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(acceptance: 33%)\",\"day\":\"22\",\"city\":\"New Brunswick, NJ\",\"id\":\"4d126477-38d4-3fc0-8451-18bd5bbcf53e\",\"created\":\"2019-07-11T05:04:57.734Z\",\"accessed\":\"2019-07-11\",\"file_attached\":\"true\",\"profile_id\":\"0bdc2b56-796f-3d8c-b59b-624a5fa689c3\",\"last_modified\":\"2020-07-25T07:49:59.807Z\",\"read\":\"true\",\"starred\":false,\"authored\":\"true\",\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"ghassemi2019bswarm-mrs\",\"patent_owner\":\"Ghassemi, Payam\",\"notes\":\"(acceptance: 33%)\",\"private_publication\":false,\"abstract\":\"Decentralized swarm robotic solutions to searching for targets that emit a spatially varying signal promise task parallelism, time efficiency, and fault tolerance. It is, however, challenging for swarm algorithms to offer scalability and efficiency, while preserving mathematical insights into the exhibited behavior. 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 title = {Decentralized Informative Path Planning With Balanced Exploration-Exploitation for Swarm Robotic Search},
 type = {inproceedings},
 year = {2019},
 pages = {V001T02A058},
 websites = {https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658},
 month = {8},
 publisher = {American Society of Mechanical Engineers},
 day = {18},
 city = {Anaheim, CA, USA},
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 created = {2019-07-08T20:56:16.682Z},
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 citation_key = {ghassemi2019bswarm-detc},
 patent_owner = {Ghassemi, Payam},
 notes = {Identified as one of the strongest CIE 2019 papers; invited to submit to Journal of Computing and Information Science in Engineering.},
 private_publication = {false},
 abstract = {Swarm robotic search is concerned with searching targets in unknown environments (e.g., for search and rescue or hazard localization), using a large number of collaborating simple mobile robots. In such applications, decentralized swarm systems are touted for their task/coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely nature-inspired swarm heuristics and multi-robotic search methods. However, simultaneously offering computationally-efficient scalability and fundamental insights into the exhibited behavior (instead of a black-box behavior model), remains challenging under either of these two class of approaches. In this paper, we develop an important extension of the batch Bayesian search method for application to embodied swarm systems, searching in a physical 2D space. Key contributions lie in: 1) designing an acquisition function that not only balances exploration and exploitation across the swarm, but also allows modeling knowledge extraction over trajectories; and 2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Significantly superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines, along with favorable performance scalability with increasing swarm size.},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Chowdhury, Souma},
 doi = {10.1115/DETC2019-97887},
 booktitle = {Volume 1: 39th Computers and Information in Engineering Conference},
 keywords = {ADAMS,BSwarm,Swarm}
}

@inproceedings{
 title = {Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search},
 type = {inproceedings},
 year = {2019},
 keywords = {ADAMS,BSwarm,Conference,Swarm},
 pages = {188-194},
 websites = {http://arxiv.org/abs/1907.04396,https://ieeexplore.ieee.org/abstract/document/8901084/},
 month = {8},
 publisher = {Institute of Electrical and Electronics Engineers. (acceptance: 33%)},
 day = {22},
 city = {New Brunswick, NJ},
 id = {4d126477-38d4-3fc0-8451-18bd5bbcf53e},
 created = {2019-07-11T05:04:57.734Z},
 accessed = {2019-07-11},
 file_attached = {true},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-25T07:49:59.807Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2019bswarm-mrs},
 patent_owner = {Ghassemi, Payam},
 notes = {(acceptance: 33%)},
 private_publication = {false},
 abstract = {Decentralized swarm robotic solutions to searching for targets that emit a spatially varying signal promise task parallelism, time efficiency, and fault tolerance. It is, however, challenging for swarm algorithms to offer scalability and efficiency, while preserving mathematical insights into the exhibited behavior. A new decentralized search method (called Bayes-Swarm), founded on batch Bayesian Optimization (BO) principles, is presented here to address these challenges. Unlike swarm heuristics approaches, Bayes-Swarm decouples the knowledge generation and task planning process, thus preserving insights into the emergent behavior. Key contributions lie in: 1) modeling knowledge extraction over trajectories, unlike in BO; 2) time-adaptively balancing exploration/exploitation and using an efficient local penalization approach to account for potential interactions among different robots' planned samples; and 3) presenting an asynchronous implementation of the algorithm. This algorithm is tested on case studies with bimodal and highly multimodal signal distributions. Up to 76 times better efficiency is demonstrated compared to an exhaustive search baseline. The benefits of exploitation/exploration balancing, asynchronous planning, and local penalization, and scalability with swarm size, are also demonstrated.},
 bibtype = {inproceedings},
 author = {Ghassemi, Payam and Chowdhury, Souma},
 doi = {10.1109/MRS.2019.8901084},
 booktitle = {International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2019}
}

@article{
 title = {An Extended Bayesian Optimization Approach to Decentralized Swarm Robotic Search},
 type = {article},
 year = {2020},
 volume = {20},
 websites = {https://asmedigitalcollection.asme.org/computingengineering/article-abstract/doi/10.1115/1.4046587/1075826/An-Extended-Bayesian-Optimization-Approach-to?redirectedFrom=fulltext},
 month = {10},
 day = {1},
 id = {245dc51f-2b33-32f7-a83e-c237548a2f71},
 created = {2019-10-31T00:37:20.554Z},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-07-25T07:49:59.788Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {ghassemi2020bswarm-jcise},
 patent_owner = {Ghassemi, Payam},
 private_publication = {false},
 abstract = {Swarm robotic search aims at searching targets using a large number of collaborating simple mobile robots, with applications to search and rescue and hazard localization. In this regard, decentralized swarm systems are touted for their coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely, nature-inspired swarm heuristics and multi-robotic search methods. However, the ability to simultaneously achieve efficient scalability and provide fundamental insights into the exhibited behavior (as opposed to exhibiting a black-box behavior) remains an open problem. To address this problem, this paper extends the underlying search approach in batch-Bayesian optimization to perform search with embodied swarm agents operating in a (simulated) physical 2D arena. Key contributions lie in (1) designing an acquisition function that not only balances exploration and exploitation across the swarm but also allows modeling knowledge extraction over trajectories and (2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target-search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Notably, superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines as well as a swarm optimization-based state-of-the-art method. Favorable scalability characteristics are also demonstrated.},
 bibtype = {article},
 author = {Ghassemi, Payam and Chowdhury, Souma},
 doi = {10.1115/1.4046587},
 journal = {Journal of Computing and Information Science in Engineering},
 number = {5},
 keywords = {ADAMS,BSwarm,Swarm}
}

@article{
 title = {A Penalized Batch-Bayesian Approach to Informative Path Planning for Decentralized Swarm Robotic Search},
 type = {article},
 year = {2020},
 publisher = {SAGE Publications},
 id = {535b306d-b3b7-30d6-9d10-0aa545ba0515},
 created = {2020-11-18T01:22:39.978Z},
 file_attached = {false},
 profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},
 last_modified = {2020-11-22T04:48:49.746Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Ghassemi, Payam and Balazon, Mark and Chowdhury, Souma},
 journal = {The International Journal of Robotics Research},
 number = {Under Review},
 keywords = {ADAMS,BSwarm,Swarm}
}\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! 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In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ghassemi-chowdhury-anextendedbayesianoptimizationapproachtodecentralizedswarmroboticsearch-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>;  and <a class=\"bibbase author link\" href=\"https://124118259-887001909434481872.preview.editmysite.com/\">Chowdhury,  S.</a>\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://asmedigitalcollection.asme.org/computingengineering/article-abstract/doi/10.1115/1.4046587/1075826/An-Extended-Bayesian-Optimization-Approach-to?redirectedFrom=fulltext\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-chowdhury-anextendedbayesianoptimizationapproachtodecentralizedswarmroboticsearch-2020', 'https://asmedigitalcollection.asme.org/computingengineering/article-abstract/doi/10.1115/1.4046587/1075826/An-Extended-Bayesian-Optimization-Approach-to?redirectedFrom=fulltext', event);\">\n    An Extended Bayesian Optimization Approach to Decentralized Swarm Robotic Search.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  <i>Journal of Computing and Information Science in Engineering</i>, 20(5). 10 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://asmedigitalcollection.asme.org/computingengineering/article-abstract/doi/10.1115/1.4046587/1075826/An-Extended-Bayesian-Optimization-Approach-to?redirectedFrom=fulltext\"\n     onclick=\"log_download('ghassemi-chowdhury-anextendedbayesianoptimizationapproachtodecentralizedswarmroboticsearch-2020', 'https://asmedigitalcollection.asme.org/computingengineering/article-abstract/doi/10.1115/1.4046587/1075826/An-Extended-Bayesian-Optimization-Approach-to?redirectedFrom=fulltext', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"An Extended Bayesian Optimization Approach to Decentralized Swarm Robotic Search [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1115/1.4046587\"\n     onclick=\"log_download('ghassemi-chowdhury-anextendedbayesianoptimizationapproachtodecentralizedswarmroboticsearch-2020', 'http://doi.org/10.1115/1.4046587', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-chowdhury-anextendedbayesianoptimizationapproachtodecentralizedswarmroboticsearch-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-chowdhury-anextendedbayesianoptimizationapproachtodecentralizedswarmroboticsearch-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {An Extended Bayesian Optimization Approach to Decentralized Swarm Robotic Search},\n type = {article},\n year = {2020},\n volume = {20},\n websites = {https://asmedigitalcollection.asme.org/computingengineering/article-abstract/doi/10.1115/1.4046587/1075826/An-Extended-Bayesian-Optimization-Approach-to?redirectedFrom=fulltext},\n month = {10},\n day = {1},\n id = {245dc51f-2b33-32f7-a83e-c237548a2f71},\n created = {2019-10-31T00:37:20.554Z},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-25T07:49:59.788Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2020bswarm-jcise},\n patent_owner = {Ghassemi, Payam},\n private_publication = {false},\n abstract = {Swarm robotic search aims at searching targets using a large number of collaborating simple mobile robots, with applications to search and rescue and hazard localization. In this regard, decentralized swarm systems are touted for their coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely, nature-inspired swarm heuristics and multi-robotic search methods. However, the ability to simultaneously achieve efficient scalability and provide fundamental insights into the exhibited behavior (as opposed to exhibiting a black-box behavior) remains an open problem. To address this problem, this paper extends the underlying search approach in batch-Bayesian optimization to perform search with embodied swarm agents operating in a (simulated) physical 2D arena. Key contributions lie in (1) designing an acquisition function that not only balances exploration and exploitation across the swarm but also allows modeling knowledge extraction over trajectories and (2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target-search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Notably, superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines as well as a swarm optimization-based state-of-the-art method. Favorable scalability characteristics are also demonstrated.},\n bibtype = {article},\n author = {Ghassemi, Payam and Chowdhury, Souma},\n doi = {10.1115/1.4046587},\n journal = {Journal of Computing and Information Science in Engineering},\n number = {5},\n keywords = {ADAMS,BSwarm,Swarm}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Swarm robotic search aims at searching targets using a large number of collaborating simple mobile robots, with applications to search and rescue and hazard localization. In this regard, decentralized swarm systems are touted for their coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely, nature-inspired swarm heuristics and multi-robotic search methods. However, the ability to simultaneously achieve efficient scalability and provide fundamental insights into the exhibited behavior (as opposed to exhibiting a black-box behavior) remains an open problem. To address this problem, this paper extends the underlying search approach in batch-Bayesian optimization to perform search with embodied swarm agents operating in a (simulated) physical 2D arena. Key contributions lie in (1) designing an acquisition function that not only balances exploration and exploitation across the swarm but also allows modeling knowledge extraction over trajectories and (2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target-search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Notably, superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines as well as a swarm optimization-based state-of-the-art method. Favorable scalability characteristics are also demonstrated.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ghassemi-balazon-chowdhury-apenalizedbatchbayesianapproachtoinformativepathplanningfordecentralizedswarmroboticsearch-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>; Balazon,  M.;  and <a class=\"bibbase author link\" href=\"https://124118259-887001909434481872.preview.editmysite.com/\">Chowdhury,  S.</a>\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  A Penalized Batch-Bayesian Approach to Informative Path Planning for Decentralized Swarm Robotic Search.\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  <i>The International Journal of Robotics Research</i>, (Under Review).  2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-balazon-chowdhury-apenalizedbatchbayesianapproachtoinformativepathplanningfordecentralizedswarmroboticsearch-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-balazon-chowdhury-apenalizedbatchbayesianapproachtoinformativepathplanningfordecentralizedswarmroboticsearch-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A Penalized Batch-Bayesian Approach to Informative Path Planning for Decentralized Swarm Robotic Search},\n type = {article},\n year = {2020},\n publisher = {SAGE Publications},\n id = {535b306d-b3b7-30d6-9d10-0aa545ba0515},\n created = {2020-11-18T01:22:39.978Z},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-11-22T04:48:49.746Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Ghassemi, Payam and Balazon, Mark and Chowdhury, Souma},\n journal = {The International Journal of Robotics Research},\n number = {Under Review},\n keywords = {ADAMS,BSwarm,Swarm}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"ghassemi-chowdhury-decentralizedinformativepathplanningwithbalancedexplorationexploitationforswarmroboticsearch-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>;  and Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-chowdhury-decentralizedinformativepathplanningwithbalancedexplorationexploitationforswarmroboticsearch-2019', 'https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658', event);\">\n    Decentralized Informative Path Planning With Balanced Exploration-Exploitation for Swarm Robotic Search.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>Volume 1: 39th Computers and Information in Engineering Conference</i>, pages V001T02A058, 8 2019. American Society of Mechanical Engineers\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658\"\n     onclick=\"log_download('ghassemi-chowdhury-decentralizedinformativepathplanningwithbalancedexplorationexploitationforswarmroboticsearch-2019', 'https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Decentralized Informative Path Planning With Balanced Exploration-Exploitation for Swarm Robotic Search [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1115/DETC2019-97887\"\n     onclick=\"log_download('ghassemi-chowdhury-decentralizedinformativepathplanningwithbalancedexplorationexploitationforswarmroboticsearch-2019', 'http://doi.org/10.1115/DETC2019-97887', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-chowdhury-decentralizedinformativepathplanningwithbalancedexplorationexploitationforswarmroboticsearch-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-chowdhury-decentralizedinformativepathplanningwithbalancedexplorationexploitationforswarmroboticsearch-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Decentralized Informative Path Planning With Balanced Exploration-Exploitation for Swarm Robotic Search},\n type = {inproceedings},\n year = {2019},\n pages = {V001T02A058},\n websites = {https://doi.org/10.1115/DETC2019-97887,https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings/IDETC-CIE2019/59179/Anaheim, California, USA/1069658},\n month = {8},\n publisher = {American Society of Mechanical Engineers},\n day = {18},\n city = {Anaheim, CA, USA},\n id = {595b1ccf-7204-3901-9fbc-ac4ca113a8d9},\n created = {2019-07-08T20:56:16.682Z},\n accessed = {2019-07-08},\n file_attached = {false},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-25T07:49:59.592Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2019bswarm-detc},\n patent_owner = {Ghassemi, Payam},\n notes = {Identified as one of the strongest CIE 2019 papers; invited to submit to Journal of Computing and Information Science in Engineering.},\n private_publication = {false},\n abstract = {Swarm robotic search is concerned with searching targets in unknown environments (e.g., for search and rescue or hazard localization), using a large number of collaborating simple mobile robots. In such applications, decentralized swarm systems are touted for their task/coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely nature-inspired swarm heuristics and multi-robotic search methods. However, simultaneously offering computationally-efficient scalability and fundamental insights into the exhibited behavior (instead of a black-box behavior model), remains challenging under either of these two class of approaches. In this paper, we develop an important extension of the batch Bayesian search method for application to embodied swarm systems, searching in a physical 2D space. Key contributions lie in: 1) designing an acquisition function that not only balances exploration and exploitation across the swarm, but also allows modeling knowledge extraction over trajectories; and 2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Significantly superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines, along with favorable performance scalability with increasing swarm size.},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Chowdhury, Souma},\n doi = {10.1115/DETC2019-97887},\n booktitle = {Volume 1: 39th Computers and Information in Engineering Conference},\n keywords = {ADAMS,BSwarm,Swarm}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Swarm robotic search is concerned with searching targets in unknown environments (e.g., for search and rescue or hazard localization), using a large number of collaborating simple mobile robots. In such applications, decentralized swarm systems are touted for their task/coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely nature-inspired swarm heuristics and multi-robotic search methods. However, simultaneously offering computationally-efficient scalability and fundamental insights into the exhibited behavior (instead of a black-box behavior model), remains challenging under either of these two class of approaches. In this paper, we develop an important extension of the batch Bayesian search method for application to embodied swarm systems, searching in a physical 2D space. Key contributions lie in: 1) designing an acquisition function that not only balances exploration and exploitation across the swarm, but also allows modeling knowledge extraction over trajectories; and 2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Significantly superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines, along with favorable performance scalability with increasing swarm size.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://payamghassemi.weebly.com/multi-robot-task-allocation.html\">Ghassemi,  P.</a>;  and Chowdhury,  S.\n</span>\n\n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/74f09d67-a3ab-9e8f-24ca-4513fced092a/ghassemi2019bswarm_mrs.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/74f09d67-a3ab-9e8f-24ca-4513fced092a/ghassemi2019bswarm_mrs.pdf.pdf', event);\">\n    Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search.\n  </a>\n  \n  \n  \n</span>\n\n  \n\n</span>\n\n  In <i>International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2019</i>, pages 188-194, 8 2019. Institute of Electrical and Electronics Engineers. (acceptance: 33%)\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/74f09d67-a3ab-9e8f-24ca-4513fced092a/ghassemi2019bswarm_mrs.pdf.pdf\"\n     onclick=\"log_download('ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019', 'https://bibbase.org/service/mendeley/0bdc2b56-796f-3d8c-b59b-624a5fa689c3/file/74f09d67-a3ab-9e8f-24ca-4513fced092a/ghassemi2019bswarm_mrs.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"http://arxiv.org/abs/1907.04396,https://ieeexplore.ieee.org/abstract/document/8901084/\"\n     onclick=\"log_download('ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019', 'http://arxiv.org/abs/1907.04396,https://ieeexplore.ieee.org/abstract/document/8901084/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/MRS.2019.8901084\"\n     onclick=\"log_download('ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019', 'http://doi.org/10.1109/MRS.2019.8901084', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ghassemi-chowdhury-informativepathplanningwithlocalpenalizationfordecentralizedandasynchronousswarmroboticsearch-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search},\n type = {inproceedings},\n year = {2019},\n keywords = {ADAMS,BSwarm,Conference,Swarm},\n pages = {188-194},\n websites = {http://arxiv.org/abs/1907.04396,https://ieeexplore.ieee.org/abstract/document/8901084/},\n month = {8},\n publisher = {Institute of Electrical and Electronics Engineers. (acceptance: 33%)},\n day = {22},\n city = {New Brunswick, NJ},\n id = {4d126477-38d4-3fc0-8451-18bd5bbcf53e},\n created = {2019-07-11T05:04:57.734Z},\n accessed = {2019-07-11},\n file_attached = {true},\n profile_id = {0bdc2b56-796f-3d8c-b59b-624a5fa689c3},\n last_modified = {2020-07-25T07:49:59.807Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {ghassemi2019bswarm-mrs},\n patent_owner = {Ghassemi, Payam},\n notes = {(acceptance: 33%)},\n private_publication = {false},\n abstract = {Decentralized swarm robotic solutions to searching for targets that emit a spatially varying signal promise task parallelism, time efficiency, and fault tolerance. It is, however, challenging for swarm algorithms to offer scalability and efficiency, while preserving mathematical insights into the exhibited behavior. A new decentralized search method (called Bayes-Swarm), founded on batch Bayesian Optimization (BO) principles, is presented here to address these challenges. Unlike swarm heuristics approaches, Bayes-Swarm decouples the knowledge generation and task planning process, thus preserving insights into the emergent behavior. Key contributions lie in: 1) modeling knowledge extraction over trajectories, unlike in BO; 2) time-adaptively balancing exploration/exploitation and using an efficient local penalization approach to account for potential interactions among different robots&#x27; planned samples; and 3) presenting an asynchronous implementation of the algorithm. This algorithm is tested on case studies with bimodal and highly multimodal signal distributions. Up to 76 times better efficiency is demonstrated compared to an exhaustive search baseline. The benefits of exploitation/exploration balancing, asynchronous planning, and local penalization, and scalability with swarm size, are also demonstrated.},\n bibtype = {inproceedings},\n author = {Ghassemi, Payam and Chowdhury, Souma},\n doi = {10.1109/MRS.2019.8901084},\n booktitle = {International Symposium on Multi-Robot and Multi-Agent Systems, MRS 2019}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Decentralized swarm robotic solutions to searching for targets that emit a spatially varying signal promise task parallelism, time efficiency, and fault tolerance. It is, however, challenging for swarm algorithms to offer scalability and efficiency, while preserving mathematical insights into the exhibited behavior. A new decentralized search method (called Bayes-Swarm), founded on batch Bayesian Optimization (BO) principles, is presented here to address these challenges. Unlike swarm heuristics approaches, Bayes-Swarm decouples the knowledge generation and task planning process, thus preserving insights into the emergent behavior. Key contributions lie in: 1) modeling knowledge extraction over trajectories, unlike in BO; 2) time-adaptively balancing exploration/exploitation and using an efficient local penalization approach to account for potential interactions among different robots' planned samples; and 3) presenting an asynchronous implementation of the algorithm. This algorithm is tested on case studies with bimodal and highly multimodal signal distributions. Up to 76 times better efficiency is demonstrated compared to an exhaustive search baseline. The benefits of exploitation/exploration balancing, asynchronous planning, and local penalization, and scalability with swarm size, are also demonstrated.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);