Three-Dimensional Path Planning of UAV Based on Improved Particle Swarm Optimization
Lixia Deng, Huanyu Chen, Xiaoyiqun Zhang, Haiying Liu
Abstract
The traditional particle swarm optimization algorithm is fast and efficient, but it is easy to fall into a local optimum. An improved PSO algorithm is proposed and applied in 3D path planning of UAV to solve the problem. Improvement methods are described as follows: combining PSO algorithm with genetic algorithm (GA), setting dynamic inertia weight, adding sigmoid function to improve the crossover and mutation probability of genetic algorithm, and changing the selection method. The simulation results show that the improved PSO algorithm solves better route results and is faster and more stable.
Topics & Concepts
CrossoverParticle swarm optimizationMathematical optimizationGenetic algorithmInertiaComputer sciencePath (computing)Sigmoid functionSelection (genetic algorithm)MutationMeta-optimizationAlgorithmMotion planningMulti-swarm optimizationMathematicsArtificial intelligenceArtificial neural networkRobotPhysicsClassical mechanicsChemistryProgramming languageBiochemistryGeneRobotic Path Planning AlgorithmsRobotics and Sensor-Based LocalizationUAV Applications and Optimization