Energy-minimum optimization of the intelligent excavating process for large cable shovel through trajectory planning

Abstract

Large cable shovel (LCS) is a complex engineering machine which is widely used in the open pit mine. It is characterized by low efficiency, high maintenance cost, and high energy consumption if manipulated by an inexperienced operator. To address these challenges, intelligentization could be a feasible solution. In this work, an intelligent excavation system is put forward and the corresponding energy-minimum optimization through trajectory planning of the optimal excavation is developed to realize the intelligentization of the LCS. Firstly, the excavating resistance acting on the dipper is modeled and the corresponding forces are analyzed. Then, by establishing the kinetics and dynamic models of the excavating process, the point to point (PTP) trajectory planning method is developed by setting the objective to minimize the energy consumption per unite volume material. Polynomial curves in different degrees are used in the PTP planning method and the optimal one is compared with the conventional S-curve in terms of the excavating performance. To explore the advantage of the proposed intelligent system and the corresponding trajectory planning based energy-minimum optimization method, four types of ore piles with different pile angles are compared with respect to the excavating performance. Results show that the larger the pile angle is, the later the maximum hoist power and crowd power will appear. Further, the effects on the excavating performance from different ore piles with complex terrains, including the flat type, concave type, convex type, and concave-convex type, are also studied in the numerical experiments. It is found that the trajectory based energy-minimum optimization method for the intelligent LCS can significantly save excavation energy as well as keep sufficient fill factor.