摘要
为实现智能化电铲在露天矿山实时节能的挖掘,提出了一种基于堆料面预测模型的能耗最优挖掘轨迹规划方法.该方法通过激光雷达获取实际堆料面点云感知外部环境,并基于点云数据,采用多项式响应面(PRS)法对堆料面形貌进行建模,实现轨迹规划中动态挖掘体积计算;然后,采用拉格朗日方程建立电铲工作装置动力学模型计算挖掘能耗,采用高次多项式对挖掘轨迹进行插值,将挖掘时间和能耗分别作为优化变量和优化目标,以挖掘过程中几何条件与电机性能等为约束,实现真实料场环境中高效的三维挖掘轨迹规划.实验结果表明,基于多项式响应面法的堆料面模型精度能达到95%以上且建模时间在0.05 s内;挖掘轨迹规划可满足实时性要求,计算结果可靠且所得轨迹能有效应用于电铲自主挖掘.
To achieve the effective and energy-saving operation of intelligent electric shovel in the open-pit mine, an energy-minimum excavation trajectory planning method based on the stockpile surface prediction model is proposed. In the proposed method, the point cloud of the stockpile surface is obtained by LiDAR to perceive the on-site working conditions. Based on the point cloud data, the profile of the stockpile surface is constructed by polynomial response surface(PRS) method, and the dynamic excavation volume in trajectory planning can be calculated. Then, the Lagrange equation is applied to establish the dynamic model of the electric shovel for calculating the energy consumption during excavation, and the excavation trajectory is interpolated by a high-order polynomial. The excavation time and energy consumption are taken as the optimized variable and target, respectively, in addition, motor performance and geometric constraints are also explicitly added during the optimization to generate an efficient 3 D excavation trajectory planning in real stockpile. Series of experimental results show that the accuracy of the stockpile surface model based on PRS method is more than 95% and the time consuming is within 0.05 s. Further, the excavation trajectory planning method can satisfy the real-time requirement for actual excavation, the result of calculation during excavation is reliable, so it can be effectively applied to autonomous excavation of electric shovel.
作者
黎柿汪
张天赐
付涛
李光
孙刚
宋学官
LI Shiwang;ZHANG Tianci;FU Tao;LI Guang;SUN Gang;SONG Xueguan(School of Mechanical Engineering,Dalian University of Technology,Dalian 116024,China;State Key Laboratory of Mining Equipment and Inelligent Manufacturing,Taiyuan Heavy Industry Co.,Ltd.,Taiyuan 030024,China)
出处
《大连理工大学学报》
CAS
CSCD
北大核心
2022年第6期582-591,共10页
Journal of Dalian University of Technology
基金
国家自然科学基金资助项目(52075068)。
关键词
智能电铲
多项式响应面(PRS)法
堆料面预测模型
能耗最优
轨迹规划
intelligent shovel
polynomial response surface(PRS)method
stockpile surface prediction model
energy-minimum optimization
trajectory planning
