Three-Dimensional Path Planning for Post-Disaster Rescue UAV by Integrating Improved Grey Wolf Optimizer and Artificial Potential Field Method

Abstract

The path planning of unmanned aerial vehicles (UAVs) is crucial in UAV search and rescue operations to ensure efficient and safe search activities. However, most existing path planning algorithms are not suitable for post-disaster mountain rescue mission scenarios. Therefore, this paper proposes the IGWO-IAPF algorithm based on the fusion of the improved grey wolf optimizer (GWO) and the improved artificial potential field (APF) algorithm. This algorithm builds upon the grey wolf optimizer and introduces several improvements. Firstly, a nonlinear adjustment strategy for control parameters is proposed to balance the global and local search capabilities of the algorithm. Secondly, an optimized individual position update strategy is employed to coordinate the algorithm’s search ability and reduce the probability of falling into local optima. Additionally, a waypoint attraction force is incorporated into the traditional artificial potential field algorithm based on the force field to fulfill the requirements of three-dimensional path planning and further reduce the probability of falling into local optima. The IGWO is used to generate an initial path, where each point is assigned an attraction force, and then the IAPF is utilized for subsequent path planning. The simulation results demonstrate that the improved IGWO exhibits approximately a 60% improvement in convergence compared to the conventional GWO. Furthermore, the integrated IGWO-IAPF algorithm shows an approximately 10% improvement in path planning effectiveness compared to other traditional algorithms. It possesses characteristics such as shorter flight distance and higher safety, making it suitable for meeting the requirements of post-disaster rescue missions.