基于高维多目标优化的多无人机协同航迹规划

Multi-UAV coordinated path planning based on many-objective optimization

随着无人机应用领域的增多,多无人机协同航迹规划问题变得愈发重要.然而,现存的多无人机协同航迹规划问题大多将多个目标加权转换为单目标问题进行优化,为减少多目标加权的主观性,本文提出一种基于高维多目标优化的多无人机协同航迹规划模型,此模型可以同时优化多无人机航迹距离代价、多无人机航迹威胁代价、多无人机航迹能耗代价,以及多无人机协同性能.同时,为提高高维多目标优化算法在解决此模型时的性能,提出一种基于个体评估交叉策略的NSGA-Ⅲ算法(NSGAⅢ-ICO),可以根据算法运行代数综合评估个体优劣并指导种群交叉操作.仿真结果证明,此模型可以有效地提供多无人机协同航迹,且通过与其他高维多目标优化算法的比较,可以证明此改进算法可以有效地提高多无人机协同航迹规划的多种性能.

With the increase in the application areas of unmanned aerial vehicles(UAVs),the problem of coordinated path planning for multi-UAVs becomes more and more significant.However,most of the existing methods optimize this problem by converting multi-objective weighting into a single-objective problem.To reduce the subjectivity of multi-objective weighting,a coordinated path planning model based on many-objective optimization is proposed to optimize the multi-UAVs’track distance cost,track threat cost,track energy cost,and coordination performance.In the many-objective evolutionary algorithm(MaOEA),the convergence and diversity of the individuals are both important indicators to evaluate the performance of individuals.To solve the problem that individuals in the algorithm are not easy to evaluate,an NSGA-Ⅲalgorithm based on the individual evaluation mating strategy(NSGAⅢ-ICO)is proposed,which can comprehensively evaluate the performance of individuals according to the generation of the algorithm and guide the mating operation.Simulation results show that this model can effectively provide coordinated tracks for multi-UAVs,and by comparing it with other MaOEAs,it can be proved that this proposed algorithm can effectively improve the performance of coordinated path planning for multi-UAVs.