Excavating Trajectory Planning of a Mining Rope Shovel Based on Material Surface Perception

Abstract

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.

Time‐energy consumption optimal path‐constrained trajectory planning of excavator robotics

“…According to Equation (20), the linear acceleration constraint expression with the path jerk extreme value can be obtained as…”

“…Chen et al [19] established velocity and acceleration constraints, used genetic algorithm (GA) optimization to obtain time-energy optimal trajectories under given constraints, and obtained optimal solutions by adjusting the weight coefficients of different operation modes. Feng et al [20] used the gray wolf optimizer to obtain time-energy optimal mining trajectories for four different shapes of pile surface materials through sixth-order polynomial interpolation. However, owing to common problems such as the slow convergence and poor local exploration of intelligent algorithms, the obtained optimal solution may be a local optimal solution.…”

“…Feng et al. [20] used the gray wolf optimizer to obtain time–energy optimal mining trajectories for four different shapes of pile surface materials through sixth‐order polynomial interpolation. However, owing to common problems such as the slow convergence and poor local exploration of intelligent algorithms, the obtained optimal solution may be a local optimal solution.…”