Optimized multi-UAV cooperative path planning under the complex confrontation environment

As an emerging technology, multi-UAV collaboration is widely used in military and civil applications, including regional surveillance, remote sensing, target strike, etc. As a key step in the implementation of multi-UAV cooperative missions, path planning aims to generate near-optimal paths that satisfy certain constraints, ensure that each UAV can reach the mission area quickly and reduce the probability of being captured and destroyed by the antagonism side. In this paper, we design an optimized multi-UAV cooperative path planning method under the complex confrontation environment. Firstly, the threat model is designed based on the actual situation. Combining the threat and fuel consumption criteria, under the constraints of time and space, a multi-constraint objective optimization model is established. Following this, an improved grey wolf optimizer algorithm is used to solve the optimization model. Based on the characteristics of the multi-UAV cooperative path planning, the algorithm is improved in three aspects: population initialization, decay factor updating, and individual position updating. The simulation results demonstrate that the proposed algorithm is effective in generating paths for multi-UAV cooperative path planning and has the advantages of a lower path cost and faster convergence speed as compared to the other algorithms tested in this work.