multi-agent decision support systems},
    url = {https://sites.google.com/view/dexahai-18/home},
    year = {2018}
}

Given the increased interest and requirements to explain decisions made by agents there is potential for approaches and techniques from various domains to provide novel approaches to supporting dialogue and explainability within the HAI context.The International Workshop on Dialogue, Explanation and Argumentation in Human-Agent Interaction (DEXAHAI) will focus on the approaches, concepts and applications relevant to supporting dialogue and explainability in the Human-Agent Interaction (HAI) context. We focus on the following questions: How to develop methods to facilitate dialogue between humans and agents?How to derive arguments given such dialogues?How to explain the decisions made by the intelligent systems and how humans interact with such systems?

@article{Cielniak2018_Semantic_Segmentation,
    author = {Jeon, W.-S. and Cielniak, G. and Rhee, S.-Y.},
    journal = {International Journal of Fuzzy Logic and Intelligent Systems},
    number = {3},
    pages = {196-203},
    title = {Semantic segmentation using trade-off and internal ensemble},
    volume = {18},
    year = {2018}
}

@article{Bosilj_2018_Connected_Attribute_Morphology,
    author = {Petra Bosilj and Tom Duckett and Grzegorz Cielniak},
    doi = {10.1016/j.compind.2018.02.003},
    journal = {Computers in Industry},
    month = {jun},
    pages = {226--240},
    publisher = {Elsevier {BV}},
    title = {Connected attribute morphology for unified vegetation segmentation and classification in precision agriculture},
    url = {https://doi.org/10.1016%2Fj.compind.2018.02.003},
    volume = {98},
    year = {2018}
}

@conference{11577_3455196_Online_Transfer_Learning,
    abstract = {Human detection and tracking is an essential task for service robots, where the combined use of multiple sensors has potential advantages that are yet to be fully exploited. In this paper, we introduce a framework allowing a robot to learn a new 3D LiDAR-based human classifier from other sensors over time, taking advantage of a multisensor tracking system. The main innovation is the use of different detectors for existing sensors (i.e. RGB-D camera, 2D LiDAR) to train, online, a new 3D LiDAR-based human classifier based on a new "trajectory probability". Our framework uses this probability to check whether new detection belongs to a human trajectory, estimated by different sensors and/or detectors, and to learn a human classifier in a semi-supervised fashion. The framework has been implemented and tested on a real-world dataset collected by a mobile robot. We present experiments illustrating that our system is able to effectively learn from different sensors and from the environment, and that the performance of the 3D LiDAR-based human classification improves with the number of sensors/detectors used.},
    author = {Yan, Z and Sun, L and Ducketi, T and Bellotto, N},
    booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    pages = {7635--7640},
    publisher = {IEEE},
    title = {Multisensor Online Transfer Learning for 3D LiDAR-based Human Detection with a Mobile Robot},
    year = {2018}
}

Human detection and tracking is an essential task for service robots, where the combined use of multiple sensors has potential advantages that are yet to be fully exploited. In this paper, we introduce a framework allowing a robot to learn a new 3D LiDAR-based human classifier from other sensors over time, taking advantage of a multisensor tracking system. The main innovation is the use of different detectors for existing sensors (i.e. RGB-D camera, 2D LiDAR) to train, online, a new 3D LiDAR-based human classifier based on a new "trajectory probability". Our framework uses this probability to check whether new detection belongs to a human trajectory, estimated by different sensors and/or detectors, and to learn a human classifier in a semi-supervised fashion. The framework has been implemented and tested on a real-world dataset collected by a mobile robot. We present experiments illustrating that our system is able to effectively learn from different sensors and from the environment, and that the performance of the 3D LiDAR-based human classification improves with the number of sensors/detectors used.

@article{Bellotto2018_Camera_Based_Physiological,
    author = {Cosar, S. and Yan, Z. and Zhao, F. and Lambrou, T. and Yue, S. and Bellotto, N.},
    journal = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS},
    pages = {5010-5013},
    title = {Thermal Camera Based Physiological Monitoring with an Assistive Robot},
    volume = {2018-July},
    year = {2018}
}

@inproceedings{fox2018chicken_Road_Game_Theory,
    author = {Fox, C. and Camara, F. and Markkula, G. and Romano, R. and Madigan, R. and Merat, N.},
    booktitle = {Proc. 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS)},
    pdf = {http://5m.duckdns.org/pubs//Fox2018chicken.pdf},
    title = {When should the chicken cross the road?: Game theory for autonomous vehicle-human interactions},
    year = {2018}
}

@article{Bochtis2018_Humanitarian_Disaster_Victims,
    author = {Papadaki, S. and Banias, G. and Achillas, C. and Aidonis, D. and Folinas, D. and Bochtis, D. and Papangelou, S.},
    journal = {Springer Optimization and Its Applications},
    pages = {157-202},
    title = {A humanitarian logistics case study for the intermediary phase accommodation center for refugees and other humanitarian disaster victims},
    volume = {140},
    year = {2018}
}

@article{Bochtis2018_Operations_Management,
    author = {Bochtis, D. and S?rensen, C.A.G. and Kateris, D.},
    journal = {Operations Management in Agriculture},
    pages = {1-240},
    title = {Operations management in agriculture},
    year = {2018}
}

@article{Del_Duchetto_2018_Learning_Local_Recovery,
    author = {Del Duchetto, Francesco and Kucukyilmaz, Ayse and Iocchi, Luca and Hanheide, Marc},
    doi = {10.1109/lra.2018.2861080},
    issn = {2377-3774},
    journal = {IEEE Robotics and Automation Letters},
    month = {October},
    number = {4},
    pages = {4084–4091},
    publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
    title = {Do Not Make the Same Mistakes Again and Again: Learning Local Recovery Policies for Navigation From Human Demonstrations},
    url = {http://dx.doi.org/10.1109/LRA.2018.2861080},
    volume = {3},
    year = {2018}
}