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.

When a mobile robot does not have perfect knowledge of its position, conventional controllers can experience failures such as collisions because the uncertainty of the position is not considered in choosing control actions. In this paper, we show how global planning and local feedback control can be combined to generate control laws in the space of distributions over position, that is, in information space. We give a novel algorithm for computing “information-constrained” linear quadratic Gaussian (icLQG) policies for controlling a robot with imperfect state information. The icLQG algorithm uses the belief roadmap algorithm to efficiently search for a trajectory that approximates the globally-optimal motion plan in information space, and then iteratively computes a feedback control law to locally optimize the global approximation. The icLQG algorithm is not only robust to imperfect state information but also scalable to high-dimensional systems and environments. In addition, icLQG is capable of answering multiple queries efficiently. We demonstrate performance results for controlling a vehicle on the plane and a helicopter in three dimensions.