Feng Yinnan, Wu Juan, Lin Baoguo, Guo Chenhao
College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Shanxi Province Engineer Technology Research Center for Mine Fluid Control, Taiyuan 030024, China.
Sensors (Basel). 2023 Jul 25;23(15):6653. doi: 10.3390/s23156653.
The mining rope shovel (MRS) is one of the core pieces of equipment for open-pit mining, and is currently moving towards intelligent and unmanned transformation, replacing traditional manual operations with intelligent mining. Aiming at the demand for online planning of an intelligent shovel excavation trajectory, an MRS excavating trajectory planning method based on material surface perception is proposed here. First, point cloud data of the material stacking surface are obtained through laser radar to perceive the excavation environment and these point cloud data are horizontally calibrated and filtered to reconstruct the surface morphology of the material surface to provide a material surface model for calculation of the mining volume in the subsequent trajectory planning. Second, kinematics and dynamics analysis of the MRS excavation device are carried out using the Product of Exponentials (PoE) and Lagrange equation, providing a theoretical basis for calculating the excavation energy consumption in trajectory planning. Then, the trajectory model of the bucket tooth tip is established by the method of sixth-order polynomial interpolation. The unit mass excavation energy consumption and unit mass excavation time are taken as the objective function, and the motor performance and the geometric size of the MRS are taken as constraints. The grey wolf optimizer is used for iterative optimization to realize efficient and energy-saving excavation trajectory planning of the MRS. Finally, trajectory planning is carried out for material surfaces with four different shapes (typical, convex, concave, and convex-concave). The results of experimental validation show that the actual hoist and crowd forces are essentially consistent with the planned hoist and crowd forces in terms of the peak value and trend variations, verifying the accuracy of the calculation model and confirming that the full bucket rate and various parameters meet the constraints. Therefore, the trajectory planning method based on material surface perception are feasible for application to different excavation conditions.
矿用绳铲是露天采矿的核心设备之一，目前正朝着智能化和无人化转型，以智能采矿取代传统的人工操作。针对智能铲挖掘轨迹在线规划的需求，提出了一种基于物料表面感知的矿用绳铲挖掘轨迹规划方法。首先，通过激光雷达获取物料堆积表面的点云数据，以感知挖掘环境，并对这些点云数据进行水平校准和滤波，重构物料表面的形态，为后续轨迹规划中计算挖掘量提供物料表面模型。其次，利用指数积（PoE）和拉格朗日方程对矿用绳铲挖掘装置进行运动学和动力学分析，为轨迹规划中计算挖掘能耗提供理论依据。然后，采用六阶多项式插值法建立斗齿尖的轨迹模型。以单位质量挖掘能耗和单位质量挖掘时间为目标函数，以矿用绳铲的电机性能和几何尺寸为约束条件，利用灰狼优化器进行迭代优化，实现矿用绳铲高效节能的挖掘轨迹规划。最后，对四种不同形状（典型、凸形、凹形和凹凸形）的物料表面进行轨迹规划。实验验证结果表明，实际提升力和推压阻力在峰值和趋势变化方面与规划的提升力和推压阻力基本一致，验证了计算模型的准确性，并确认满斗率和各项参数满足约束条件。因此，基于物料表面感知的轨迹规划方法适用于不同挖掘条件。
