Joint optimization for heterogeneous multi-UAV configuration and mission planning within SEAD scenarios

SEAD (suppression of enemy air defenses) is a typical application scenario of multi-UAV cooperation. Based on the characteristics of this scenario, the number of different types of UAV was also used as a decision variable in the task planning problem, fully characterizing the various constraints of the target, mission, and UAV, and establishing a heterogeneous UAV formation path problem model. A two-layer joint optimization method was designed to solve the model: the upper layer was designed with the task connection impact indicator to accurately assess the quantitative requirements of various types of UAVs and guide UAVs configuration adjustments; the lower layer improved the genetic algorithm, which can efficiently handle multiple coupling constraints and can accurately adjust the mission plan in conjunction with UAV quantity changes. The two layers coordinate with each other to obtain a UAV configuration and mission execution plan that meet the requirements. Simulation results show that the method can obtain a reasonable UAV configuration plan without traversing various UAV configurations, while obtaining an efficient and feasible mission execution plan.