@article{Su2026, 
author = {Chang Su and Liangliang Zhao and Dongbing Xiang},
title = {Dynamic Integration of Q-Learning and A-APF for Efficient Path Planning in Complex Underground Mining Environments},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {86},
number = {2},
pages = {1-24},
keywords = {Q-learning, A* algorithm, artificial potential field, path planning, hybrid algorithm},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.071319},
doi = {10.32604/cmc.2025.071319},
abstract = {To address low learning efficiency and inadequate path safety in spraying robot navigation within complex obstacle-rich environments—with dense, dynamic, unpredictable obstacles challenging conventional methods—this paper proposes a hybrid algorithm integrating Q-learning and improved A*-Artificial Potential Field (A-APF). Centered on the Q-learning framework, the algorithm leverages safety-oriented guidance generated by A-APF and employs a dynamic coordination mechanism that adaptively balances exploration and exploitation. The proposed system comprises four core modules: (1) an environment modeling module that constructs grid-based obstacle maps; (2) an A-APF module that combines heuristic search from A* algorithm with repulsive force strategies from APF to generate guidance; (3) a Q-learning module that learns optimal state-action values (Q-values) through spraying robot–environment interaction and a reward function emphasizing path optimality and safety; and (4) a dynamic optimization module that ensures adaptive cooperation between Q-learning and A-APF through exploration rate control and environment-aware constraints. Simulation results demonstrate that the proposed method significantly enhances path safety in complex underground mining environments. Quantitative results indicate that, compared to the traditional Q-learning algorithm, the proposed method shortens training time by 42.95% and achieves a reduction in training failures from 78 to just 3. Compared to the static fusion algorithm, it further reduces both training time (by 10.78%) and training failures (by 50%), thereby improving overall training efficiency.}
}