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

Qiushi Bi, Guoqiang Wang, Yongpeng Wang, Zongwei Yao and Robert Hall
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Qiushi Bi: School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
Guoqiang Wang: School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
Yongpeng Wang: Taiyuan Heavy Industry Co., LTD., Taiyuan 030024, China
Zongwei Yao: School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
Robert Hall: School of Mining and Petroleum Engineering, University of Alberta, Edmonton, AB T6G 2H5, Canada

Energies, 2020, vol. 13, issue 12, 1-20

Abstract: 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 s to 10 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.

Keywords: digging trajectory; cable shovel; robotic excavation; multi-objective genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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