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

Author

Listed:
  • 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
    State Key Laboratory of Mining Equipment and Intelligent Manufacturing, Taiyuan 030024, China)

  • Zongwei Yao

    (School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
    Key Laboratory of CNC Equipment Reliability, Ministry of Education, Changchun 130025, China)

  • Robert Hall

    (School of Mining and Petroleum Engineering, University of Alberta, Edmonton, AB T6G 2H5, Canada)

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.

Suggested Citation

  • Qiushi Bi & Guoqiang Wang & Yongpeng Wang & Zongwei Yao & Robert Hall, 2020. "Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a Multi-Objective Genetic Algorithm," Energies, MDPI, vol. 13(12), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3118-:d:372356
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    Citations

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    Cited by:

    1. Laura Tribioli & Manfredi Villani, 2022. "Electrified Powertrains for a Sustainable Mobility: Topologies, Design and Integrated Energy Management Strategies," Energies, MDPI, vol. 15(9), pages 1-2, April.
    2. Jing Yang & Yingjie Gao & Rui Guo & Qingshan Gao & Jingyi Zhao, 2023. "Research on Excavator Trajectory Control Based on Hybrid Interpolation," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    3. Topno, Seema Ashishan & Sahoo, Lalit Kumar & Umre, B.S., 2021. "Energy efficiency assessment of electric shovel operating in opencast mine," Energy, Elsevier, vol. 230(C).

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