A Topology-Guided Path Integral Approach for Stochastic Optimal Control
This addresses robot motion planning in complex, obstacle-filled settings, though it appears incremental as it builds on existing path integral and topological planning approaches.
The paper tackles the problem of stochastic optimal control for robot motion in cluttered environments, where sampling methods often get stuck in local minima due to obstacles. The result is a method that combines topological motion planning with path integral control to generate dynamically feasible, collision-free trajectories while mitigating local optima issues.
This work presents an efficient method to solve a class of continuous-time, continuous-space stochastic optimal control problems of robot motion in a cluttered environment. The method builds upon a path integral representation of the stochastic optimal control problem that allows computation of the optimal solution through sampling and estimation process. As this sampling process often leads to a local minimum especially when the state space is highly non-convex due to the obstacle field, we present an efficient method to alleviate this issue by devising a proposed topological motion planning algorithm. Combined with a receding-horizon scheme in execution of the optimal control solution, the proposed method can generate a dynamically feasible and collision-free trajectory while reducing concern about local optima. Illustrative numerical examples are presented to demonstrate the applicability and validity of the proposed approach.