Multi-UAV Cooperative Path Planning via Improved Grey Wolf Optimization Algorithm

Multi-UAV cooperative path planning is a key problem in the research filed on UAV swarm, which is usually a complex nonlinear NP-hard optimization problem. In this paper, the grey wolf optimization (GWO) algorithm is employed to deal with the complex multi-objective optimization problem with multiple constraints in multi-UAV cooperative path planning, and the penalty function method is used to transform the multi-constrained path planning problem into an unconstrained optimization problem. Furthermore, aiming at the problems of slow convergent speed and local optimal solutions in the traditional GWO algorithm, nonlinear convergent factor and Levy flight update strategy are introduced to accelerate the convergent speed and enhance the optimization ability of multi-UAV cooperative path planning algorithm. Finally, the trajectories are smoothed by cubic B-spline curve to improve the flight stability of UAVs.