Distributed Multi-Coverage for Robot Swarms
For robot swarm deployments in surveillance and monitoring, this work provides a practical distributed solution to a previously centralized problem, enabling scalability and fault tolerance.
The paper addresses the problem of maintaining reliable multi-coverage of critical assets by robot swarms under constraints like limited communication and onboard computation. It presents a distributed algorithm that achieves multi-coverage without global coordination, enabling robustness to partial failures.
Autonomous drone swarms deployed for surveillance, environmental monitoring, and infrastructure inspection must maintain reliable coverage of critical assets despite robot failures. This requires multicoverage: each asset must be observed by multiple robots for redundancy, with coverage requirements varying by asset importance. While recent work has solved the centralized problem optimally using integer programming, practical deployments face constraints that demand distributed solutions: robots operate with limited communication ranges, onboard computation restricts global planning, and partial system failures must not cause mission abort. We present a distributed multicoverage algorithm for robot swarms operating with local sensing, local communication, and no global coordination.