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.

中文翻译：

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具有连续范围测量的移动机器人部分可观察性下的随机运动计划

在本文中，我们解决了部分可观察性下的随机运动计划问题，尤其是如何导航配备有连续距离传感器（如LIDAR）的移动机器人。与许多现有的机器人运动计划方法相反，我们通过将系统建模为POMDP来明确考虑机器人状态的不确定性。通用POMDP求解器的最新工作通常限于离散的观察空间，由于LIDAR的连续测量，因此不适用于所提出的问题。在这项工作中，我们以现有的蒙特卡洛树搜索方法POMCP为基础，并提出了一种新的算法POMCP ++。我们的算法可以使用一种新颖的测量选择策略来处理连续的观测空间。POMCP ++算法通过在推出阶段删除隐式的完美状态假设，克服了推出政策的价值估算中的过分乐观。我们通过证明它是Monte Carlo树搜索算法，从理论上验证了POMCP ++。通过与也可以应用于提出的问题的其他方法进行比较，我们表明POMCP ++产生了更高的成功率和总回报。