Particle Swarm Optimization (PSO) algorithm is widely used in path planning due to its simple principle and easy implementation.To address the problems of the traditional PSO algorithm,such as poor search accuracy and easy to fall into local optimal solutions,an improved PSO algorithm is proposed for 3D path planning of UAVs in urban environments.To improve the search efficiency and accuracy of particles,a chaotic sequence is used to initialize the particle swarm,making the initial particle distribution more uniform.Adaptive segmented inertia weights and adaptive exponential learning factors are introduced to balance the global and local search ability of particles.An acceleration factor is added to the velocity update formula to enhance the ability of particles to leave the poor regions.Adaptive adjustment coefficients are introduced to optimize the particle position update formula.Four test functions are selected for testing and simulation experiments are conducted.The quality of the results obtained by the improved algorithm is better than the quality of those obtained by the Genetic Algorithm (GA) and the traditional PSO algorithm.