  Designing autonomous aerial robot team systems remains a grand challenge in
robotics. Existing works in this field can be categorized as centralized and
decentralized. Centralized methods suffer from scale dilemmas, while
decentralized ones often lead to poor planning quality. In this paper, we
propose an enhanced decentralized autonomous aerial robot team system with
group planning. According to the spatial distribution of agents, the system
dynamically divides the team into several groups and isolated agents. For
conflicts within each group, we propose a novel coordination mechanism named
group planning. The group planning consists of efficient multi-agent
pathfinding (MAPF) and trajectory joint optimization, which can significantly
improve planning quality and success rate. We demonstrate through simulations
and real-world experiments that our method not only has applicability for a
large-scale team but also has top-level planning quality
