For the problem of UAV path planning in complex multi-obstacle environment,an algorithm based on Improved Particle Swarm Optimization (IPSO) is proposed.Firstly,the stability of the algorithm is enhanced by unifying the obstacle environment modeling,optimizing the fitness function,and using chaotic particle initialization to diversify the particle swarm.Then,the acceleration constant of the traditional Particle Swarm Optimization (PSO) is replaced by the adaptive acceleration coefficient to avoid falling into local minimum,while improving the efficiency of the algorithm converging to the global optimal solution.Finally,the encoding method of the particle search trajectory in the traditional PSO is replaced by the encoding of UAV motion,which is used to improve the optimality of the solution and search for the optimal path solution.The simulation results show that the IPSO can effectively solve the problems of the traditional PSO in UAV path planning in the complex multi-obstacle environment.In comparison with Gray Wolf Optimization (GWO),Differential Evolution (DE),Quantum Particle Swarm Optimization (QPSO) and traditional PSO,the improved algorithm has significantly improved the path optimization accuracy and stability in different scenarios of static environments.In comparison with Dynamic Particle Swarm Optimization (DPSO), the new algorithm can also be better adapted to the dynamic environment.