An Incremental Sampling-based Algorithm for Stochastic Optimal Control

Details An Incremental Sampling-based Algorithm for Stochastic Optimal Control An Incremental Sampling-based Algorithm for Stochastic Optimal Control

2012-02-24

arxiv.org/abs/1202.5544v1

Computer Science

Robotics

Part of the results have been submitted to the IEEE International Conference on Robotics and Automation (ICRA 2012). Minnesota

Scientific paper

In this paper, we consider a class of continuous-time, continuous-space stochastic optimal control problems. Building upon recent advances in Markov chain approximation methods and sampling-based algorithms for deterministic path planning, we propose a novel algorithm called the incremental Markov Decision Process (iMDP) to compute incrementally control policies that approximate arbitrarily well an optimal policy in terms of the expected cost. The main idea behind the algorithm is to generate a sequence of finite discretizations of the original problem through random sampling of the state space. At each iteration, the discretized problem is a Markov Decision Process that serves as an incrementally refined model of the original problem. We show that with probability one, (i) the sequence of the optimal value functions for each of the discretized problems converges uniformly to the optimal value function of the original stochastic optimal control problem, and (ii) the original optimal value function can be computed efficiently in an incremental manner using asynchronous value iterations. Thus, the proposed algorithm provides an anytime approach to the computation of optimal control policies of the continuous problem. The effectiveness of the proposed approach is demonstrated on motion planning and control problems in cluttered environments in the presence of process noise.

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