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\n  \n 2023\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n A blockchain-based information market to incentivise cooperation in swarms of self-interested robots.\n \n \n \n \n\n\n \n Van Calck, L.; Pacheco, A.; Strobel, V.; Dorigo, M.; and Reina, A.\n\n\n \n\n\n\n Scientific Reports, 13: 20417. 2023.\n \n\n\n\n
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@article{VanPacStr-etal2023:SciRep,\nauthor = {Van Calck, Lud{\\'e}ric and Pacheco, Alexandre and Strobel, Volker and Dorigo, Marco and Reina, Andreagiovanni},\njournal = {Scientific Reports},\ntitle = {A blockchain-based information market to incentivise cooperation in swarms of self-interested robots},\nvolume = {13},\npages = {20417},\nyear = {2023},\ndoi = {10.1038/s41598-023-46238-1},\nurl_PDF = {http://iridia.ulb.ac.be/~vstrobel/articles/VanPacStr-etal2023scirep.pdf}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n A Generic Framework for Byzantine-tolerant Consensus Achievement in Robot Swarms.\n \n \n \n \n\n\n \n Zhao, H.; Pacheco, A.; Strobel, V.; Reina, A.; Liu, X.; Dudek, G.; and Dorigo, M.\n\n\n \n\n\n\n In Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023), pages 8839–8846, 2023. IEEE Press\n \n\n\n\n
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@inproceedings{ZhaPacStr-etal2023:iros,\nauthor = {H. Zhao and A. Pacheco and V. Strobel and A. Reina and X. Liu and G. Dudek and M. Dorigo},\nbooktitle = {Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)},\npublisher = {IEEE Press},\ntitle = {A Generic Framework for Byzantine-tolerant Consensus Achievement in Robot Swarms},\npages = {8839--8846},\nyear = {2023},\nurl_PDF = {http://iridia.ulb.ac.be/~vstrobel/articles/ZhaPacStr-etal2023iros.pdf}\n}\n\n\n
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\n \n\n \n \n \n \n \n \n Robot swarms neutralize harmful Byzantine robots using a blockchain-based token economy.\n \n \n \n \n\n\n \n Strobel, V.; Pacheco, A.; and Dorigo, M.\n\n\n \n\n\n\n Science Robotics, 8(79): eabm4636. 2023.\n \n\n\n\n
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@article{StrPacDor2023:scirobotics,\nauthor = {Volker Strobel and Alexandre Pacheco and Marco Dorigo},\ntitle = {Robot swarms neutralize harmful {B}yzantine robots using a blockchain-based token economy},\njournal = {Science Robotics},\nvolume = {8},\nnumber = {79},\npages = {eabm4636},\nyear = {2023},\ndoi = {10.1126/scirobotics.abm4636},\nurl_PDF = {http://iridia.ulb.ac.be/~vstrobel/articles/StrPacDor2023scirobotics.pdf},\nabstract = {Through cooperation, robot swarms can perform tasks or solve problems that a single robot from the swarm could not perform/solve by itself. However, it has been shown that a single Byzantine robot (such as a malfunctioning or malicious robot) can disrupt the coordination strategy of the entire swarm. Therefore, a versatile swarm robotics framework that addresses security issues in inter-robot communication and coordination is urgently needed. Here, we show that security issues can be addressed by setting up a token economy between the robots. To create and maintain the token economy, we used blockchain technology, originally developed for the digital currency Bitcoin. The robots were given crypto tokens that allowed them to participate in the swarm’s security-critical activities. The token economy was regulated via a smart contract that decided how to distribute crypto tokens among the robots depending on their contributions. We designed the smart contract so that Byzantine robots soon ran out of crypto tokens and could therefore no longer influence the rest of the swarm. In experiments with up to 24 physical robots, we demonstrated that our smart contract approach worked: The robots could maintain blockchain networks, and a blockchain-based token economy could be used to neutralize the destructive actions of Byzantine robots in a collective-sensing scenario. In experiments with more than 100 simulated robots, we studied the scalability and long-term behavior of our approach. The obtained results demonstrate the feasibility and viability of blockchain-based swarm robotics. A token economy implemented via blockchain-based smart contracts allowed robot swarms to neutralize harmful Byzantine robots.}}\n\n
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\n Through cooperation, robot swarms can perform tasks or solve problems that a single robot from the swarm could not perform/solve by itself. However, it has been shown that a single Byzantine robot (such as a malfunctioning or malicious robot) can disrupt the coordination strategy of the entire swarm. Therefore, a versatile swarm robotics framework that addresses security issues in inter-robot communication and coordination is urgently needed. Here, we show that security issues can be addressed by setting up a token economy between the robots. To create and maintain the token economy, we used blockchain technology, originally developed for the digital currency Bitcoin. The robots were given crypto tokens that allowed them to participate in the swarm’s security-critical activities. The token economy was regulated via a smart contract that decided how to distribute crypto tokens among the robots depending on their contributions. We designed the smart contract so that Byzantine robots soon ran out of crypto tokens and could therefore no longer influence the rest of the swarm. In experiments with up to 24 physical robots, we demonstrated that our smart contract approach worked: The robots could maintain blockchain networks, and a blockchain-based token economy could be used to neutralize the destructive actions of Byzantine robots in a collective-sensing scenario. In experiments with more than 100 simulated robots, we studied the scalability and long-term behavior of our approach. The obtained results demonstrate the feasibility and viability of blockchain-based swarm robotics. A token economy implemented via blockchain-based smart contracts allowed robot swarms to neutralize harmful Byzantine robots.\n
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\n \n\n \n \n \n \n \n \n Real-time coordination of a foraging robot swarm using blockchain smart contracts.\n \n \n \n \n\n\n \n Pacheco, A.; Strobel, V.; Reina, A.; and Dorigo, M.\n\n\n \n\n\n\n In Dorigo, M.; Hamann, H.; López-Ibáñez, M.; García-Nieto, J.; Engelbrecht, A.; Pinciroli, C.; Strobel, V.; and Camacho-Villalón, C., editor(s), Proceedings of the Thirteenth International Conference on Swarm Intelligence (ANTS 2022), volume 13491, of LNCS, pages 196–208, Cham, Switzerland, 2022. Springer\n \n\n\n\n
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@inProceedings{PacStrReiDor2022:ants,\n   author = {Pacheco, Alexandre and Strobel, Volker and Reina, Andreagiovanni and Dorigo, Marco},\n   editor = {Dorigo, Marco and Hamann, Heiko and L\\'{o}pez-Ib\\'{a}\\~{n}ez, Manuel and Garc\\'{\\i}a-Nieto, Jos\\'{e} and Engelbrecht, Andries and Pinciroli, Carlo and Strobel, Volker and Camacho-Villal\\'{o}n, Christian},   \n   title = {Real-time coordination of a foraging robot swarm using blockchain smart contracts},\n   booktitle = {Proceedings of the Thirteenth International Conference on Swarm Intelligence (ANTS 2022)},   \n   pages = {196--208},\n   series = {LNCS},\n   volume = {13491},\n   publisher = {Springer},\n   address = {Cham, Switzerland},\n   year = {2022},\n   doi = {10.1007/978-3-031-20176-9_16},\n   url_PDF = {http://iridia.ulb.ac.be/~vstrobel/articles/PacStrReiDor2022ants.pdf},   \n}\n\n\n
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\n \n\n \n \n \n \n \n \n A Blockchain-Controlled Physical Robot Swarm Communicating via an Ad-Hoc Network.\n \n \n \n \n\n\n \n Pacheco, A.; Strobel, V.; and Dorigo, M.\n\n\n \n\n\n\n In Dorigo, M.; Stützle, T.; Blesa, M. J.; Blum, C.; Hamann, H.; Heinrich, M. K.; and Strobel, V., editor(s), Swarm Intelligence – Proceedings of ANTS 2020 – Twelfth International Conference, volume 12421, of LNCS, pages 3–15, Cham, Switzerland, 2020. Springer\n \n\n\n\n
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@InProceedings{PacStrDor2020:ants,\n  author={Pacheco, Alexandre and Strobel, Volker and Dorigo, Marco},\n  title={A Blockchain-Controlled Physical Robot Swarm Communicating via an Ad-Hoc Network},\n  booktitle={Swarm Intelligence -- Proceedings of ANTS 2020 -- Twelfth International Conference},\n  editor={Dorigo, Marco and St\\"{u}tzle, Thomas and Blesa, Maria J. and Blum, Christian and Hamann, Heiko and Heinrich, Mary Katherine and Strobel, Volker},\n  year={2020},\n  pages={3--15},\n  series={LNCS},\n  volume={12421},\n  publisher={Springer},  \n  address = {Cham, Switzerland},  \n  doi={10.1007/978-3-030-60376-2_1},\n  url_PDF = {http://iridia.ulb.ac.be/~vstrobel/articles/PacStrDor2020ants.pdf},     \n}\n\n\n
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\n \n\n \n \n \n \n \n \n Blockchain Technology Secures Robot Swarms: A Comparison of Consensus Protocols and Their Resilience to Byzantine Robots.\n \n \n \n \n\n\n \n Strobel, V.; Castelló Ferrer, E.; and Dorigo, M.\n\n\n \n\n\n\n Frontiers in Robotics and AI, 7: 54. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"Blockchain pdf\n  \n \n \n \"BlockchainPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{StrCasDor2020:frontiers,\n  author={Strobel, Volker and Castell\\'o Ferrer, Eduardo and Dorigo, Marco},\n  title={Blockchain Technology Secures Robot Swarms: {A} Comparison of Consensus Protocols and Their Resilience to {B}yzantine Robots},\n  journal={Frontiers in Robotics and AI},\n  volume={7},\n  pages={54},\n  year={2020},\n  url_PDF = {http://iridia.ulb.ac.be/~vstrobel/articles/StrCasDor2020frontiers.pdf},\n  url={https://www.frontiersin.org/article/10.3389/frobt.2020.00054},\n  doi={10.3389/frobt.2020.00054},\n  issn={2296-9144},\n  abstract={Consensus achievement is a crucial capability for robot swarms, for example, for path selection, spatial aggregation, or collective sensing. However, the presence of malfunctioning and malicious robots (Byzantine robots) can make it impossible to achieve consensus using classical consensus protocols. In this work, we show how a swarm of robots can achieve consensus even in the presence of Byzantine robots by exploiting blockchain technology. Bitcoin and later blockchain frameworks, such as Ethereum, have revolutionized financial transactions. These frameworks are based on decentralized databases (blockchains) that can achieve secure consensus in peer-to-peer networks. We illustrate our approach in a collective sensing scenario where robots in a swarm are controlled via blockchain-based smart contracts (decentralized protocols executed via blockchain technology) that serve as “meta-controllers” and we compare it to state-of-the-art consensus protocols using a robot swarm simulator. Additionally, we show that our blockchain-based approach can prevent attacks where robots forge a large number of identities (Sybil attacks). The developed robot-blockchain interface is released as open-source software in order to facilitate future research in blockchain-controlled robot swarms. Besides increasing security, we expect the presented approach to be important for data analysis, digital forensics, and robot-to-robot financial transactions in robot swarms.}\n}\n\n\n
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\n Consensus achievement is a crucial capability for robot swarms, for example, for path selection, spatial aggregation, or collective sensing. However, the presence of malfunctioning and malicious robots (Byzantine robots) can make it impossible to achieve consensus using classical consensus protocols. In this work, we show how a swarm of robots can achieve consensus even in the presence of Byzantine robots by exploiting blockchain technology. Bitcoin and later blockchain frameworks, such as Ethereum, have revolutionized financial transactions. These frameworks are based on decentralized databases (blockchains) that can achieve secure consensus in peer-to-peer networks. We illustrate our approach in a collective sensing scenario where robots in a swarm are controlled via blockchain-based smart contracts (decentralized protocols executed via blockchain technology) that serve as “meta-controllers” and we compare it to state-of-the-art consensus protocols using a robot swarm simulator. Additionally, we show that our blockchain-based approach can prevent attacks where robots forge a large number of identities (Sybil attacks). The developed robot-blockchain interface is released as open-source software in order to facilitate future research in blockchain-controlled robot swarms. Besides increasing security, we expect the presented approach to be important for data analysis, digital forensics, and robot-to-robot financial transactions in robot swarms.\n
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\n \n\n \n \n \n \n \n \n MyPDDL: Tools for Efficiently Creating PDDL Domains and Problems.\n \n \n \n \n\n\n \n Strobel, V.; and Kirsch, A.\n\n\n \n\n\n\n In Vallati, M.; and Kitchin, D., editor(s), Knowledge Engineering Tools and Techniques for AI Planning, pages 67–90. Springer, Cham, Switzerland, 2020.\n \n\n\n\n
\n\n\n\n \n \n \"MyPDDL: pdf\n  \n \n \n \"MyPDDL:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@incollection{StrKir2020:bookchapter,\n  title="{MyPDDL}: Tools for Efficiently Creating {PDDL} Domains and Problems",\n  author="Strobel, Volker and Kirsch, Alexandra",\n  editor="Vallati, Mauro and Kitchin, Diane",\n  booktitle="Knowledge Engineering Tools and Techniques for AI Planning",\n  year="2020",\n  publisher="Springer",\n  address="Cham, Switzerland",\n  pages="67--90",\n  abstract="The Planning Domain Definition Language (PDDL) is the state-of-the-art language for specifying planning problems in artificial intelligence research. Writing and maintaining these planning problems, however, can be time-consuming and error- prone. To address this issue, we present myPDDL---a modular toolkit for developing and manipulating PDDL domains and problems. To evaluate myPDDL, we compare its features to existing knowledge engineering tools for PDDL. In a user test, we additionally assess two of its modules, namely the syntax highlighting feature and the type diagram generator. The users of syntax highlighting detected 36{\\%} more errors than non-users in an erroneous domain file. The average time on task for questions on a PDDL type hierarchy was reduced by 48{\\%} when making the type diagram generator available. This implies that myPDDL can support knowledge engineers well in the PDDL design and analysis process.",\n  isbn="978-3-030-38561-3",\n  url_PDF = {http://iridia.ulb.ac.be/~vstrobel/articles/StrKir2020bookchapter.pdf},     \n  url="https://doi.org/10.1007/978-3-030-38561-3_4",\n  doi="10.1007/978-3-030-38561-3_4",\n\n}\n\n
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\n The Planning Domain Definition Language (PDDL) is the state-of-the-art language for specifying planning problems in artificial intelligence research. Writing and maintaining these planning problems, however, can be time-consuming and error- prone. To address this issue, we present myPDDL—a modular toolkit for developing and manipulating PDDL domains and problems. To evaluate myPDDL, we compare its features to existing knowledge engineering tools for PDDL. In a user test, we additionally assess two of its modules, namely the syntax highlighting feature and the type diagram generator. The users of syntax highlighting detected 36% more errors than non-users in an erroneous domain file. The average time on task for questions on a PDDL type hierarchy was reduced by 48% when making the type diagram generator available. This implies that myPDDL can support knowledge engineers well in the PDDL design and analysis process.\n
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\n \n\n \n \n \n \n \n \n A Framework for Swarm Robotics Experimentation with Pi-puck Robots and an Ethereum-based Blockchain.\n \n \n \n \n\n\n \n Pacheco, A.; Strobel, V.; and Dorigo, M.\n\n\n \n\n\n\n Technical Report TR/IRIDIA/2020-001, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium, February 2020.\n \n\n\n\n
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@techreport{PacStrDor2020:techreport-001,\n  author={Alexandre Pacheco and Volker Strobel and Marco Dorigo},\n  title={A Framework for Swarm Robotics Experimentation with Pi-puck Robots and an Ethereum-based Blockchain},\n  institution={IRIDIA, Universit{\\'e} Libre de Bruxelles},\n  year={2020},\n  number={TR/IRIDIA/2020-001},\n  address={Brussels, Belgium},\n  month={February},\n  url_PDF={http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2020-001.pdf},\n  url={http://iridia.ulb.ac.be/IridiaTrSeries/history.php?tryear=2020&trsnum=001}  \n}\n\n
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\n \n\n \n \n \n \n \n \n Blockchain Technology for Robot Swarms: A Shared Knowledge and Reputation Management System for Collective Estimation.\n \n \n \n \n\n\n \n Strobel, V.; and Dorigo, M.\n\n\n \n\n\n\n In Dorigo, M.; Birattari, M.; Blum, C.; Christensen, A. L.; Reina, A.; and Trianni, V., editor(s), Swarm Intelligence – Proceedings of ANTS 2018 – Eleventh International Conference, volume 11172, of LNCS, pages 425–426, Cham, Switzerland, 2018. Springer\n \n\n\n\n
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@inproceedings{StrDor2018:ants,\n  author = {Strobel, Volker and Dorigo, Marco},\n  editor = {Dorigo, Marco and Birattari, Mauro and Blum, Christian and Christensen, Anders L. and Reina, Andreagiovanni and Trianni, Vito},\n  title = {Blockchain Technology for Robot Swarms: {A} Shared Knowledge and Reputation Management System for Collective Estimation},\n  year = {2018},\n  booktitle = {Swarm Intelligence -- Proceedings of ANTS 2018 -- Eleventh International Conference},\n  volume = {11172},\n  series = {LNCS},\n  pages = {425--426},\n  publisher = {Springer},\n  url_PDF = {http://iridia.ulb.ac.be/~vstrobel/articles/StrDor2018ants.pdf},\n  url = {https://link.springer.com/book/10.1007/978-3-030-00533-7},\n  address = {Cham, Switzerland}\n}\n\n
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\n \n\n \n \n \n \n \n \n Managing Byzantine Robots via Blockchain Technology in a Swarm Robotics Collective Decision Making Scenario.\n \n \n \n \n\n\n \n Strobel, V.; Castelló Ferrer, E.; and Dorigo, M.\n\n\n \n\n\n\n In Dastani, M.; Sukthankar, G.; André, E.; and Koenig, S., editor(s), Proceedings of 17th International Conference on Autonomous Agents and MultiAgent Systems, of AAMAS '18, pages 541–549, Richland, SC, 2018. International Foundation for Autonomous Agents and Multiagent Systems\n \n\n\n\n
\n\n\n\n \n \n \"Managing pdf\n  \n \n \n \"ManagingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{StrCasDor2018:aamas,\n  author = {Strobel, Volker and Castell{\\'o} Ferrer, Eduardo and Dorigo, Marco},\n  editor={Dastani, M. and Sukthankar, G. and Andr\\'e, E. and Koenig, S.},\n  title = {Managing {B}yzantine Robots via Blockchain Technology in a Swarm Robotics Collective Decision Making Scenario},\n  year = {2018},\n  booktitle= {Proceedings of 17th International Conference on Autonomous Agents and MultiAgent Systems},\n  series = {AAMAS '18},\n  pages={541--549},\n  address = {Richland, SC},\n  publisher = {International Foundation for Autonomous Agents and Multiagent Systems},\n  url_PDF = {http://iridia.ulb.ac.be/~vstrobel/articles/StrCasDor2018aamas.pdf},\n  url = {https://dl.acm.org/citation.cfm?id=3237464},\n  abstract = {While swarm robotics systems are often claimed to be highly fault-tolerant, so far research has limited its attention to safe laboratory settings and has virtually ignored security issues in the presence of Byzantine robots---i.e., robots with arbitrarily faulty or malicious behavior. However, in many applications one or more Byzantine robots may suffice to let current swarm coordination mechanisms fail with unpredictable or disastrous outcomes. In this paper, we provide a proof-of-concept for managing security issues in swarm robotics systems via blockchain technology. Our approach uses decentralized programs executed via blockchain technology (blockchain-based smart contracts) to establish secure swarm coordination mechanisms and to identify and exclude Byzantine swarm members. We studied the performance of our blockchain-based approach in a collective decision-making scenario both in the presence and absence of Byzantine robots and compared our results to those obtained with an existing collective decision approach. The results show a clear advantage of the blockchain approach when Byzantine robots are part of the swarm.}\n}\n\n
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\n While swarm robotics systems are often claimed to be highly fault-tolerant, so far research has limited its attention to safe laboratory settings and has virtually ignored security issues in the presence of Byzantine robots—i.e., robots with arbitrarily faulty or malicious behavior. However, in many applications one or more Byzantine robots may suffice to let current swarm coordination mechanisms fail with unpredictable or disastrous outcomes. In this paper, we provide a proof-of-concept for managing security issues in swarm robotics systems via blockchain technology. Our approach uses decentralized programs executed via blockchain technology (blockchain-based smart contracts) to establish secure swarm coordination mechanisms and to identify and exclude Byzantine swarm members. We studied the performance of our blockchain-based approach in a collective decision-making scenario both in the presence and absence of Byzantine robots and compared our results to those obtained with an existing collective decision approach. The results show a clear advantage of the blockchain approach when Byzantine robots are part of the swarm.\n
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\n \n\n \n \n \n \n \n \n Efficient Global Indoor Localization for Micro Aerial Vehicles.\n \n \n \n \n\n\n \n Strobel, V.; Meertens, R.; and de Croon, G. C. H. E.\n\n\n \n\n\n\n In Moschetta, J.; Hattenberger, G; and de Plinval, H., editor(s), The 9th International Micro Air Vehicle Conference and Flight Competition (IMAV 2017), pages 270–277, Toulouse, France, 2017. \n \n\n\n\n
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@inproceedings{StrMeedeC2017:imav,\n  author = {Strobel, Volker and Meertens, Roland and de Croon, G. C. H. E.},\n  editor = {Moschetta, J.-M. and Hattenberger, G and de Plinval, H.},\n  title = {Efficient Global Indoor Localization for Micro Aerial Vehicles},\n  year = {2017},\n  booktitle = {The 9th International Micro Air Vehicle Conference and Flight Competition (IMAV 2017)},\n  pages = {270--277},\n  address = {Toulouse, France},\n  url_PDF = {http://iridia.ulb.ac.be/~vstrobel/articles/StrMeedeC2017imav.pdf},\n  url = {http://www.imavs.org/imav2017-proceedings/},\n  abstract = {Indoor localization for autonomous micro aerial vehicles (MAVs) requires specific localization techniques, since the Global Positioning System (GPS) is usually not available. We present an efficient onboard computer vision approach that estimates 2D positions of an MAV in real-time. This global localization system does not suffer from error accumulation over time and uses a k-Nearest Neighbors (k-NN) algorithm to predict positions based on textons---small characteristic image patches that capture the texture of an environment. A particle filter aggregates the estimates and resolves positional ambiguities. To predict the performance of the approach in a given setting, we developed an evaluation technique that compares environments and identifies critical areas within them. We conducted flight tests to demonstrate the applicability of our approach. The algorithm has a localization accuracy of approximately 0.6 m on a 5 m x 5 m area at a runtime of 32 ms on board of an MAV. Based on random sampling, its computational effort is scalable to different platforms, trading off speed and accuracy.}\n}\n\n
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\n Indoor localization for autonomous micro aerial vehicles (MAVs) requires specific localization techniques, since the Global Positioning System (GPS) is usually not available. We present an efficient onboard computer vision approach that estimates 2D positions of an MAV in real-time. This global localization system does not suffer from error accumulation over time and uses a k-Nearest Neighbors (k-NN) algorithm to predict positions based on textons—small characteristic image patches that capture the texture of an environment. A particle filter aggregates the estimates and resolves positional ambiguities. To predict the performance of the approach in a given setting, we developed an evaluation technique that compares environments and identifies critical areas within them. We conducted flight tests to demonstrate the applicability of our approach. The algorithm has a localization accuracy of approximately 0.6 m on a 5 m x 5 m area at a runtime of 32 ms on board of an MAV. Based on random sampling, its computational effort is scalable to different platforms, trading off speed and accuracy.\n
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\n \n\n \n \n \n \n \n \n Planning in the Wild: Modeling Tools for PDDL.\n \n \n \n \n\n\n \n Strobel, V.; and Kirsch, A.\n\n\n \n\n\n\n In Lutz, C.; and Thielscher, M., editor(s), KI 2014: Advances in Artificial Intelligence, volume 8736, of LNCS, pages 273–284. Springer, Cham, Switzerland, 2014.\n \n\n\n\n
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@incollection{StrKir2014:aai,\n  author = {Strobel, Volker and Kirsch, Alexandra},\n  editor = {Lutz, Carsten and Thielscher, Michael},\n  title = {Planning in the Wild: {M}odeling Tools for {PDDL}},\n  booktitle = {KI 2014: Advances in Artificial Intelligence},\n  pages = {273--284},\n  series = {LNCS},\n  volume = {8736},\n  publisher = {Springer},\n  year = {2014},\n  address = {Cham, Switzerland},\n  url_PDF = {http://iridia.ulb.ac.be/~vstrobel/articles/StrKir2014aai.pdf},\n  url = {https://link.springer.com/chapter/10.1007%2F978-3-319-11206-0_27},\n  doi = {10.1007/978-3-319-11206-0_27},\n  abstract = {Even though there are sophisticated AI planning algorithms, many integrated, large-scale projects do not use planning. One reason seems to be the missing support by engineering tools such as syntax highlighting and visualization. We propose myPDDL---a modular toolbox for efficiently creating PDDL domains and problems. To evaluate myPDDL, we compare it to existing knowledge engineering tools for PDDL and experimentally assess its usefulness for novice PDDL users.}\n}\n
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\n Even though there are sophisticated AI planning algorithms, many integrated, large-scale projects do not use planning. One reason seems to be the missing support by engineering tools such as syntax highlighting and visualization. We propose myPDDL—a modular toolbox for efficiently creating PDDL domains and problems. To evaluate myPDDL, we compare it to existing knowledge engineering tools for PDDL and experimentally assess its usefulness for novice PDDL users.\n
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