让无人机群在不依赖全局控制的情况下实现多覆盖，保障关键资产持续被监视。
Distributed Multi-Coverage for Robot Swarms
- 每架无人机仅依靠局部感知与通信，自主分配覆盖任务。
- 算法确保每个重要目标都有多个无人机同时观测，提升系统容错性。
- 适合通信受限、计算资源少的无人机集群，适用于野外巡查等场景。
用于监视、环境监测和基础设施巡检的自主无人机群必须在机器人故障时仍保持对关键资产的可靠覆盖。这需要多覆盖：每个资产需被多个机器人观测以提供冗余，且覆盖需求随资产重要性变化。尽管近期研究已通过整数规划最优求解集中式问题，但实际部署面临分布式约束：机器人通信范围有限，机载计算能力限制全局规划，部分系统故障也不应导致任务中止。本文提出一种无需全局协调的分布式多覆盖算法，适用于仅具备局部感知、局部通信的无人机群。
原文摘要 · Abstract (English)
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.
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