An Adaptive Cooperative Potential Field (ACPF) based UAV formation control algorithm for urban environments

Abstract

With the rapid development of unmanned aerial vehicles (UAVs), their applications in urban firefighting, rescue, and military missions continue to expand. However, maintaining formation stability and achieving reliable obstacle avoidance in complex low-altitude urban environments remain challenging. To address this issue, this paper proposes an Adaptive Cooperative Potential Field (ACPF) algorithm for UAV formation control. The algorithm is built on the artificial potential field framework. It includes three components. First, a cooperative control mechanism combines formation force and neighbor coordination force to improve spatial consistency. Second, a dynamic flight weight adjustment mechanism adaptively balances attractive, repulsive, and formation forces. Third, a 3D obstacle avoidance and altitude constraint strategy ensures safe flight by incorporating terrain information and safety margins. Simulation results show that ACPF outperforms APF, VS-DWA, and CLF-CBF-QP in formation maintenance, obstacle avoidance, and trajectory efficiency. For the regular pentagon formation, the total path length is reduced by 5.4% on average, and the convergence time is shortened by 63 s. For the rhombus formation and V-shaped formation, the path-length reductions reach 7.2% and 7.4%, while the convergence time decreases by 53 s and 66 s, respectively. Statistical experiments and parameter sensitivity analyses further demonstrate the robustness and reliability of the proposed algorithm. These results demonstrate the effectiveness of ACPF for UAV formation control in complex urban environments.