Dynamic merging and splitting for large-scale swarm navigation of UAVs and UGVs in unknown area

This paper addresses the challenge of achieving efficient collaborative navigation and obstacle avoidance for aerial-ground unmanned swarm in unknown area by proposing a dynamic merge and split method. Firstly, a UAV-dominated collaborative decision-making framework is proposed, which includes a UAV allocation method driven by environmental information and a UGV planning and allocation method guided by UAVs. This framework enables collaborative and dynamic decision-making for the merge and split of the aerial-ground unmanned swarm. Secondly, an integrated dual-model and self-organizing motion control scheme for the aerial-ground unmanned swarm is designed. For the UAV swarm, a tracking–navigation dual-model motion control is developed to enhance robustness and sensing efficiency in unknown area. For the ground vehicles, a dynamic-boundary-based self-organizing motion control is proposed to leverage their high autonomy. Collectively, these control strategies yield highly efficient and coordinated motion control for the aerial-ground unmanned swarm. Finally, numerical simulation experiments are conducted and performance metrics are designed to compare the proposed method with existing representative approaches. The results indicate that the advantages of the proposed method gradually become more pronounced as the swarm scale increases.