Multi-objective Optimal Excavator Movement Planning for Autonomous Operation

doi:10.3901/JME.2022.07.237

Abstract

Abstract: Motion planning for backhoe excavators is a prerequisite for autonomous operation. Aiming at the shortest excavation time and the lowest excavation energy consumption, this paper proposes a method of excavation movement planning. Taking a 21-ton hydraulic backhoe as the research object, first combine the collected excavation work load spectrum and the corresponding bucket tip trajectory, and on the premise that the full bucket ratio is greater than 0.9, extract an excavation trajectory with the least energy consumption per unit mass as the target Optimal motion planning for the trajectory. The dynamic model of the working device of the excavator is further established, and the accuracy of the dynamic model is verified by the measured data. On this basis, an optimization model with the initial speed and acceleration of the bucket tip as the optimization variables, and the shortest excavation time and the lowest excavation energy consumption as the goal is established. After introducing weight coefficients to characterize different mining operation modes, genetic algorithms are used to optimize the optimization model. And analyze the influence of different weight coefficients on the planning results, and the results show that: for different weight coefficients, the optimization model can obtain the optimal solution, which verifies the effectiveness of the optimization method.

Key words: excavator, trajectory planning, genetic algorithm, autonomous operations

CLC Number:

TU621

CHEN Jinshi, ZHANG Miaomiao, BI Qiushi, LI Yongqi, HUO Dongyang, QI Hongyang. Multi-objective Optimal Excavator Movement Planning for Autonomous Operation[J]. Journal of Mechanical Engineering, 2022, 58(7): 237-245.

References

[1] 李运华,范茹军,杨丽曼,等. 智能化挖掘机的研究现状与发展趋势[J]. 机械工程学报,2020,56(13):165-178. LI Yunhua,FAN Rujun,YANG Liman,et al. Research status and development trend of intelligent excavators[J]. Journal of Mechanical Engineering,2020,56(13):165-178.
[2] 邹智红. 反铲液压挖掘机挖掘性能综合评价体系构建及其应用研究[D]. 重庆:重庆大学,2019. ZOU Zhihong. Establishment of comprehensive evaluation system of digging performance for hydraulic excavator and its applications[D]. Chongqing:Chongqing University,2019.
[3] 李虹,张韵悦,孙志毅,等. 挖掘机分段多项式的时间最优轨迹规划[J]. 中国工程机械学报,2020,18(1):7-13. LI Hong,ZHANG Yunyue,SUN Zhiyi,et al. Piecewise polynomial time optimal trajectory planning for excavator[J]. Chinese Journal of Construction Machinery,2020,18(1):7-13.
[4] KIM Y B,HA J,KANG H,et al. Dynamic optimal trajectory for earthmoving excavator[J]. Automation in Construction,2013,35:568-578.
[5] YOO S,PARK C,YOU S,et al. A dynamic-based optimal trajectory generation for controlling an automated excavator[J]. Proceedings of the Institution of Mechanical Engineers,Part C:Journey of Mechanical Engineering Science,2010,224(10):2109-2119.
[6] ZHAO Y, WANG J, ZHANG Y,et al. A novel method of soil parameter identification and force prediction for automatic excavation[J]. IEEE Access,2020,224(10):2109-2129.
[7] 邵辉,叶贤成,孙祥云. 液压挖掘机的两级平滑挖掘规划方法[J]. 华侨大学学报,2016,37(5):610-614. SHAO Hui,YE Xiancheng,SUN Xiangyun. Research on two-level planning method of smoothly excavating for hydraulic excavator[J]. Journal of Huaqiao University,2016,37(5):610-614.
[8] 李勇. 自动作业液压挖掘机的铲斗轨迹控制和回转力矩控制研究[D]. 杭州:浙江大学,2019. LI Yong. Study on bucket trajectory and swing torque control for the autonomous hydraulic excavator[D]. Hangzhou:Zhejiang University,2019.
[9] WANG X B,SUN W,LI E Y,et al. Energy-minimum optimization of the intelligent excavating process for large cable shovel through trajectory planning[J]. Structural and Multidisciplinary Optimization,2018,58(5):2219-2237.
[10] PARK B. Development of a virtual reality excavator simulator:A mathematic model of excavator digging and a calculation methodology[D]. Blacksburg:Virginia Polytechnic Inst. State Univ.,2002.
[11] ZOU Z,CHEN J,PANG X. Task space-based dynamic trajectory planning for digging process of a hydraulic excavator with the integration of soil-bucket interaction[J]. Proceedings of the Institution of Mechanical Engineers Part K:Journal of Multi-body Dynamic,2019,233(3):598-616.
[12] KOIVO A J,THOMA M,KOCAOGLA E,et al. Modeling and control of excavator dynamic during digging during digging operation[J]. Journey of Aerospace Engineering,1996,9(1):10-18.
[13] 龚文. 液压挖掘机半物理仿真平台及上车机构实时仿真研究[D]. 杭州:浙江大学,2010. GONG Wen. Hardware-in-the-loop simulation station and arm real-time simulation in hydraulic excavator[D]. Hangzhou:Zhejiang University,2010.
[14] 徐弓岳,丁华锋,孙玉玉. 基于改进非支配排序遗传算法的正铲挖掘机工作装置优化设计[J]. 机械工程学报,2016,52(21):35-43. XU Gongyue,DING Huafeng,SUN Yuyu. Optimization of face-shovel excavator's attachment based on improved nsga-Ⅱ[J]. Journal of Mechanical Engineering,2016,52(21):35-43.
[15] 徐海黎,解祥荣,庄健,等. 工业机器人的最优时间与最优能量轨迹规划[J]. 机械工程学报,2010,46(9):19-25. XU Haili,XIE Xiangrong,ZHUANG Jian,et al. Global time-energy optimal planning of industrial robot trajectories[J]. Journal of Mechanical Engineering,2010,46(9):19-25.
[16] WANGHUI B U,LIU Z. Motion planning for industrial robots based on task process decomposition[J]. Chinese Journal of Mechanical Engineering,2010,23(2):135-141.