Artificial Potential Field Based Reward Shaping for Efficient UAV Path Planning via Reinforcement Learning

Unmanned aerial vehicles have been widely adopted owing to their compact size, high maneuverability and low cost. Consequently, research on unmanned aerial vehicles guidance, navigation and control systems has emerged as a prominent topic. Path planning constitutes a critical component of guidance, navigation and control systems. To overcome the limitations of existing unmanned aerial vehicles path planning algorithms, including restricted environmental perception, low planning efficiency and diminished learning efficacy—we propose a novel method that integrates the artificial potential field with deep reinforcement learning. We devise a reward strategy that incorporates artificial potential field and path fitting, and construct a hierarchical mechanism that fuses reinforcement learning with classical algorithms. This mechanism effectively optimises the search path, mitigates inefficient exploration and substantially accelerates the path planning process. Finally, experiments conducted in a simulation environment, along with comparative analyses against state‐of‐the‐art techniques, demonstrate the superiority of the proposed method in terms of path planning efficiency.