Qingwei Zhong, Yingxue YU, Linfeng Zhong, Yongxiang Zhang, Su Liu, Weijun Pan, Xu Yan
A systematic framework is proposed to address both static and dynamic obstacle challenges faced by Unmanned Aerial Vehicles (UAVs) operating in urban low-altitude airspace, with the goal of enhancing obstacle avoidance capability and conflict resolution efficiency. For static conflict scenarios, a bi-objective dynamic protected zone model is developed, aiming to minimize both the probability of boundary violations and the spatial occupancy rate of the safety buffer. Pareto frontiers are generated based on real flight data to characterize UAV protection requirements, while a variable independence assumption is introduced to simplify high-dimensional integration and improve computational efficiency. For dynamic conflicts, a cluster-based protection strategy is proposed, which reduces modeling and computational complexity by grouping UAVs according to similarities in velocity and heading. Conflict resolution is achieved through a Three-Dimensional (3D) protected zone model combined with an Improved Velocity Obstacle method (IVO). The Möller-Trumbore algorithm is employed to calculate the required escape speed and heading direction, effectively overcoming the vertical limitations of traditional Two-Dimensional (2D) models. Experimental results demonstrate that the proposed method significantly improves airspace utilization and reduces computation time by approximately 65.1% compared to baseline approaches, providing strong support for the safe and efficient deployment of multi-UAV systems in complex urban environments.