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Rapidly-exploring Random Belief Trees for Motion Planning Under Uncertainty

Author(s)
Bry, Adam P.
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Abstract
In this paper we address the problem of motion planning in the presence of state uncertainty, also known as planning in belief space. The work is motivated by planning domains involving nontrivial dynamics, spatially varying measurement properties, and obstacle constraints. To make the problem tractable, we restrict the motion plan to a nominal trajectory stabilized with a linear estimator and controller. This allows us to predict distributions over future states given a candidate nominal trajectory. Using these distributions to ensure a bounded probability of collision, the algorithm incrementally constructs a graph of trajectories through state space, while efficiently searching over candidate paths through the graph at each iteration. This process results in a search tree in belief space that provably converges to the optimal path. We analyze the algorithm theoretically and also provide simulation results demonstrating its utility for balancing information gathering to reduce uncertainty and finding low cost paths.
Date issued
2011-05
URI
http://hdl.handle.net/1721.1/66703
Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Journal
Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA 2011)
Publisher
Institute of Electrical and Electronics Engineers
Citation
Bry, Adam and Nicholas Roy. "Rapidly-exploring Random Belief Trees for Motion Planning Under Uncertainty." In Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA 2011), May 9-13, 2011, Shanghai International Convention Center, Shanghai, China.
Version: Author's final manuscript

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