采用多目标粒子群-遗传算法的井筒钻孔机械臂臂长设计  

Arm Length Design of Wellbore Drilling Robotic Arm Using MOPSO-GA Optimization Algorithm

作　　者：胡启国[1] 苏文 HU Qiguo;SU Wen(School of Mechantronics and Vehicle Engineering,Chongqing Jiaotong University,Chongqing 400074,China)

机构地区：[1]重庆交通大学机电与车辆工程学院,重庆400074

出　　处：《华侨大学学报（自然科学版）》2023年第2期150-156,共7页Journal of Huaqiao University(Natural Science)

基　　金：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)。

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.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.

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 

分 类 号：TD421[矿业工程—矿山机电] TP241.202[自动化与计算机技术—检测技术与自动化装置]