Abstract: Path planning is a critical component of autonomous unmanned aerial vehicle (UAV) navigation systems, yet traditional and sampling-based methods encounter limitations in three-dimensional (3D) path planning. This paper offers a structured review of applicable algorithms in 3D space, introduces the state-of-the-art techniques, and addresses cutting-edge challenges associated with UAV heuristic decomposition methods. Furthermore, we develop a Q-learning guided grey wolf optimizer (QGWO) to tackle the UAV 3D path planning problem in complex scenarios. QGWO incorporates two exploration strategies from the aquila optimizer into the grey wolf optimizer, enhancing its capacity to escape local optima and utilize the population for broader exploration. Q-learning guides the search process, enabling the algorithm to store iterative information, accelerate convergence, and balance exploration and exploitation. Additionally, Laplace crossover perturbs the positions of the α and β wolves, preventing the algorithm from becoming trapped in local optima. To validate its effectiveness, QGWO and ten advanced heuristic algorithms were tested in 3D path planning simulations across six terrain scenarios of varying complexity. Experimental results demonstrate that QGWO achieves optimal cost metrics, outperforming the original grey wolf optimizer by up to 1.34% and significantly surpassing other algorithms with a 70.92% reduction in standard deviation. This highlights the effectiveness and robustness of QGWO in 3D path planning for UAV. Moreover, the Wilcoxon rank sum test shows that the null hypothesis is rejected in 98.33% of cases, confirming the statistical superiority of the proposed QGWO.

Binbin Tu, Fei Wang, Xiaowei Han and Xibei Fu. “Q-learning Guided Grey Wolf Optimizer for UAV 3D Path Planning”. International Journal of Advanced Computer Science and Applications (ijacsa) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150747

@article{Tu2024,
title = {Q-learning Guided Grey Wolf Optimizer for UAV 3D Path Planning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150747},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150747},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Binbin Tu and Fei Wang and Xiaowei Han and Xibei Fu}
}