面向自主作业的挖掘机多目标最优挖掘运动规划

doi:10.3901/JME.2022.07.237

机械工程学报 ›› 2022, Vol. 58 ›› Issue (7): 237-245.doi: 10.3901/JME.2022.07.237

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面向自主作业的挖掘机多目标最优挖掘运动规划

陈晋市¹, 张淼淼¹, 毕秋实¹, 李永奇², 霍东阳¹, 齐洪阳¹

1. 吉林大学机械与航空航天工程学院长春 130025;2. 徐州徐工液压件有限公司徐州 221004

收稿日期:2021-04-13 修回日期:2021-08-12 出版日期:2022-04-05 发布日期:2022-05-20
通讯作者: 毕秋实(通信作者),男,1988年出生,博士研究生,讲师,博士后。主要研究方向为机械设计理论与方法。E-mail:bqs@jlu.edu.cn
作者简介:陈晋市,男,1983年出生,博士,副教授,博士研究生导师。主要研究方向为流体传动与控制。E-mail:spreading@jlu.edu.cn
基金资助:
国家重点研发计划资助项目(2018YFB20009)。

Multi-objective Optimal Excavator Movement Planning for Autonomous Operation

CHEN Jinshi¹, ZHANG Miaomiao¹, BI Qiushi¹, LI Yongqi², HUO Dongyang¹, QI Hongyang¹

1. College of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025;2. Xuzhou Xugong Hydraulic Parts Co. LTD, Xuzhou 221004

Received:2021-04-13 Revised:2021-08-12 Online:2022-04-05 Published:2022-05-20

摘要/Abstract

摘要： 对反铲挖掘机进行运动规划是实现自主作业前提。以最短挖掘时间和最低挖掘能耗为目标，提出了一种挖掘运动规划的方法。以21 t级反铲液压挖掘机为研究对象，首先结合采集的挖掘作业载荷谱和对应的斗尖轨迹，在保证满斗率大于0.9前提下，提取一条单位质量能耗最小的挖掘轨迹作为目标轨迹进行最优运动规划。进一步建立了挖掘机工作装置动力学模型，并通过实测数据验证了该动力学模型的准确性。在此基础上建立以斗尖初速度和加速度为优化变量，以最短挖掘时间和最低挖掘能耗为目标的优化模型。引入权重系数表征不同的挖掘作业模式后，利用遗传算法对优化模型进行优化求解。并分析不同权重系数对规划结果的影响，结果表明：针对不同权重系数，优化模型都可以取得最优解，验证了该优化方法的有效性。

关键词: 挖掘机, 运动规划, 遗传算法, 自主作业

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

中图分类号:

TU621

陈晋市, 张淼淼, 毕秋实, 李永奇, 霍东阳, 齐洪阳. 面向自主作业的挖掘机多目标最优挖掘运动规划[J]. 机械工程学报, 2022, 58(7): 237-245.

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.

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面向自主作业的挖掘机多目标最优挖掘运动规划

Multi-objective Optimal Excavator Movement Planning for Autonomous Operation

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