Abstract: When multiple UAVs (Unmanned Aaerial Vehicle) fight in coordination, cooperative path planning is needed to improve mission success rate. After transforming constraints of cooperative path planning into multiple targets, the fusion design of NSGA(Non-Dominated Sorting Genetic Algorithm)-Ⅲ algorithm and potential field ant colony algorithm are carried out. Firstly, the potential field of the map is constructed to make the nodes close to the obstacles difficult to be selected, and to guide the search direction. Then, the path cost, spatial cooperative constraint and temporal cooperative constraint are modeled and converted into numerical indicators, and are set as multiple targets of NSGA-Ⅲ algorithm. For NSGA-Ⅲ algorithm, critical layer selection method and evolutionary algorithm are designed. Finally, in two-dimensional and three-dimensional grid map, the improved NSGA-Ⅲ algorithm uses each population to search the desired path for each UAV. Simulation results show that the UAV paths obtained by planning are safe and cost less.

Key words: multiple unmanned aerial vehicle ( UAVs), cooperative path planning, non-dominated sorting genetic algorithm-Ⅲ (NSGA-Ⅲ) algorithm, potential field ant colony algorithm