A Dynamic Topology-Considered UAV Swarm Path Planning Method in City Environment

In modern urban environments, precise navigation and efficient path planning for drone swarms are crucial for enhancing aerial operation performance. This study proposes an innovative drone swarm path planning framework, considering the dynamic topology and internal topological changes in urban environments. We utilize a modified particle swarm optimization algorithm to determine the global optimal path, combined with a backpropagation neural network to enhance computational efficiency. Experimental results demonstrate that this method significantly reduces task execution time and energy consumption, decreases collision risks, and effectively improves the operational efficiency and safety of drone swarms in urban environments, providing new insights for practical applications.