A Novel Route Planning Method of Fixed-Wing Unmanned Aerial Vehicle Based on Improved QPSO
Chen Huang, Jiyou Fei, Wu Deng
Abstract
This paper proposes a quick and accurate method based on an improved quantum-behaved particle swarm optimization (QPSO) algorithm for route planning of fixed-wing unmanned aerial vehicle (UAV). To overcome the deficiencies of local optima and slow global convergence speed, a novel strategy of particle dimension search is proposed in the QPSO algorithm. It is implemented by transforming original evaluation function into evaluation function of waypoint to more easily escape from local optima and accelerate global convergence speed. In addition, an efficient pretreatment technology for the initial trajectory is set to shorten the calculation time of route planning. Compared with other representative route planners, the comparison results indicate that the proposed route planner is more effective and feasible, which can take on faster convergence speed and better global search ability. The proposed route planner can provide a valuable reference for the route planning of fixed-wing UAVs in different environments.