让机器人在没网络时也能高效协作，动态决定何时何地联网。
CoCoPlan: Adaptive Coordination and Communication for Multi-robot Systems in Dynamic and Unknown Environments
- 联合规划任务与通信时机，动态调整联网策略。
- 任务完成率高22.4%，通信量减少58.6%，支持100个机器人。
- 适合复杂动态环境下的大规模机器人团队使用。
多机器人系统通过协同可显著提升效率，但在实际中全时通信难以实现，交互常受限于近距离。现有方法或维持全程连接，或依赖固定时序，或采用成对协议，均无法有效适应有限通信下动态时空任务分布，导致协调效果不佳。为此，我们提出CoCoPlan，一个统一框架，联合优化协作任务规划与团队间间歇性通信。该方法结合分支定界架构，联合编码任务分配与通信事件；设计自适应目标函数，平衡任务效率与通信延迟；并引入通信事件优化模块，智能决策全局连通性重建的时机、位置与方式。大量实验表明，其相比前沿方法任务完成率提升22.4%，通信开销降低58.6%，可扩展至100个机器人。硬件实验涵盖复杂二维办公场景与大规模三维灾后救援场景。
原文摘要 · Abstract (English)
Multi-robot systems can greatly enhance efficiency through coordination and collaboration, yet in practice, full-time communication is rarely available and interactions are constrained to close-range exchanges. Existing methods either maintain all-time connectivity, rely on fixed schedules, or adopt pairwise protocols, but none adapt effectively to dynamic spatio-temporal task distributions under limited communication, resulting in suboptimal coordination. To address this gap, we propose CoCoPlan, a unified framework that co-optimizes collaborative task planning and team-wise intermittent communication. Our approach integrates a branch-and-bound architecture that jointly encodes task assignments and communication events, an adaptive objective function that balances task efficiency against communication latency, and a communication event optimization module that strategically determines when, where and how the global connectivity should be re-established. Extensive experiments demonstrate that it outperforms state-of-the-art methods by achieving a 22.4% higher task completion rate, reducing communication overhead by 58.6%, and improving the scalability by supporting up to 100 robots in dynamic environments. Hardware experiments include the complex 2D office environment and large-scale 3D disaster-response scenario.
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