Search

Open-pit mines, characterized by complicated environmental conditions and unstructured roads, pose challenges for the path planning of mine trucks, marked by inefficiency due to long-distance search and the need to adhere to the left-hand traffic rules common in mining settings. This paper introduces an optimization algorithm for adaptive dual-layer search path planning based on the left-hand traffic rules in mining areas, aiming to improve the navigation performance of mine trucks within these areas while ensuring the path safety and the real-time planning. The upper-layer search, employing the rapidly-exploring random tree (RRT) algorithm and accommodating the environmental characteristics of mining areas, features adaptive adjustments of search step sizes, achieving efficient searches with larger step sizes in open areas, while detailed searches with smaller step sizes in narrow or winding areas. Moreover, the search process is further optimized by setting range constraints for left-hand driving, to prevent potential vehicle conflicts and facilitate rapid convergence of path search. The lower-layer search incorporates a hybrid A* algorithm, effectively narrowing search space and enhancing the real-time nature of path planning through a reward and penalty mechanism for path selection. Furthermore, Reeds-Shepp (RS) curve and an improved cubic spline curve are applied to smooth the resultant path, not only optimizing path curvature but also ensuring that the mine truck reaches the target location in an optimal posture. Further optimization through the gradient descent method enables the efficient generation of safe and smooth paths. Experimental results showed a 14.8% reduction in path generation time and a 0.011 m-1 decrease in path curvature, demonstrating a significant enhancement in the navigation efficiency and safety during autonomous truck driving in mining areas.