A Hybrid Dynamic Path-Planning Method for Obstacle Avoidance in Unmanned Aerial Vehicle-Based Power Inspection

Path planning for Unmanned Aerial Vehicles (UAVs) plays a critical role in power line inspection. In complex inspection environments characterized by densely distributed and dynamic obstacles, traditional path-planning algorithms struggle to ensure both efficiency and safety. To address these challenges, this study proposes a dynamic path-planning method that integrates an improved Rapidly exploring Random Tree Star (RRT*) algorithm with the Dynamic Window Approach (DWA). The proposed method includes key components such as sampling-point search, random tree growth, global path-node optimization, and local dynamic obstacle avoidance. In the sampling-point search, a target-biased search strategy is introduced to guide the random tree growth toward the target point, while an attractive function is added to enhance search efficiency. Based on a breadth-first search strategy, the path obtained is optimized to reduce path complexity. To address the RRT* algorithm’s limitation in dynamic obstacle avoidance, a local path-planning method combining the improved DWA algorithm is proposed, improving efficiency in areas with dense obstacles. Simulation results show that, compared to traditional algorithms, the proposed method achieves an 8% to 12% optimization in path length, more than 50% in node optimization, and over 95% in planning time optimization. Furthermore, in dynamic obstacle avoidance across different motion directions, the proposed method ensures effective local dynamic obstacle avoidance while minimizing global path fluctuations.