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

## TL;DR

This paper introduces CARE, a distributed multi-robot coverage algorithm that enhances resilience to robot failures and improves efficiency through event-driven replanning and game-theoretic decision-making.

## Contribution

The paper presents a novel distributed algorithm using potential games for resilient and efficient multi-robot coverage in unknown environments with robot failures.

## Key findings

- Achieves complete coverage despite robot failures
- Reduces coverage time compared to alternative methods
- Enables faster target discovery in complex scenarios

## Abstract

This paper addresses the problem of Multi-robot Coverage Path Planning (MCPP) 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 (CARE), 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 (DESs), 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.