An Improved Particle Swarm Algorithm for UAV Path Planning

Optimizing UAV flight paths is a critical challenge for modern unmanned aircraft systems. Conventional manual methods are inefficient and error-prone. To address this, we propose an improved 3D path planning algorithm using particle swarm optimization. Our method employs a multi-objective evolutionary genetic algorithm to determine optimal waypoints while considering terrain obstacles, distances, and performance constraints. An adaptive linear coefficient of variation with constant acceleration and maximum velocity enhances optimization. We use a multi-objective genetic algorithm to tackle nonlinear planning, optimizing convergence by replacing undesirable particles. For complex environments, we combine genetic and particle swarm algorithms with simulated annealing for global optimization. Experimental results show outstanding performance, surpassing other algorithms in accuracy and stability. Our approach promises significant advancements in UAV path planning, aiding UAV technology development and operation.