Jiang Song, Yang Jiaxi, Cui Zhixiang, et al. Path planning method for autonomous haul trucks on small-curvature curves in open-pit minesJ. Journal of Mine Automation,2026,52(6):11-18, 134. DOI: 10.13272/j.issn.1671-251x.2026020040
Citation: Jiang Song, Yang Jiaxi, Cui Zhixiang, et al. Path planning method for autonomous haul trucks on small-curvature curves in open-pit minesJ. Journal of Mine Automation,2026,52(6):11-18, 134. DOI: 10.13272/j.issn.1671-251x.2026020040

Path planning method for autonomous haul trucks on small-curvature curves in open-pit mines

  • Existing path planning algorithms for open-pit coal mine haul trucks often suffer from poor path smoothness, frequent steering, and abrupt curvature variations when operating in small-curvature curves characterized by unstructured environments, strong spatiotemporal disturbances, and stringent geometric constraints. To address these problems, this study proposed an improved Hybrid A* algorithm for path planning of haul trucks in small-curvature curves of open-pit coal mines. The heuristic function was improved by introducing a distance penalty function, in which collision-risk, collision-potential, and collision-safe zones were defined to dynamically adjust node expansion priorities. A cost function was further introduced to assign greater weights to forward driving and stable heading states, guiding the haul truck to travel along the centerline of the curve, reducing unnecessary steering maneuvers, and avoiding planning failure caused by abrupt curvature changes. A multi-objective nonlinear optimization model incorporating a smoothing cost, a distance cost, a deviation cost, and a curvature variation rate cost was constructed. Position and curvature constraints were imposed to ensure compliance with vehicle kinematic requirements, and the planned path was further smoothed using quadratic programming and an interior-point solver. Simulation and field application results showed that, in typical small-curvature curve scenarios, the improved Hybrid A* algorithm generated paths that were closer to the curve centerline and exhibited better continuity and smoothness than those generated by the Dijkstra, Rapidly-Exploring Random Tree (RRT), and Hybrid A* algorithms. In addition, the proposed algorithm achieved a larger minimum safety margin, lower curvature and curvature variation rate, and significantly outperformed the comparison algorithms in terms of real-time dynamic obstacle avoidance and reliability, thereby improving the driving safety and operational stability of haul trucks in complex small-curvature curve scenarios of open-pit coal mines.
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