Research on Three-Dimensional Path Planning of UAVs in Complex Terrain Based on Enhanced Grey Wolf Optimizer

Three dimensional path planning of unmanned aerial vehicles is a crucial part of autonomous flight missions, and the performance of its algorithm directly determines the optimality, safety, and energy efficiency of the path. Aiming at the requirements of obstacle avoidance, shortest path, and smoothness in path planning under complex terrain environments, this paper proposes an Enhanced Grey Wolf Optimizer (EGWO) and conducts a comparative study with four typical intelligent optimization algorithms in 3D path planning scenarios. By constructing a 3D simulation environment with terrain undulations and threat areas, the paths generated by each algorithm are comprehensively evaluated from four dimensions: path length, altitude profile characteristics, terrain adaptability, and path smoothness. Experimental results show that the proposed EGWO algorithm exhibits better performance in complex terrain: compared with the traditional Grey Wolf Optimizer and other comparative algorithms, the path generated by EGWO is shorter, the altitude change is gentler, and it can fit terrain features more accurately, effectively reducing the energy consumption and control difficulty of UAV flight. The research results provide an efficient algorithm selection scheme for UAV path planning in complex terrain and also offer practical reference for the improved application of the Grey Wolf Optimizer in engineering optimization problems.