Path Planning of UAV Based on Improved Adaptive Grey Wolf Optimization Algorithm

Aiming at the three-dimensional path planning of unmanned aerial vehicle (UAV) in the complex environment of material delivery in earthquake-stricken areas, this paper proposes an improved adaptive grey wolf optimization algorithm (AGWO) based on the grey wolf optimization algorithm (GWO). There are two main contributions of the proposed method. Firstly, we propose an adaptive convergence factor adjustment strategy and an adaptive weight factor to update the individual's position. The effectiveness of the improved algorithm is verified by the convergence analysis and the test function simulation experiment. Secondly, the improved algorithm is applied to UAV path planning, the environmental map model is established by integrating digital elevation map and equivalent mountain threat model, and the performance evaluation function is established by fitting the calculated track length. The simulation results show that the improved AGWO is superior to the traditional intelligent algorithm in convergence precision, speed and stability performance, and it is effective for 3D trajectory optimization in complex environment.

Automated summary

This paper proposes an improved adaptive grey wolf optimization algorithm (AGWO) for UAV path planning in complex environments, which has been verified to be effective through convergence analysis and test function simulation experiments. The AGWO outperforms traditional intelligent algorithms in terms of convergence precision, speed, and stability for 3D trajectory optimization in complex environments.