DPAF-SA: A Formation Control Algorithm for Dynamic Allocation and Fusion of Potential Fields for UAV Swarms

To address the challenges of inefficient convergence in UAV swarms under complex environments due to static position allocation (SPA), as well as the tendency of traditional artificial potential field (APF) obstacle avoidance to get stuck in local optima, this paper proposes a formation control method (DPAF-SA) based on dynamic position allocation (DPA) and APF-SA fusion, grounded in the principle of consensus and the simulated annealing (SA) algorithm. First, the formation position allocation is formulated as an online combinatorial optimization problem. Based on this framework, a dynamic position allocation and dynamic virtual center mechanism is designed to solve the optimal “UAV-position point” mapping in real time, minimizing the total convergence cost of the swarm. Second, to address the local optimum trap and decoupling issues in APF, the global search capability and probabilistic jump mechanism of SA are integrated into APF. This enables optimization of the consistency control input, ensuring tight coupling between efficient obstacle avoidance and formation maintenance. Finally, a high-fidelity HIL simulation platform based on Unity3D 2022.3.2. was established to validate the engineering feasibility and real-time robustness of the proposed algorithm. Simulation results demonstrate that, compared with the representative baseline model, the proposed method achieves improvements of approximately 46.1%, 24.5%, and 39.6% in formation accuracy, convergence performance, and safety margin, respectively, validating its effectiveness.