Improved grey wolf algorithm based on dynamic weight and logistic mapping for safe path planning of UAV low-altitude penetration

Unmanned aerial vehicle (UAV) has been widely used in many fields, especially in low-altitude penetration defence, which showcases superior performance. UAV requires obstacle avoidance for safe flight and must adhere to various flight constraints, such as altitude changes and turning angles, during path planning. Excellent flight paths can enhance flight efficiency and safety, saving time and energy when performing specific tasks, directly impacting mission accomplishment. To address these challenges, this paper improves the original grey wolf algorithm (GWO). In this enhanced version, the three head wolves randomly assign influence weights to execute the position updating mechanism. A dynamic weight influence strategy is designed, which accelerates convergence in the late optimization stages, aiding in finding the global optimum. Meanwhile, the logistic mapping is introduced into the convergence factor, and a micro-vibrational convergence factor is constructed. This allows the algorithm to have a better ability to find a globally optimal solution in the search space while also being able to search deeper using areas near the currently known information. In order to validate the proposed algorithm, a simulated flight environment is established, conducting simulation experiments within safe flight environments featuring 5, 10, and 15 obstacles. Comparative analysis with seven other algorithms demonstrates the superiority of the proposed algorithm. The experimental results demonstrate that the proposed algorithm has better superiority. In terms of path length on three maps, DLGWO paths are 10.3 km, 15.5 km, and 2.6 km shorter than the second-placed MEPSO, SOGWO, and WOA, respectively. Furthermore, the planned path in this study exhibits the smallest fluctuations in altitude and turning angles.