Policy search for multi-robot coordination under uncertainty
We introduce a principled method for multi-robot coordination based on a general model (termed a MacDec-POMDP) of multi-robot cooperative planning in the presence of stochasticity, uncertain sensing, and communication limitations. A new MacDec-POMDP planning algorithm is presented that searches over policies represented as finite-state controllers, rather than the previous policy tree representation. Finite-state controllers can be much more concise than trees, are much easier to interpret, and can operate over an infinite horizon. The resulting policy search algorithm requires a substantially simpler simulator that models only the outcomes of executing a given set of motor controllers, not the details of the executions themselves and can solve significantly larger problems than existing MacDec-POMDP planners. We demonstrate significant performance improvements over previous methods and show that our method can be used for actual multi-robot systems through experiments on a cooperative multi-robot bartending domain.
Duke Scholars
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Related Subject Headings
- Industrial Engineering & Automation
- 4603 Computer vision and multimedia computation
- 4007 Control engineering, mechatronics and robotics
- 0913 Mechanical Engineering
- 0906 Electrical and Electronic Engineering
- 0801 Artificial Intelligence and Image Processing
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Industrial Engineering & Automation
- 4603 Computer vision and multimedia computation
- 4007 Control engineering, mechatronics and robotics
- 0913 Mechanical Engineering
- 0906 Electrical and Electronic Engineering
- 0801 Artificial Intelligence and Image Processing