Trajectory planning for a multi-UAV system requires jointly considering obstacle avoidance, inter-UAV conflict avoidance, and target reaching. To address this problem, this paper proposes a graph attention network-based method for multi-UAV trajectory planning. The multi-UAV system is represented as an interaction graph, where UAVs are modeled as nodes and communication-based inter-UAV relationships are modeled as edges. For each UAV, local perception, target-related direction information, previous motion direction, and neighborhood information are integrated into the node representation, while the relative geometric relationship between neighboring UAVs is used as the edge feature. The constructed graph is fed into a multi-head graph attention network to extract interaction-aware features and output an action score vector over discrete flight direction labels for each UAV. During online execution, candidate flight actions are generated according to the action scores, and the final action is selected using the geodesic cost-to-go map. The trajectories of all UAVs are then generated step by step through the online decision process. By combining local perception, target guidance, motion history, and inter-UAV interaction information, the proposed method can learn cooperative action preferences for multi-UAV trajectory generation. Experiments are conducted on different flight maps and swarm sizes using multiple performance metrics. The results show that the proposed method achieves effective performance in mission success, flight efficiency, and safety-related metrics, and it also demonstrates generalization ability on unseen maps. Compared with a CBF-based collision avoidance method, the proposed method achieves better performance in task completion, inter-UAV collision avoidance, and trajectory efficiency.
Liu et al. (Sat,) studied this question.