A Multi-Strategy Enhanced Artificial Lemming Optimization Algorithm for Three-Dimensional Dynamic Path Planning of Unmanned Aerial Vehicles

Aiming at the problem that it is difficult for existing path planning methods to plan UAV paths in real time in complex atmospheric turbulence environments, this work proposes a dynamic path planning method for UAVs based on an improved artificial lemming algorithm. First, using temperature, pressure, and wind vectors from WRF/NWP forecast data, a dynamic turbulence-change environment model in the airspace is constructed. Then, a UAV dynamic path planning model is formulated by comprehensively considering the turbulence change rate and path safety evaluation factors. Next, to address premature convergence of existing algorithms under turbulence influence, a solving method for the UAV dynamic path planning model based on an improved artificial lemming algorithm is developed. Simulation results show that, under the proposed replanning mechanism, the improved algorithm reduces the final fitness by 36.19% and cumulative turbulence exposure by 16.28% on average compared with all competing methods.