Research on unmanned mining truck path planning based on grey wolf optimization adaptive hybrid A* and artificial potential field

To enhance the path planning capabilities of unmanned mining trucks in open-pit mining scenarios, a grey wolf optimization-based adaptive hybrid A* and artificial potential field (GWO-HAPF)method was proposed. The proposed method emploied the grey wolf optimization algorithm to adaptively adjust the key parameters of the hybrid A*(HA*) algorithm, achieving a multi-objective balance among path length, smoothness, and planning time. This effectively overcame the HA* algorithm's limited adaptability to fixed parameter settings, significantly improving the quality and adaptability of global path planning. For local path planning, an improved artificial potential field method was adopted, optimizing the repulsive force function and incorporating an escape force mechanism, which effectively enhanced real-time obstacle avoidance feasibility and path smoothness. Experimental results demonstrate that, compared to the standard HA* algorithms and the improved hybrid A* (IHA*) algorithms, GWO-HAPF improves computational efficiency in global planning by an average of 80% and 14.9%, respectively, reduces path length by over 9.8%, and increases smoothness by over 53%. In local path planning, GWO-HAPF achieves a planning time that is 95.8% shorter than IHA*, while its smoothness improves to 10.19% of IHA*. These findings indicate that the proposed method exhibits outstanding advantages in planning efficiency, path length, smoothness, and real-time obstacle avoidance, showcasing its practical application value in path planning for unmanned mining trucks in open-pit mining scenarios.