@Article{cmc.2025.071319,
AUTHOR = {Chang Su, Liangliang Zhao, Dongbing Xiang},
TITLE = {Dynamic Integration of Q-Learning and A-APF for Efficient Path Planning in Complex Underground Mining Environments},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {86},
YEAR = {2026},
NUMBER = {2},
PAGES = {1--24},
URL = {http://www.techscience.com/cmc/v86n2/64782},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2025.071319}
}