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Research on autonomous path planning and tracking control methods for unmanned electric shovels
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2024-12-21 , DOI: 10.1111/mice.13402
Xiaodan Tan, Guoqiang Wang, Guohua Wu, Zongwei Yao, Yongpeng Wang, Qingxue Huang
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2024-12-21 , DOI: 10.1111/mice.13402
Xiaodan Tan, Guoqiang Wang, Guohua Wu, Zongwei Yao, Yongpeng Wang, Qingxue Huang
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.
中文翻译:
无人电铲自主路径规划与跟踪控制方法研究
在大型挖掘机械中实现完全无人操作依赖于强大的自动驾驶能力。与更轻的高速履带车辆相比,电铲具有转向限制和倒车困难,带来了独特的挑战。本文探讨了这些操作和技术挑战,并介绍了一种将 Guidance-Hybrid A* 算法与动态窗口方法相结合的轨迹规划方案。还开发了一种具有可调系数的高精度跟踪控制器。仿真结果表明,这种方法提高了路径搜索效率并防止了反向路径,航向误差控制在 5° 以内。原型实验证实了控制器在计算响应速度和控制稳定性方面的优势,在 0.1 m 处保持了高精度。
更新日期:2024-12-21
中文翻译:
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无人电铲自主路径规划与跟踪控制方法研究
在大型挖掘机械中实现完全无人操作依赖于强大的自动驾驶能力。与更轻的高速履带车辆相比,电铲具有转向限制和倒车困难,带来了独特的挑战。本文探讨了这些操作和技术挑战,并介绍了一种将 Guidance-Hybrid A* 算法与动态窗口方法相结合的轨迹规划方案。还开发了一种具有可调系数的高精度跟踪控制器。仿真结果表明,这种方法提高了路径搜索效率并防止了反向路径,航向误差控制在 5° 以内。原型实验证实了控制器在计算响应速度和控制稳定性方面的优势,在 0.1 m 处保持了高精度。