A Study of UAV Path Planning Based on Particle Swarm Optimization and Wolf Pack Algorithm

With the rapid advancement of unmanned aerial vehicle (UAV) technology, path planning becomes a critical issue in optimizing flight efficiency and safety. This paper first constructed a three-dimensional terrain environment based on an exponential function peak model. By integrating the free-boundary cubic spline interpolation method, a continuous and smooth UAV flight path was generated. Discrete sampling techniques were employed to convert the continuous curve into a computable sequence of path points, and a collision detection mechanism was incorporated to ensure obstacle avoidance capability. Secondly, according to the UAV path planning scenario, models for the Particle Swarm Optimization (PSO) and Wolf Pack Algorithm (WPA) were established. The global path was rapidly searched through the collaborative mechanism of PSO, while the division of labor strategy of WPA was utilized for local refinement optimization. Finally, simulations of the two algorithms were conducted on the MATLAB platform, and the UAV path results under both PSO and WPA were solved and displayed. Through the establishment and solution of the aforementioned models, the feasibility and effectiveness of the intelligent optimization algorithm models were verified, providing a solid foundation for subsequent research on UAV path planning.