ESCAPE: an efficient and safe distributed UAV swarm exploration framework with collision avoidance perception

Abstract Significant progress has been made in distributed unmanned aerial vehicle (UAV) swarm exploration. In complex scenarios, existing methods typically rely on shared trajectory information for collision avoidance, but communication timeliness issues may result in outdated trajectories being referenced when making collision avoidance decisions, preventing timely responses to the motion changes of other UAVs, thus elevating the collision risk. To address this issue, this paper proposes a new distributed UAV swarm exploration framework. First, we introduce an improved global exploration strategy that combines the exploration task requirements with the surrounding obstacle distribution to plan an efficient and safe coverage path. Secondly, we design a collision risk prediction method based on relative distance and relative velocity, which effectively assists UAVs in making timely collision avoidance decisions. Lastly, we propose a multi-objective local trajectory optimization function that considers the positions of UAVs and static obstacles, thereby planning safe flight trajectories. Extensive simulations and real-world experiments demonstrate that this framework enables safe and efficient exploration in complex environments.