论文标题
具有连续范围测量的移动机器人的部分可观察性下的随机运动计划
Stochastic Motion Planning under Partial Observability for Mobile Robots with Continuous Range Measurements
论文作者
论文摘要
在本文中，我们解决了部分可观察性下的随机运动计划的问题，更具体地说，如何浏览配备有连续范围传感器（例如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。关于通用POMDP求解器的最新工作通常仅限于离散的观察空间，并且由于LIDAR的连续测量值，因此不容易适用于提议的问题。在这项工作中，我们基于现有的蒙特卡洛树搜索方法POMCP，并提出了一种新的算法POMCP ++。我们的算法可以通过新颖的测量选择策略来处理连续的观察空间。 POMCP ++算法通过在推出阶段删除隐式完美的状态假设来克服推出策略的价值估计的过度优势。从理论上讲，我们证明它是一种蒙特卡洛树搜索算法来验证POMCP ++。通过与也可以应用于拟议问题的其他方法的比较，我们表明POMCP ++产生的成功率和总奖励明显更高。
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.
