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

Area of Science:

• Robotics and Automation
• Geotechnical Engineering
• Mechanical Engineering

Background:

• Open-pit mining relies heavily on mining rope shovels (MRS), which are transitioning to intelligent, unmanned operations.
• Efficient trajectory planning is crucial for the intelligent transformation of MRS, demanding methods that consider real-time excavation environments.

Purpose of the Study:

• To propose an MRS excavation trajectory planning method based on material surface perception for intelligent mining.
• To develop an energy-efficient and time-saving trajectory planning approach for MRS.

Main Methods:

• Utilized laser radar to obtain point cloud data for material surface reconstruction and modeling.
• Performed kinematics and dynamics analysis using Product of Exponentials (PoE) and Lagrange equations.
• Employed sixth-order polynomial interpolation for trajectory modeling and Grey Wolf Optimizer for iterative optimization.

Main Results:

• The proposed method successfully planned trajectories for various material surface shapes.
• Experimental validation confirmed the accuracy of the calculation model, with actual forces aligning with planned forces.
• The method ensured that the full bucket rate and other parameters met predefined constraints.

Conclusions:

• The material surface perception-based trajectory planning method is feasible for MRS in diverse excavation conditions.
• This approach contributes to the intelligent and unmanned transformation of open-pit mining operations.
• The developed method enhances excavation efficiency and energy savings.