In this article, we propose a method to replan coverage paths for a robot operating in an environment with initially unknown static obstacles. Existing coverage approaches reduce coverage time by covering along the minimum number of coverage lines (straight-line paths). However, recomputing such paths online can be computationally expensive resulting in robot stoppages that increase coverage time. A naive alternative is greedy detour replanning, i.e., replanning with minimum deviation from the initial path, which is efficient to compute but may result in unnecessary detours. In this work, we propose an anytime coverage replanning approach named OARP-Replan that performs near-optimal replans to an interrupted coverage path within a given time budget. We do this by solving linear relaxations of integer linear programs to identify sections of the interrupted path that can be optimally replanned within the time budget. We validate OARP-Replan in simulation and perform comparisons against a greedy detour replanner and other state-of-the-art coverage planners. We also demonstrate OARP-Replan in experiments using an industrial-level autonomous robot.

中文翻译：

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针对部分未知环境随时重新规划机器人覆盖路径


在本文中，我们提出了一种为在初始未知静态障碍物的环境中运行的机器人重新规划覆盖路径的方法。现有的覆盖方法通过沿着最少数量的覆盖线（直线路径）进行覆盖来减少覆盖时间。然而，在线重新计算此类路径的计算成本可能很高，导致机器人停机，从而增加覆盖时间。一种幼稚的替代方案是贪婪绕道重新规划，即以与初始路径的最小偏差进行重新规划，这计算效率高，但可能会导致不必要的绕道。在这项工作中，我们提出了一种名为 OARP-Replan 的随时覆盖重新规划方法，该方法在给定的时间预算内对中断的覆盖路径执行近乎最优的重新规划。我们通过求解整数线性程序的线性松弛来实现这一点，以识别可以在时间预算内最佳地重新规划的中断路径的部分。我们在模拟中验证 OARP-Replan，并与贪婪绕道重新规划器和其他最先进的覆盖规划器进行比较。我们还在使用工业级自主机器人的实验中演示了 OARP-Replan。