UAV Swarm Path Planning Algorithm Based on Starling Flocking

The study addresses the problem of how UAV swarms can effectively navigate through complex urban environments and reach their destinations quickly while avoiding obstacles. This paper investigates UAV swarm coordination algorithms that maintain the stability and safety of quadcopter UAV swarms, as well as UAV swarm path planning algorithms aimed at finding the shortest path from the cluster’s starting point to its destination. The swarm coordination algorithm is based on mimicking bird flock behavior and is divided into two processes: the construction of internal subnetwork topological structures and the design of behavioral rules among individuals. In the algorithm, a smoothing function replaces fixed parameters, making the UAVs’ power output more consistent with actual power output characteristics. The UAV swarm path planning algorithm is based on particle swarm optimization, which considers UAV kinematic constraints to find the optimal path. The performance of the algorithm is tested with 11 to 20 UAV swarm flights through different obstacle environments, demonstrating its effectiveness.