連続距離計測を用いた移動ロボットの部分観測下における確率的動作計画
Stochastic Motion Planning under Partial Observability for Mobile Robots with Continuous Range Measurements
LIDARのような連続観測を持つ移動ロボットのPOMDP動作計画のため、POMCPを拡張したPOMCP++を提案し、連続観測空間への対応とロールアウト時の過度な楽観性の除去を実現した。
著者: Ke Sun, Brent Schlotfeldt, George Pappas, Vijay Kumar
分類: cs.RO
原文アブストラクト
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.
