Multi-Drone 3D Path Planning Based on Multi-Strategy Improved Grey Wolf Algorithm

Multiple UAV three-dimensional path planning aims to provide reasonable and feasible flight paths for each UAV to its target while satisfying constraints and avoiding threats. Addressing the issues of slow search speed and poor path quality of current metaheuristic algorithms in solving multi-constraint and multi-threat three-dimensional spatial path planning, a path planning method based on a multi-strategy improved grey wolf optimization algorithm is proposed. Digital elevation models are used to complete three-dimensional spatial environment modeling, and a comprehensive evaluation function incorporating factors such as length, threat, height, and smoothness is established based on the flight scenario. By optimizing the initial population using a composite chaotic sequence and quasi-reverse learning strategy, and considering the crucial impact of upper-level wolves on population convergence in iterative stages, the algorithm’s ability to avoid local optima is enhanced using the elite wolf convex lens reflection learning strategy. Simulation experiments show that the multi-strategy improved grey wolf optimization algorithm improves the optimal, average, and worst values of the comprehensive evaluation function statistics by 6.1%, 5.1%, and 13.3%, respectively, compared to the original algorithm. This validates the effectiveness of the proposed method in solving multi-constraint and multi-threat three-dimensional spatial path planning problems.