UAV path planning: Integration of grey wolf algorithm and artificial potential field

Summary Unmanned aerial vehicle (UAV) path planning is an important issue in UAV applications, with the goal of finding the optimal path to meet mission requirements, while considering factors such as avoiding obstacles and optimizing flight performance. To improve the efficiency of UAV path planning and enhance the smoothness and safety of UAV operation, this paper proposes a fusion optimization algorithm (GWO‐APF), which combines the grey wolf algorithm (GWO) and artificial potential field method (APF) for UAV path planning algorithms. Based on the GWO algorithm, this algorithm first sets planning constraints such as turning angle and tolerance to reduce the probability of local optima occurring; Second, using dimensionality reduction to improve the search efficiency of the GWO algorithm and generate the initial path; Finally, gravity is given to each point on the initial path, and the APF algorithm is used for path planning again. The simulation results show that the fused GWO‐APF algorithm has stronger path planning ability compared to the traditional GWO algorithm, and has the characteristics of short range and high safety.