Time-Optimal Speed Planning for Open-Pit Mine Autonomous Driving Based on Segmented S-Curve

To address complex open-pit mining scenarios featuring terrain constraints and dynamic demands, a segmented S-Curve-based time-optimal planning framework is proposed—one that effectively overcomes the limitations of traditional unconstrained planning approaches. The dynamic trajectory segmentation framework adaptively adjusts segmentation points through identifying geometric features, thereby decomposing the overall planning task into manageable subproblems. To tackle the real-time computational challenges posed by high-order asymmetric S-Curve trajectory planning, a Gröbner basis precomputation framework offsets algebraic complexity to the offline phase. To prevent planned trajectories from exceeding speed constraints, a dynamic path point insertion correction method is put forward by relaxing acceleration rates. Through comprehensive hardware-in-the-loop (HIL) simulations and scaled-down physical tests, the proposed method exhibits significant improvements in both efficiency and robustness. Experimental validation across five mining routes reveals an average 18.8% reduction in travel time while fully satisfying all constraints. The planner achieves real-time performance with a computation time of 15 ms or less, and maintains precise path tracking (0.22 m RMS error) under varying load conditions—verifying its practical applicability to mining operations.