A Novel Multi-Strategy Hybrid Gray Wolf Optimization for Multi-UAV Cooperative Path Planning

Abstract

In recent years, unmanned aerial vehicles (UAVs) cooperative path planning is attracting more and more research attention. For the multi-UAV cooperative path planning problem, the path planning problem in three-dimensional (3D) environment is transformed into an optimization problem by introducing the fitness function and constraints such as minimizing path length, maintaining a low and stable flight altitude, and avoiding threat zones. A multi-strategy hybrid grey wolf optimization (MSHGWO) algorithm is proposed to address this problem. Firstly, a chaotic Cubic mapping is introduced to initialize the grey wolf positions to make its initial position distribution more uniform. Secondly, an adaptive adjustment weight factor is designed, which can adjust the movement weight based on the rate of fitness value decrease within a unit Euclidean distance, thereby improving the quality of the population. Finally, an elite opposition-based learning strategy is introduced to improve the population diversity so that the population jumps out of the local optimum. Simulation results indicate that the MSHGWO is capable of generating constraint-compliant paths for each UAV in complex 3D environments. Furthermore, the MSHGWO outperforms other algorithms in terms of convergence speed and solution quality. Meanwhile, flight experiments were conducted to validate the path planning capability of MSHGWO in real-world obstacle environments, further demonstrating the feasibility of the proposed multi-UAV cooperative path planning approach.

Keywords

unmanned aerial vehicle (UAV) / cooperative path planning / gray wolf optimization