Coalition is an important mean of multi-robot systems to collaborate on common tasks. An 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 exploration, 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. Different from centralized approaches, it is distributed and complete, meaning that only local communication is required and a K-serial Stable solution is ensured. Furthermore, to accelerate adaptation to dynamic targets and resource distribution that are only perceived online, a heterogeneous graph attention network 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 via large-scale simulations with 170 robots and hardware experiments of 13 robots, against several strong baselines such as 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，自适应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任务的问题，任务包括探索、动态目标捕获以及重要资源周边的防线构建。由于每个机器人可在众多任务中自主选择，如何协调这些机器人以实现整体效用最大化仍具挑战性。本文提出一种通用联盟策略——K序列稳定联盟算法。与集中式方法不同，该算法具有分布式和完备性特征，仅需局部通信即可保证K序列稳定解的存在。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学习启发式方法，在局部优化过程中选择更优参数与具有潜力的初始解。相较人工启发式或端到端预测方法，该方法既提升了在线适应性，又保留了质量保证机制。通过含170台机器人的大规模仿真及13台机器人的硬件实验，验证了所提方法优于GreedyNE和FastMaxSum等强基线方法的表现。