Topology-guided path integral approach for stochastic optimal control in cluttered environment

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This paper addresses planning and control of robot motion under uncertainty that is formulated as a continuous-time, continuous-space stochastic optimal control problem, by developing a topology-guided path integral control method. The path integral control framework, which forms the backbone of the proposed method, re-writes the Hamilton-Jacobi-Bellman equation as a statistical inference problem; the resulting inference problem is solved by a sampling procedure that computes the distribution of controlled trajectories around the trajectory by the passive dynamics. For motion control of robots in a highly cluttered environment, however, this sampling can easily be trapped in a local minimum unless the sample size is very large, since the global optimality of local minima depends on the degree of uncertainty. Thus, a homology-embedded sampling-based planner that identifies many (potentially) local-minimum trajectories in different homology classes is developed to aid the sampling process. In combination with a receding-horizon fashion of the optimal control the proposed method produces a dynamically feasible and collision-free motion plans without being trapped in a local minimum. Numerical examples on a synthetic toy problem and on quadrotor control in a complex obstacle field demonstrate the validity of the proposed method. (C) 2019 Published by Elsevier B.V.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2019-03
Language
English
Article Type
Article
Citation

ROBOTICS AND AUTONOMOUS SYSTEMS, v.113, pp.81 - 93

ISSN
0921-8890
DOI
10.1016/j.robot.2019.01.001
URI
http://hdl.handle.net/10203/251782
Appears in Collection
AE-Journal Papers(저널논문)
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