Research on path planning methods for autonomous dump trucks in open-pit mines

Abstract

To address the requirements for left-side driving and the issue of smoothing efficiency for long-distance transportation routes of mining dump trucks in open-pit mine environments, a path planning method that combines a left-side driving hybrid A* algorithm based on Clothoid curve expansion with a smoothing algorithm that minimizes the diagonal vector norm of discrete points is proposed. Initially, Clothoid curves are used instead of traditional circular arcs in the hybrid A* node expansion search process, ensuring the continuity of curvature and meeting the requirements for curvature change rate limitations in the hybrid A* search path. The left-side driving rules are then applied to improve both the cumulative cost and heuristic cost within the hybrid A* framework, incorporating left-turn costs and collision attraction costs to ensure the path veers leftward and follows along the left side of the map border. The heuristic cost is adjusted based on the relation of the expansion direction to the nearest point on the map border, facilitating a heuristic cost map that favors left-side driving and generates a rough global path for leftward travel. Finally, by integrating quadratic programming techniques with the objective of minimizing the diagonal vector norm of discrete points under movement constraints along the coordinate axes, a smoothing optimization model is constructed to smooth the global path. To prevent the curvature from exceeding the vehicle's steering response capability during the smoothing process, a tunnel reduction technique for the feasible region of high-curvature points is employed, limiting the smooth movement of high-curvature points. The results show that the proposed method can generate a global path suitable for left-side driving rules in mining areas, which follows the left side boundary of the map. The heuristic cost improvement significantly reduces the number of nodes expanded by the hybrid A*, favoring the implementation of left-side driving. The discrete point path smoothing method effectively enhances the smoothness of the global path, aiding in vehicle control and tracking. Moreover, thanks to Clothoid curve expansion and the feasible region tunnel reduction technique, the method can effectively handle high-curvature paths, such as loading and unloading, without curvature exceeding limits. Comparing the planning times for paths of different lengths, the path smoothing algorithm significantly reduces the total planning time, with a 4-kilometer path smoothing process requiring only 76 milliseconds. The execution of left-side planning and the reduction in planning time improve the adaptability of the path planning for mining area scenarios.

Keywords

• open-pit mine
• autonomous dump trucks
• path planning
• clothoid curve expansion
• left-side driving hybrid a* algorithm
• discrete point smoothing