UAV Online Path Planning Based On Improved Genetic Algorithm with Optimized Search Region
Xiaohai Wang, Xiuyun Meng · 2019 IEEE International Conference on Unmanned Systems (ICUS) · 2019
When performing online path planning for unmanned aerial vehicle(UAV), the planning algorithm needs to have high search efficiency. In this case, the weakness of poor local search ability and low planning efficiency of the traditional Genetic Algorithm(GA) will be reflected. In addition, the population information is not used sufficiently in GA. To address these shortcomings, this paper proposes an improved genetic algorithm. Before gene manipulation of each generation, some individuals in the population are analyzed to judge the searching value of different regions in the planning space, then the generating regions of evolution operator is reasonably restricted. The improved algorithm is used for UAV online path planning. The simulation results show that the method strengthens the local search ability of the genetic algorithm and improves the planning efficiency, and can complete UAV online path planning for tracking moving targets in the face of sudden threats.