Excavating trajectory planning of electric shovel based on dynamic excavating volume prediction

Mining electric shovels are one of the core equipments for open-pit mining, and are currently moving towards intelligent and unmanned transformation, with intelligent mining instead of traditional manual operation. In the excavation operation process, due to the complexity and changeability of the material surfaces, different excavation strategies should be adopted to achieve the optimal excavation trajectory. It is an important research direction to realize the unmanned excavation of electric shovels by studying a trajectory planning method that is not limited to fixed resting angle surface, can comprehensively consider the type of material surfaces and aim at the minimum excavation energy consumption per unit volume. Therefore, an electric shovel excavation trajectory planning method based on material surface perception is proposed, which firstly obtains the point cloud data of the material surface through laser radar to perceive the excavation environment, and then carries out horizontal calibration and filtering processing on the point cloud data, and adopts Delaunay triangulation rule to realize the calculation of the dynamic excavation volume of the material. Then, the trajectory model of the dipper tooth tip is established by using the 6th polynomial interpolation method, and the minimum excavation energy consumption per unit volume is taken as the optimization objective, and the whale optimization algorithm is used to plan the excavation trajectories for four kinds of complex stacking surfaces (concave, convex, convex-concave and stepped stacking surfaces). Finally, an electric shovel scaled model test bench is built to experimentally verify the trajectory planning results of the simulation. The correlation coefficient R² values between the test results and the simulation results are both greater than 0.85, and the deviation between the material amount in the bucket and the planned excavation volume is about 5%, which verifies the reliability of the trajectory optimization model and the accuracy of dynamic modeling.