A novel waypoint guidance and adaptive evolution strategy for unmanned aerial vehicle 3D route planning

In the field of unmanned aerial vehicles (UAVs) mission planning, autonomous route planning in complex 3D environments represents a crucial aspect. To enhance the utilization of valuable information and increase search efficiency when solving path planning problems in 3D environments, a novel UAV route planning algorithm named WG-GWO is proposed with a waypoint guidance and an adaptive evolution strategy. Initially, we introduce a novel waypoint extraction method based on cubic B-spline to harness crucial information from individuals eliminated during the evolutionary process. Subsequently, an adaptive evolutionary direction selection strategy is introduced, which utilizes historical information and a differential operator to improve the search ability. The WG-GWO also incorporates Le´vy flight to prevent premature convergence to local minimums. Simulation results validate the effectiveness of the proposed algorithm, demonstrating that WG-GWO outperforms several existing algorithms in generating feasible routes and achieving faster convergence speeds across various scenarios. These results suggest that WG-GWO holds potential for practical engineering applications.