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.

翻译：多机器人系统通过协调与协作可显著提升效率，然而在实际应用中，全时通信往往难以实现，交互通常仅限于近距离通信。现有方法或维持全时连接，或依赖固定调度，或采用成对协议，但均无法在有限通信条件下有效适应动态时空任务分布，导致协调效果欠佳。为弥补这一不足，我们提出CoCoPlan——一个协同优化协作任务规划与团队间歇通信的统一框架。该方法集成了三个核心组件：联合编码任务分配与通信事件的分支定界架构、平衡任务效率与通信延迟的自适应目标函数，以及策略性决定全局连接重建时机、位置与方式的通信事件优化模块。大量实验表明，本方法在动态环境中实现了22.4%的任务完成率提升，降低58.6%的通信开销，并通过支持多达100个机器人显著提升了系统可扩展性，性能优于现有先进方法。硬件实验涵盖复杂的二维办公环境与大规模三维灾难响应场景。