A hybrid A path planning algorithm for underground load-haul-dump vehicles guided by reference states

• Published in: Measurement science & technology Vol. 36; no. 10; p. 106215
• Main Authors: Wu, Jia-xi, Peng, Pingan, Wang, Liguan, Wang, Jiaheng, Liu, Yongchuan, Wu, Zhaohao
• Format: Journal Article
• Language: English
• Published: 31.10.2025
• ISSN: 0957-0233; 1361-6501
• DOI: 10.1088/1361-6501/ae10ce

As a critical enabler of trackless mining operations, load-haul-dump (LHD) vehicles rely heavily on efficient and safe path planning to support the industrial deployment of autonomous driving systems. However, existing planning algorithms often suffer from heavy computational demands, limited adaptability to the constrained kinematics of articulated LHDs, and insufficient robustness in complex underground environments. To address these challenges, we propose a Hybrid A* path planning method guided by high-level reference states. The method first integrates the A* algorithm with generalized Voronoi diagrams to rapidly generate a reference state set along tunnel centerlines. This reference serves as a structural guide for downstream search. Then, a region-aware dynamic node expansion strategy and a multi-objective heuristic cost function are introduced to improve planning efficiency and path quality. Comparative evaluations across three representative underground tunnel scenarios demonstrate that the proposed approach outperforms baseline methods in terms of safety, efficiency, and robustness, providing a viable solution for motion planning of LHDs in confined underground spaces.