  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.
