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This paper addresses the problem of Multi-robot Coverage Path Planning (MCPP)for unknown environments in the presence of robot failures. Unexpected robotfailures can seriously degrade the performance of a robot team and in extremecases jeopardize the overall operation. Therefore, this paper presents adistributed algorithm, called Cooperative Autonomy for Resilience andEfficiency (CARE), which not only provides resilience to the robot team againstfailures of individual robots, but also improves the overall efficiency ofoperation via event-driven replanning. The algorithm uses distributed DiscreteEvent Supervisors (DESs), which trigger games between a set of feasible playersin the event of a robot failure or idling, to make collaborative decisions fortask reallocations. The game-theoretic structure is built using PotentialGames, where the utility of each player is aligned with a shared objectivefunction for all players. The algorithm has been validated in various complexscenarios on a high-fidelity robotic simulator, and the results demonstratethat the team achieves complete coverage under failures, reduced coverage time,and faster target discovery as compared to three alternative methods.

CARE: Cooperative Autonomy for Resilience and Efficiency of Robot Teams for Complete Coverage of Unknown Environments under Robot Failures