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

Abstract

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-III algorithm 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.