Abstract Partially observable Markov decision processes (POMDPs) have been successfully applied to various robot motion planning tasks under uncertainty. However, most existing POMDP algorithms assume a discrete state space, while the natural state space of a robot is often continuous. This paper presents Monte Carlo Value Iteration (MCVI) for continuous-state POMDPs. MCVI samples both a robot's state space and the corresponding belief space, and avoids inefficient a priori discretization of the state space as a grid. Both theoretical results and preliminary experimental results indicate that MCVI is a promising new approach for robot motion planning under uncertainty.
Bai et al. REF presents Monte Carlo value iteration for continuous-state POMDPs with discrete action and observation spaces.
Monte Carlo Value Iteration for Continuous-State POMDPs
WORKSHOP ON THE ALGORITHMIC FOUNDATIONS OF ROBOTICS
Computer Science