Stochastic Motion Planning under Partial Observability for Mobile Robots with Continuous Range Measurements
This work provides a more robust motion planning solution for mobile robots operating under partial observability, which is a common challenge in real-world robotics applications.
This paper addresses stochastic motion planning for mobile robots with continuous range sensors like LIDAR, modeling the system as a POMDP. The proposed POMCP++ algorithm, an extension of POMCP, handles continuous observation spaces and overcomes over-optimism in value estimation, resulting in significantly higher success rates and total rewards compared to other applicable methods.
In this paper, we address the problem of stochastic motion planning under partial observability, more specifically, how to navigate a mobile robot equipped with continuous range sensors such as LIDAR. In contrast to many existing robotic motion planning methods, we explicitly consider the uncertainty of the robot state by modeling the system as a POMDP. Recent work on general purpose POMDP solvers is typically limited to discrete observation spaces, and does not readily apply to the proposed problem due to the continuous measurements from LIDAR. In this work, we build upon an existing Monte Carlo Tree Search method, POMCP, and propose a new algorithm POMCP++. Our algorithm can handle continuous observation spaces with a novel measurement selection strategy. The POMCP++ algorithm overcomes over-optimism in the value estimation of a rollout policy by removing the implicit perfect state assumption at the rollout phase. We validate POMCP++ in theory by proving it is a Monte Carlo Tree Search algorithm. Through comparisons with other methods that can also be applied to the proposed problem, we show that POMCP++ yields significantly higher success rate and total reward.