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• W2885752244 abstract "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." @default.
• W2885752244 created "2018-08-22" @default.
• W2885752244 creator A5003306942 @default.
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• W2885752244 date "2016-03-16" @default.
• W2885752244 modified "2023-09-29" @default.
• W2885752244 title "Topology-Guided Path Integral Approach for Stochastic Optimal Control in Cluttered Environment" @default.
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• W2885752244 hasPublicationYear "2016" @default.
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