Trajectory Optimization of Autonomous Mining for Powerful Rocker Arm Hydraulic Shovels

doi:10.3901/JME.2024.18.374

Abstract

Abstract: Taking the shortest mining time，the maximum bucket fill factor，and the lowest mining energy consumption as the optimization objectives，a trajectory optimization method of autonomous mining is proposed for powerful rocker arm hydraulic shovels. At first，the advantages of the powerful rocker arm hydraulic shovel in human manipulation and autonomous mining are analyzed. Based on this，the advantages and disadvantages of different mining methods for hydraulic shovels are compared by the co-simulation of dynamic simulation software RecurDyn and discrete element simulation software EDEM. Then，the composite mining is determined as the autonomous mining method of hydraulic shovels. Moreover，mathematical models and the digging resistance model of the powerful rocker arm hydraulic shovels are established. Furthermore，taking the parabolic acceleration polynomial curve as the trajectory planning curve of the three groups for hydraulic cylinders，the multi-objective optimization function and its constraint conditions are determined. And，the genetic algorithm(GA) is used to solve the optimization problem. Final，the effectiveness and feasibility of the optimized results are verified by the semi-physical real-time simulation platform of the powerful rocker arm hydraulic shovels with the controller in the loop. The results show that compared with the mechanical mining excavator with the same bucket capacity，the average energy consumption of the actuators for the powerful rocker arm hydraulic shovels can be reduced by 16.41% under the premise that the full bucket rate and working time are maintained in reasonable ranges by using the same trajectory optimization method. Therefore，the mining hydraulic excavator has superior performance.

Key words: powerful rocker arm, mining hydraulic excavators, autonomous mining, trajectory optimization, genetic algorithm

CLC Number:

TG621

QIN Tao, LI Yunhua, FAN Rujun. Trajectory Optimization of Autonomous Mining for Powerful Rocker Arm Hydraulic Shovels[J]. Journal of Mechanical Engineering, 2024, 60(18): 374-384.

References

[1] 于海旭，何适，辛昊天，等. 露天煤矿电铲智能化技术应用及其发展趋势[J]. 工矿自动化，2021，47(S1)：103-105. YU Haixu，HE Shi，XIN Haotian，et al. Application and development trend of intelligent technology of electric shovel in open-pit coal mine[J]. Industry and Mine Automation，2021，47(S1)：103-105.
[2] ZHANG Tianci，FU Tao，CUI Yunhao，et al. Toward autonomous mining：Design and development of an unmanned electric shovel via point cloud-based optimal trajectory planning[J]. Frontiers of Mechanical Engineering，2022，17(3)：30.
[3] TIWARI R，KNOWLES J，DANKO G. Bucket trajectory classification of mining excavators[J]. Automation in Construction，2013，31：128-139.
[4] 陈晋市，张淼淼，毕秋实，等. 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，et al. Multi-objective optimal excavator movement planning for autonomous operation[J]. Journal of Mechanical Engineering，2022，58(7)：237-245.
[5] XU Gongyue，DING Huafeng，FENG Zemin. Optimal design of hydraulic excavator shovel attachment based on multiobjective evolutionary algorithm[J]. IEEE/ASME Transactions on Mechatronics，2019，24(2)：808-819.
[6] 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.
[7] 孙志毅，张韵悦，李虹，等. 挖掘机的最优时间轨迹规划[J]. 机械工程学报，2019，55(5)：166-174. SUN Zhiyi，ZHANG Yunyue，LI Hong，et al. Time optimal trajectory planning of excavator[J]. Journal of Mechanical Engineering，2019，55(5)：166-174.
[8] 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(自然科学版)，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 (Natural Science)，2016，37(5)：610-614.
[9] WANG Xiaobang，SUN Wei，LI Eryang，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] XU Gongyue，FENG Zemin，GUO Erkuo，et al. Multi-objective optimization of hydraulic shovel using evolutionary algorithm[J]. Automation in Construction，2022，142：104486.
[11] FAN Rujun，LI Yunhua，YANG Liman. Multiobjective trajectory optimization of intelligent electro-hydraulic shovel[J]. Frontiers of Mechanical Engineering，2022，17(4)：50.
[12] KIM Y B，HA J，KANG H，et al. Dynamic optimal trajectory for earthmoving excavator[J]. Automation in Construction，2013，35：568-578.
[13] YANG Yajue，LONG Pinxin，SONG Xinbin，et al. Optimization-based framework for excavation trajectory generation[J]. IEEE Robotics and Automation Letters，2021，6(2)：1479-1486.
[14] XU Gongyue，DING Huafeng，FENG Zemin. Optimal design of hydraulic excavator shovel attachment based on multiobjective evolutionary algorithm[J]. IEEE/ASME Transactions on Mechatronics，2019，24(2)：808-819.
[15] 丁华锋，曹宇，杨真真，等. 基于D-H法的多连杆正铲挖掘机运动学分析与包络图绘制[J]. 燕山大学学报，2014，38(3)：197-203． DING Huafeng，CAO Yu，YANG Zhenzhen，et al. Position kinematics analysis of multi-linkage face-shovel excavator and envelope plotting using D-H method[J]. Journal of Yanshan University，2014，38(3)：197-203.
[16] 李运华，牛天昊. 特大型矿用挖掘机挖掘阻力感知与仿真研究[J]. 中国工程机械学报，2021，19(5)：403-408. LI Yunhua，NIU Tianhao. Study on perception of excavating resistance and simulation for super-huge mining excavator[J]. Chinese Journal of Construction Machinery，2021，19(5)：403-408.
[17] LI Yunhua，MU Xiangyang，FAN Rujun. Multi-objective optimization and simulation of novel working mechanism for face-shovel excavator[J]. International Journal of Intelligent Robotics and Applications，2021，5(1)：1-9.
[18] 董宝祥，秦涛，杨旭，等. 矿用正铲液压挖掘机人机协同控制[J/OL]. 北京航空航天大学学报：1-11[2023-02-08]. DONG Baoxiang，QIN Tao，YANG Xu，et al. Human-machine collaborative control of mining forward shovel hydraulic excavators[J/OL]. Journal of Beijing University of Aeronautics and Astronautics：1-11[2023-02-08].
[19] ALEKSEEVA T V. Machines for earthmoving work：Theory and calculations[M]. New Delhi：Taylor & Francis，1985.
[20] 穆晓鹏. 矿用电铲运行轨迹控制及液电混驱节能方法研究[D]. 山西：太原理工大学，2019. MU Xiaopeng. Research on trajectory control and hydraulic-electric hybrid energy saving method of mining electric shovel[D]. Shanxi：Taiyuan University of Technology，2019.