Digging Trajectory Optimization for Cable Shovel Robotic Excavation based on a Multi-Objective Genetic Algorithm

Energies

Zongwei Yao¹  Guoqiang Wang¹  Qiushi Bi¹  Robert Hall²  Yongpeng Wang³ 

[1] School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China;School of Mining and Petroleum Engineering, University of Alberta, Edmonton T6G 2H5, AB, Canada;Taiyuan heavy industry Co., LTD., Taiyuan 030024, China;

关键词: digging trajectory;    cable shovel;    robotic excavation;    multi-objective genetic algorithm;   

DOI  :  10.3390/en13123118

来源: DOAJ

【 摘 要 】

As one of the most essential earth-moving equipment, cable shovels significantly influence the efficiency and economy in the open-pit mining industry. The optimal digging trajectory planning for each cycle is the base for achieving effective and energy-saving operation, especially for robotic excavation, in which case, the digging trajectory can be precisely tracked. In this paper, to serve the vision of cable shovel automation, a two-phase multi-objective genetic algorithm was established for optimal digging trajectory planning. To be more specific, the optimization took digging time and energy consumption per payload as objects with the constraints of the limitations of the driving system and geometrical conditions. The WK-55-type cable shovel was applied for the validation of the effectiveness of the multi-objective optimization method for digging trajectories. The digging performance of the WK-55 cable shovel was tested in the Anjialing mining site to establish the constraints. Besides, the digging parameters of the material were selected based on the tested data to make the optimization in line with the condition of the real digging operations. The optimization results for different digging conditions indicate that the digging time decreased from an average of $20\mathrm{s}$ to $10\mathrm{s}$ after the first phase optimization, and the energy consumption per payload reduced by $13.28\%$ after the second phase optimization, which validated the effectiveness and adaptivity of the optimization algorithm established in this paper.

【 授权许可】

Unknown