Research on autonomous path planning and tracking control methods for unmanned electric shovels

Abstract

Achieving fully unmanned operations in large‐scale excavating machinery relies on robust autonomous driving capabilities. Electric shovels, with their steering limitations and reversing difficulties, present unique challenges, compared to lighter, high‐speed‐tracked vehicles. This paper explores these operational and technical challenges and introduces a trajectory planning scheme combining the Guidance‐Hybrid A* algorithm with the dynamic window approach. A high‐precision tracking controller with adjustable factors was also developed. Simulation results show that this approach enhances path‐searching efficiency and prevents reversing paths, with heading error control within 5°. Prototype experiments confirmed the controller's superiority in computational response speed and control stability, maintaining high precision at 0.1 m.