Coalition is an important mean of multi-robot systems to collaborate on common tasks. An effective and adaptive coalition strategy is essential for the online performance in dynamic and unknown environments. In this work, the problem of territory defense by large-scale heterogeneous robotic teams is considered. The tasks include surveillance, capture of dynamic targets, and perimeter defense over valuable resources. Since each robot can choose among many tasks, it remains a challenging problem to coordinate jointly these robots such that the overall utility is maximized. This work proposes a generic coalition strategy called K-serial stable coalition algorithm (KS-COAL). Different from centralized approaches, it is distributed and anytime, meaning that only local communication is required and a K-serial Nash-stable solution is ensured. Furthermore, to accelerate adaptation to dynamic targets and resource distribution that are only perceived online, a heterogeneous graph attention network (HGAN)-based heuristic is learned to select more appropriate parameters and promising initial solutions during local optimization. Compared with manual heuristics or end-to-end predictors, it is shown to both improve online adaptability and retain the quality guarantee. The proposed methods are validated rigorously via large-scale simulations with hundreds of robots, against several strong baselines including GreedyNE and FastMaxSum.

翻译：联盟是多机器人系统协作完成共同任务的重要手段。在动态未知环境中，有效且自适应的联盟策略对在线性能至关重要。本研究考虑大规模异构机器人团队执行领土防御问题，任务包括对动态目标的监控捕获、以及有价值资源的边界防御。由于每个机器人可在多个任务中选择，如何协调这些机器人以实现整体效用最大化仍具挑战性。本文提出一种名为K-序列稳定联盟算法（KS-COAL）的通用联盟策略。与集中式方法不同，该算法具有分布式与任意时特性，仅需局部通信即可确保K-序列纳什稳定解。此外，为加速适应仅能在线感知的动态目标与资源分布，我们基于异构图注意力网络（HGAN）学习启发式方法，在局部优化中选择更合适的参数与优质初始解。相较于人工启发式或端到端预测器，该方法既能提升在线适应性，又可保留质量保证。通过包含数百个机器人的大规模仿真实验，与GreedyNE和FastMaxSum等强基线方法对比，验证了所提方法的有效性。