Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory
IEEE Controlling large swarms of robotic agents presents many challenges including, but not limited to, computational complexity due to a large number of agents, uncertainty in the functionality of each agent in the swarm, and uncertainty in the swarm's configuration. The contribution of this w...
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/135348 |
| _version_ | 1826203618463711232 |
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| author | Doerr, Bryce Linares, Richard |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Doerr, Bryce Linares, Richard |
| author_sort | Doerr, Bryce |
| collection | MIT |
| description | IEEE Controlling large swarms of robotic agents presents many challenges including, but not limited to, computational complexity due to a large number of agents, uncertainty in the functionality of each agent in the swarm, and uncertainty in the swarm's configuration. The contribution of this work is to decentralize Random Finite Set (RFS)-based control of large collaborative swarms for controlling individual agents. The RFS-based control formulation assumes a Gaussian Mixture Probability Hypothesis Density (GM-PHD) approximation and a complete topology for centralized swarm control. To generalize the control topology in a localized or decentralized manner, sparse LQR is used to sparsify the RFS-based control gain matrix obtained using iterative LQR. This allows agents to use information of agents near each other (localized topology) or only the agent's own information (decentralized topology) to make a control decision. Sparsity and performance for decentralized RFS-based control are compared for different degrees of localization in feedback control gains which show that the stability and performance compared to centralized control do not degrade significantly in provi |
| first_indexed | 2024-09-23T12:40:08Z |
| format | Article |
| id | mit-1721.1/135348 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:40:08Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1353482023-02-23T15:57:12Z Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory Doerr, Bryce Linares, Richard Massachusetts Institute of Technology. Department of Aeronautics and Astronautics IEEE Controlling large swarms of robotic agents presents many challenges including, but not limited to, computational complexity due to a large number of agents, uncertainty in the functionality of each agent in the swarm, and uncertainty in the swarm's configuration. The contribution of this work is to decentralize Random Finite Set (RFS)-based control of large collaborative swarms for controlling individual agents. The RFS-based control formulation assumes a Gaussian Mixture Probability Hypothesis Density (GM-PHD) approximation and a complete topology for centralized swarm control. To generalize the control topology in a localized or decentralized manner, sparse LQR is used to sparsify the RFS-based control gain matrix obtained using iterative LQR. This allows agents to use information of agents near each other (localized topology) or only the agent's own information (decentralized topology) to make a control decision. Sparsity and performance for decentralized RFS-based control are compared for different degrees of localization in feedback control gains which show that the stability and performance compared to centralized control do not degrade significantly in provi 2021-10-27T20:23:04Z 2021-10-27T20:23:04Z 2021 2021-05-06T15:41:11Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135348 en 10.1109/TCNS.2021.3059793 IEEE Transactions on Control of Network Systems Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Doerr, Bryce Linares, Richard Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory |
| title | Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory |
| title_full | Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory |
| title_fullStr | Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory |
| title_full_unstemmed | Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory |
| title_short | Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory |
| title_sort | decentralized control of large collaborative swarms using random finite set theory |
| url | https://hdl.handle.net/1721.1/135348 |
| work_keys_str_mv | AT doerrbryce decentralizedcontroloflargecollaborativeswarmsusingrandomfinitesettheory AT linaresrichard decentralizedcontroloflargecollaborativeswarmsusingrandomfinitesettheory |