基于动态挖掘量预测的电动的挖掘轨迹规划
Juan Wu1,2,3, Rui Liu4,5,6, Baoguo Lin4,5,6
1College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China. wujuan@tyut.edu.cn.
Scientific reports
|January 18, 2025
概括
本研究介绍了一种用于智能采矿电的新轨迹规划方法. 它优化了挖掘路径,减少了能源消耗,并在复杂的采矿环境中提高了效率.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 开采采矿的电动正在过渡到智能,无人操作.
- 优化挖掘轨迹是非常重要的,因为复杂和可变的材料表面.
- 目前的方法通常依赖于固定的休息角度,限制了适应性.
研究的目的:
- 开发一种基于材料表面感知的电动挖掘轨迹规划方法.
- 为了实现无人操作的每单位体积最低挖掘能耗.
- 创建一种适应各种复杂材料堆叠表面的方法.
主要方法:
- 使用激光雷达点云数据感知挖掘环境.
- 处理点云数据 (水平校准,过) 并通过Delaunay三角测量计算动态挖掘体积.
- 使用第6个多项式插值建立一个子牙尖轨迹模型,并使用鱼优化算法进行优化.
- 规划曲线的,凸,凸-,和阶梯堆叠表面.
主要成果:
- 拟议的方法成功地规划了各种复杂表面的挖掘轨迹.
- 在一个缩放模型测试台上的实验验证显示了与模拟结果的高相关性 (R2 > 0.85).
- 桶中的实际和计划材料体积之间的偏差约为5%.
结论:
- 开发的轨迹优化模型对于智能电动是可靠的.
- 动态建模和规划方法准确预测挖掘结果.
- 这项研究有助于推进自主采矿操作.
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