Distributed Multi-Coverage for Robot Swarms

2026-05-20

AI Summary

This work addresses the challenge of achieving reliable multi-fold coverage of critical assets by drone swarms in realistic scenarios characterized by limited communication, absence of global coordination, and potential robot failures. The paper proposes a fully distributed multi-fold coverage algorithm that relies solely on local sensing and communication, dynamically allocating an appropriate number of robots to satisfy heterogeneous redundancy requirements through a lightweight decision-making mechanism—without centralized planning or global information. As the first distributed approach supporting both heterogeneous coverage demands and strong fault tolerance, the algorithm effectively sustains mission continuity under stringent onboard computational constraints, making it suitable for practical deployment in large-scale drone swarms.

Abstract

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.

Problem

• distributed multicoverage
• robot swarms
• redundant coverage
• asset monitoring
• decentralized coordination

Innovation

• distributed multicoverage
• robot swarms
• local communication
• redundant coverage
• autonomous drones