This paper addresses the problem of Multi-robot Coverage Path Planning for unknown environments in the presence of robot failures. Unexpected robot failures can seriously degrade the performance of a robot team and in extreme cases jeopardize the overall operation. Therefore, this paper presents a distributed algorithm, called Cooperative Autonomy for Resilience and Efficiency, which not only provides resilience to the robot team against failures of individual robots, but also improves the overall efficiency of operation via event-driven replanning. The algorithm uses distributed Discrete Event Supervisors, which trigger games between a set of feasible players in the event of a robot failure or idling, to make collaborative decisions for task reallocations. The game-theoretic structure is built using Potential Games, where the utility of each player is aligned with a shared objective function for all players. The algorithm has been validated in various complex scenarios on a high-fidelity robotic simulator, and the results demonstrate that the team achieves complete coverage under failures, reduced coverage time, and faster target discovery as compared to three alternative methods.

中文翻译：

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关心：机器人团队的弹性和效率合作自主权，以完全覆盖机器人故障下的未知环境

本文针对存在机器人故障的未知环境，解决了多机器人覆盖路径规划问题。意外的机器人故障可能会严重降低机器人团队的性能，并在极端情况下危及整体操作。因此，本文提出了一种分布式算法，称为“弹性与效率协同自主”，该算法不仅可以为机器人团队提供针对单个机器人故障的弹性，而且可以通过事件驱动的重新计划提高整体操作效率。该算法使用分布式离散事件监督程序，如果机器人出现故障或空转，则会触发一组可行玩家之间的游戏，以便为任务重新分配做出协作决策。游戏理论结构是使用潜在游戏建立的，其中每个玩家的效用与所有玩家的共享目标函数保持一致。该算法已在高保真机器人模拟器上的各种复杂场景中得到验证，结果表明，与三种替代方法相比，该团队可在故障情况下实现完全覆盖，缩短覆盖时间并更快地发现目标。