In this paper, 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 mixed-integer linear programs (MILPs) to identify sections of the interrupted path that can be optimally replanned within the time budget. We validate our approach in simulation using maps of real-world environments and compare our approach against a greedy detour replanner and other state-of-the-art approaches.

翻译：本文提出了一种在初始存在未知静态障碍物的环境中，为机器人重新规划覆盖路径的方法。现有覆盖方法通过沿最少覆盖线（直线路径）进行覆盖来缩短覆盖时间。然而，实时重新计算此类路径计算开销较大，会导致机器人停机，从而增加覆盖时间。一种简单的替代方案是贪心绕行重规划，即尽量减小与初始路径的偏差进行重规划，这种方法计算效率高，但可能产生不必要的绕行。在本研究中，我们提出了一种名为OARP-Replan的即时覆盖重规划方法，该方法能在给定时间预算内对中断的覆盖路径进行接近最优的重规划。为此，我们通过求解混合整数线性规划（MILP）的线性松弛，识别出可在时间预算内进行最优重规划的中断路径段。我们使用真实环境地图在仿真中验证了该方法，并将其与贪心绕行重规划器及其他前沿方法进行了比较。