Multiple Routes Planning for UAV Based on Multi-agent Particle Swarm Optimization
Xuzhi Chen, Nanyu Chen, Zhijun Meng, Kaipeng Wang · International Conference on Electric Information and Control Engineering · 2012
Aiming at planning multiple routes for unmanned aerial vehicle (UAV), multi-agent system (MAS) and particle swarm optimization (PSO) are integrated to form a hybrid algorithm: Multi-Agent Particle Swarm Optimization (MAPSO). Traditional population structure of the original PSO is changed. A particle in MAPSO, being regarded as an agent, represents a candidate route. All agents live in a lattice-like environment, with each agent fixed on a lattice-point. By this means, the speed of information passing among particles is optimized. Moreover, means clustering algorithm is introduced to form spatial distinct subpopulations. As a result of all the efforts, an effective way to plan multiple routes is found. Using it, an emulator is designed and some experiments are done. The results prove the feasibility and suitability of the novel method for multiple routes planning issue.