Rapidly-exploring Random Belief Trees for Motion Planning Under Uncertainty
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...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2011
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| Online Access: | http://hdl.handle.net/1721.1/66703 https://orcid.org/0000-0002-8293-0492 |
| Summary: | 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. |
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