Multi-UAV Multi-Task Path Planning Based on DDE-SA Algorithm

The content of this study is based on the multi-task path planning problem of multiple UAVs in three-dimensional complex mountain scenes, and a path planning method based on the DDE-SA algorithm is proposed. The algorithm improves the DE algorithm, uses a mutation strategy based on optimal solution guidance, and introduces intergenerational mutation in the mutation process to prevent falling into the local optimal solution during the iteration process, and adds an adaptive crossover rate to increase the diversity of genes. The SA algorithm is used to select individuals in each iteration. The algorithm is run on the benchmark test function and compared with other algorithms, which shows its optimization performance advantage on the benchmark function. Path planning simulation is carried out in a three-dimensional environment, and experiments are conducted on dense hemispherical radar threat areas. The results show that multiple UAVs can safely complete multi-machine multi-task inspections, solving the problem of target allocation and path planning in a complex mountain environment with dense obstacles.