[1]胡启国,苏文.采用多目标粒子群-遗传算法的井筒钻孔机械臂臂长设计[J].华侨大学学报(自然科学版),2023,44(2):150-156.[doi:10.11830/ISSN.1000-5013.202209021]
　HU Qiguo,SU Wen.Arm Length Design of Wellbore Drilling Robotic Arm Using MOPSO-GA Optimization Algorithm[J].Journal of Huaqiao University(Natural Science),2023,44(2):150-156.[doi:10.11830/ISSN.1000-5013.202209021]
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采用多目标粒子群-遗传算法的井筒钻孔机械臂臂长设计()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第44卷
期数:
2023年第2期
页码:
150-156
栏目:
出版日期:
2023-03-14

文章信息/Info

Title:
Arm Length Design of Wellbore Drilling Robotic Arm Using MOPSO-GA Optimization Algorithm
文章编号:
1000-5013(2023)02-0150-07
作者:
胡启国;  苏文
重庆交通大学 机电与车辆工程学院, 重庆 400074
Author(s):
HU Qiguo;  SU Wen
School of Mechantronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China
关键词:
机械臂;  井筒工程;  参数优化;  多目标粒子群-遗传算法(MOPSO-GA);  可达度
Keywords:
robotic arm;  wellbore engineering;  parameter optimization;  MOPSO-GA;  accessibility
分类号:
TD421;TP241.202
DOI:
10.11830/ISSN.1000-5013.202209021
文献标志码:
A
摘要:
为了解决井筒工程人工钻爆法施工突出问题,采用4自由度机械臂替代人工完成井底炮孔钻掘.首先,在无初始臂长参数下,通过算法获得一组结构参数小,在有限封闭作业空间内末端执行器可达位置范围大的臂长参数.然后,借助MDH(modified Denavit-Hartenberg)坐标运动学参数化正向建模,以末端位置包络线为约束逆向筛选,以臂长参数、可达度为目标,采用多目标粒子群-遗传算法(MOPSO-GA)进行参数寻优,得到若干组Pareto最优解集,并根据适应度选择最优参数结果.最后,对最优参数蒙特卡洛法和运动学进行仿真验证.结果表明:末端点云布于井底,包覆井筒钻孔工作区域,各臂运动学参数相对平稳,能够完成目标任务.
Abstract:
In order to solve the outstanding problems in the manual drilling and blasting method of wellbore engineering, a four degree of freedom robotic arm is used to replace manual work to complete the drilling of well bottom blasthole. Firstly, without the initial arm length parameters, a set of arm length parameters with small structural parameters and a large range of reachable end-effector positions in a finite enclosed operating space are obtained by the algorithm. Then, with the help of MDH(modified Denavit-Hartenberg)coordinate kinematic parametric forward modeling, reverse screening is performed with the end position envelope as the constraint, with the arm length parameters and accessibility as the goal, parameters optimization are achieved using multi-objective particle swarm optimization-genetic algorithm(MOPSO-GA), several sets of Pareto optimal solution sets are obtained, and the optimal parameter results are selected according to the fitness. Finally, the Monte Carlo method with optimal parameters and kinematics are simulated and verified. The results show that the end point cloud is distributed at the well bottom, covering the working area of the wellbore drilling, and the kinematic parameters of each arm are relatively stable, which can complete the task.

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备注/Memo

备注/Memo:
收稿日期: 2022-09-29
通信作者: 胡启国(1966-),男,教授,博士,主要从事机械系统动力学和可靠性设计与优化的研究.E-mail:swpihqg@163.com.
基金项目: 国家自然科学基金资助项目(51375519); 重庆市教委科学技术研究项目(KJZD-K202000703)
更新日期/Last Update: 2023-03-20