Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment
This paper unifies and extends several different existing strategies for deploying groups of robots in an environment. A cost function is proposed that can be specialized to represent widely different multi-robot deployment tasks. It is shown that geometric and probabilistic deployment strategies th...
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| Format: | Article |
| Language: | en_US |
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Sage Publications
2013
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| Online Access: | http://hdl.handle.net/1721.1/79093 https://orcid.org/0000-0001-5473-3566 https://orcid.org/0000-0002-7161-7812 |
| _version_ | 1811079771688271872 |
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| author | Schwager, Mac Rus, Daniela L. Slotine, Jean-Jacques E |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Schwager, Mac Rus, Daniela L. Slotine, Jean-Jacques E |
| author_sort | Schwager, Mac |
| collection | MIT |
| description | This paper unifies and extends several different existing strategies for deploying groups of robots in an environment. A cost function is proposed that can be specialized to represent widely different multi-robot deployment tasks. It is shown that geometric and probabilistic deployment strategies that were previously seen as distinct are in fact related through this cost function, and differ only in the value of a single parameter. These strategies are also related to potential field-based controllers through the same cost function, though the relationship is not as simple. Distributed controllers are then obtained from the gradient of the cost function and are proved to converge to a local minimum of the cost function. Three special cases are derived as examples: a Voronoi-based coverage control task, a probabilistic minimum variance task, and a task using artificial potential fields. The performance of the three different controllers are compared in simulation. A result is also proved linking multi-robot deployment to non-convex optimization problems, and multi-robot consensus (i.e. all robots moving to the same point) to convex optimization problems, which implies that multi-robot deployment is inherently more difficult than multi-robot consensus. |
| first_indexed | 2024-09-23T11:20:13Z |
| format | Article |
| id | mit-1721.1/79093 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:20:13Z |
| publishDate | 2013 |
| publisher | Sage Publications |
| record_format | dspace |
| spelling | mit-1721.1/790932022-10-01T02:54:13Z Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment Schwager, Mac Rus, Daniela L. Slotine, Jean-Jacques E Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Mechanical Engineering Massachusetts Institute of Technology. Nonlinear Systems Laboratory Rus, Daniela L. Slotine, Jean-Jacques E. This paper unifies and extends several different existing strategies for deploying groups of robots in an environment. A cost function is proposed that can be specialized to represent widely different multi-robot deployment tasks. It is shown that geometric and probabilistic deployment strategies that were previously seen as distinct are in fact related through this cost function, and differ only in the value of a single parameter. These strategies are also related to potential field-based controllers through the same cost function, though the relationship is not as simple. Distributed controllers are then obtained from the gradient of the cost function and are proved to converge to a local minimum of the cost function. Three special cases are derived as examples: a Voronoi-based coverage control task, a probabilistic minimum variance task, and a task using artificial potential fields. The performance of the three different controllers are compared in simulation. A result is also proved linking multi-robot deployment to non-convex optimization problems, and multi-robot consensus (i.e. all robots moving to the same point) to convex optimization problems, which implies that multi-robot deployment is inherently more difficult than multi-robot consensus. United States. Office of Naval Research. Multidisciplinary University Research Initiative. Smarts (Grant N00014-09-1-1051) United States. Army Research Office. Multidisciplinary University Research Initiative. Scalable Swarms of Autonomous Robots and Mobile Sensors Project (Grant W911NF-05-1-0219) National Science Foundation (U.S.) (Grant IIS-0513755) National Science Foundation (U.S.) (Grant IIS-0426838) National Science Foundation (U.S.) (Grant CNS-0520305) National Science Foundation (U.S.) (Grant CNS-0707601) National Science Foundation (U.S.) (Grant EFRI-0735953) 2013-06-11T19:01:38Z 2013-06-11T19:01:38Z 2010-09 Article http://purl.org/eprint/type/JournalArticle 0278-3649 1741-3176 http://hdl.handle.net/1721.1/79093 Schwager, Mac, Daniela L. Rus, and Jean-Jacques E. Slotine. “Unifying Geometric, Probabilistic, and Potential Field Approaches to Multi-robot Deployment.” The International Journal of Robotics Research 30.3 (2010): 371–383. https://orcid.org/0000-0001-5473-3566 https://orcid.org/0000-0002-7161-7812 en_US http://dx.doi.org/10.1177/0278364910383444 The International Journal of Robotics Research Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Sage Publications MIT web domain |
| spellingShingle | Schwager, Mac Rus, Daniela L. Slotine, Jean-Jacques E Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment |
| title | Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment |
| title_full | Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment |
| title_fullStr | Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment |
| title_full_unstemmed | Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment |
| title_short | Unifying geometric, probabilistic, and potential field approaches to multi-robot deployment |
| title_sort | unifying geometric probabilistic and potential field approaches to multi robot deployment |
| url | http://hdl.handle.net/1721.1/79093 https://orcid.org/0000-0001-5473-3566 https://orcid.org/0000-0002-7161-7812 |
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