Planning for decentralized control of multiple robots under uncertainty
This paper presents a probabilistic framework for synthesizing control policies for general multi-robot systems that is based on decentralized partially observable Markov decision processes (Dec-POMDPs). Dec-POMDPs are a general model of decision-making where a team of agents must cooperate to optim...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2015
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| Online Access: | http://hdl.handle.net/1721.1/100515 https://orcid.org/0000-0002-1729-6085 https://orcid.org/0000-0002-6786-7384 https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0001-6054-7145 |
| _version_ | 1826218129690198016 |
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| author | Amato, Christopher Cruz, Gabriel Maynor, Christopher A. How, Jonathan P. Kaelbling, Leslie P. Konidaris, George D. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Amato, Christopher Cruz, Gabriel Maynor, Christopher A. How, Jonathan P. Kaelbling, Leslie P. Konidaris, George D. |
| author_sort | Amato, Christopher |
| collection | MIT |
| description | This paper presents a probabilistic framework for synthesizing control policies for general multi-robot systems that is based on decentralized partially observable Markov decision processes (Dec-POMDPs). Dec-POMDPs are a general model of decision-making where a team of agents must cooperate to optimize a shared objective in the presence of uncertainty. Dec-POMDPs also consider communication limitations, so execution is decentralized. While Dec-POMDPs are typically intractable to solve for real-world problems, recent research on the use of macro-actions in Dec-POMDPs has significantly increased the size of problem that can be practically solved. We show that, in contrast to most existing methods that are specialized to a particular problem class, our approach can synthesize control policies that exploit any opportunities for coordination that are present in the problem, while balancing uncertainty, sensor information, and information about other agents. We use three variants of a warehouse task to show that a single planner of this type can generate cooperative behavior using task allocation, direct communication, and signaling, as appropriate. This demonstrates that our algorithmic framework can automatically optimize control and communication policies for complex multi-robot systems. |
| first_indexed | 2024-09-23T17:14:39Z |
| format | Article |
| id | mit-1721.1/100515 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T17:14:39Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1005152022-09-30T00:42:12Z Planning for decentralized control of multiple robots under uncertainty Amato, Christopher Cruz, Gabriel Maynor, Christopher A. How, Jonathan P. Kaelbling, Leslie P. Konidaris, George D. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Amato, Christopher Konidaris, George D. Cruz, Gabriel Maynor, Christopher A. How, Jonathan P. Kaelbling, Leslie P. This paper presents a probabilistic framework for synthesizing control policies for general multi-robot systems that is based on decentralized partially observable Markov decision processes (Dec-POMDPs). Dec-POMDPs are a general model of decision-making where a team of agents must cooperate to optimize a shared objective in the presence of uncertainty. Dec-POMDPs also consider communication limitations, so execution is decentralized. While Dec-POMDPs are typically intractable to solve for real-world problems, recent research on the use of macro-actions in Dec-POMDPs has significantly increased the size of problem that can be practically solved. We show that, in contrast to most existing methods that are specialized to a particular problem class, our approach can synthesize control policies that exploit any opportunities for coordination that are present in the problem, while balancing uncertainty, sensor information, and information about other agents. We use three variants of a warehouse task to show that a single planner of this type can generate cooperative behavior using task allocation, direct communication, and signaling, as appropriate. This demonstrates that our algorithmic framework can automatically optimize control and communication policies for complex multi-robot systems. 2015-12-28T00:00:56Z 2015-12-28T00:00:56Z 2015-05 Article http://purl.org/eprint/type/ConferencePaper 978-1-4799-6923-4 http://hdl.handle.net/1721.1/100515 Amato, Christopher, George Konidaris, Gabriel Cruz, Christopher A. Maynor, Jonathan P. How, and Leslie P. Kaelbling. “Planning for Decentralized Control of Multiple Robots Under Uncertainty.” 2015 IEEE International Conference on Robotics and Automation (ICRA) (May 2015). https://orcid.org/0000-0002-1729-6085 https://orcid.org/0000-0002-6786-7384 https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0001-6054-7145 en_US http://dx.doi.org/10.1109/ICRA.2015.7139350 Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT web domain |
| spellingShingle | Amato, Christopher Cruz, Gabriel Maynor, Christopher A. How, Jonathan P. Kaelbling, Leslie P. Konidaris, George D. Planning for decentralized control of multiple robots under uncertainty |
| title | Planning for decentralized control of multiple robots under uncertainty |
| title_full | Planning for decentralized control of multiple robots under uncertainty |
| title_fullStr | Planning for decentralized control of multiple robots under uncertainty |
| title_full_unstemmed | Planning for decentralized control of multiple robots under uncertainty |
| title_short | Planning for decentralized control of multiple robots under uncertainty |
| title_sort | planning for decentralized control of multiple robots under uncertainty |
| url | http://hdl.handle.net/1721.1/100515 https://orcid.org/0000-0002-1729-6085 https://orcid.org/0000-0002-6786-7384 https://orcid.org/0000-0001-8576-1930 https://orcid.org/0000-0001-6054-7145 |
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