基于人工蜂群算法的机器人运动学参数标定  

Calibration of Robot Kinematic Parameters Based on Artificial Bee Colony Algorithm

作　　者：蔡鑫宇 赵铁军[1] CAI Xinyu;ZHAO Tiejun(School of Mechanical Engineering,Shenyang University of Technology,Shenyang 110027,China)

机构地区：[1]沈阳工业大学机械工程学院,沈阳110027

出　　处：《机械工程师》2024年第3期44-46,51,共4页Mechanical Engineer

摘　　要：为了提高机器人的绝对定位精度,针对经典的D-H模型,使用修正的MD-H模型来描述IRB 120型机器人的运动模型,避免了运动过程中出现的奇异现象。对末端位置测量采用的方法是单目视觉测量,由于传统的位置误差模型需要计算出机器人基坐标系和测量坐标系之间的转换矩阵,故采用距离误差模型,可消除坐标系转换所带来的误差。此外,针对最小二乘法容易陷入局部寻优、结果不稳定的问题,提出了人工蜂群算法(ABC)求解高维非线性方程组,对两种方法进行对比。结果表明,人工蜂群算法优于最小二乘法,且使机器人的距离误差精度提高了54.97%。To improve the absolute positioning accuracy of robots,the modified MD-H model is used to describe the motion model of IRB 120 robot for the classic D-H model,which avoids the strange phenomenon that occurs during the movement.The method used for the end position measurement is monocular visual measurement,because the traditional position error model needs to calculate the conversion matrix between the base coordinate system and the measurement coordinate system of the robot,so the distance error model can eliminate the error caused by the coordinate system conversion.In addition,aiming at the problem that the least squares method is easy to fall into local optimization and unstable results,the artificial bee colony algorithm(ABC)is proposed to solve the high-dimensional nonlinear equation system,and the two methods are compared.The results show that the artificial bee colony algorithm is better than the least squares method,and the distance error accuracy of the robot is improved by 54.97%.

关 键 词：MD-H模型 距离误差模型 运动学标定 人工蜂群 

分 类 号：TP23[自动化与计算机技术—检测技术与自动化装置]