Virtual Tube-Based Traffic Planning and Conflict Resolution Method for Urban Low-Altitude UAVs

Aiming at the core technical bottlenecks of limited route planning efficiency, insufficient path smoothness, and poor real-time dynamic conflict resolution during multi-UAV cooperative operation within virtual tubes in complex urban low-altitude environments, this paper proposes a systematic multi-UAV route planning and conflict resolution method fully oriented to virtual tube constraints. With the large-scale commercialization of urban low-altitude logistics and Urban Air Mobility (UAM), disordered flight of massive UAVs in dense building environments has become a primary cause of flight safety incidents. Virtual tube technology is widely recognized as a promising solution for structured low-altitude airspace management, yet existing methods fail to simultaneously meet the dual requirements of high-efficiency route planning under strict boundary constraints and real-time dynamic conflict resolution for multi-UAV systems, which severely restricts the large-scale application of virtual tube technology. To address these challenges, this paper first constructs a structured airspace model of virtual tubes for urban low-altitude scenarios with dense buildings, complex obstacle distribution and diverse operating entities. Specifically, a 3D urban building threat model with a safety expansion mechanism is established to eliminate collision risks from UAV positioning errors and airflow disturbances, and the virtual tube is mathematically characterized and divided into a core constraint zone for normal steady flight and an elastic adjustment zone for emergency maneuvering. On this basis, a multi-objective route optimization model with strict inequality constraints is formulated, taking minimum total path length, minimum cumulative heading angle change (for path smoothness characterization) and minimum safety risk cost as optimization objectives, thus transforming the unstructured low-altitude environment into a plannable space with clear boundary constraints and sufficient maneuver redundancy. Second, to overcome the defects of traditional bidirectional RRT algorithms including massive invalid sampling, slow convergence and poor path feasibility under virtual tube constraints, this paper integrates four core improvement mechanisms into the bidirectional RRT framework: ellipsoidal confined sampling, artificial potential field (APF) target guidance, alternating exploration expansion, and real-time tube boundary verification, forming the BI-APF-RRT global route planning algorithm. This algorithm effectively improves route search efficiency, path feasibility and trajectory quality in complex constrained environments, while significantly reducing the computational burden caused by invalid sampling and redundant expansion. Furthermore, for dynamic conflict issues in multi-UAV cooperative operation, this paper proposes an adaptive large-range sampling conflict detection method, which adaptively adjusts the prediction time window and sampling step size according to the relative distance and speed between UAVs to balance detection accuracy and computational efficiency, and classifies conflicts into long-range, medium-range and short-range levels by predicted collision time. On this basis, a three-layer hierarchical conflict resolution strategy (global route layer, local replanning layer, maneuver control layer) is constructed, adopting speed adjustment, heading fine-tuning and height adjustment strategies for different conflict levels respectively, to realize dynamic safety separation maintenance and local maneuver coordination of UAVs without breaking virtual tube constraints. For rigorous validation, extensive simulation experiments are carried out on MATLAB 2024a platform, including static route planning tests in three typical urban environments (simple, medium, complex) and dynamic conflict resolution tests in multi-UAV scenarios. Simulation results show that the proposed algorithm stably generates feasible routes meeting virtual tube constraints in all environments, with excellent environmental adaptability, path continuity and operational stability. In the 10-UAV cooperative scenario, it reaches a 98.2% conflict resolution success rate, with an average replanning time of only 126 ms and a minimum safety distance of 6.2 m, fully complying with UAV low-altitude flight safety requirements. This research provides solid technical support for UAV route organization, operation safety guarantee and high-density cooperative flight in low-altitude logistics and UAM scenarios.