基于改进SPBO算法的关节机器人动力学参数辨识  

Joint Robot Dynamics Model Identification Based on Better SPBO Algorithm

作　　者：兰建斌 陈立平[1] LAN Jianbin;CHEN Liping(National CAD Support Software Engineering Research Center,Huazhong University of Science and Technology,Wuhan Hubei 430074,China)

机构地区：[1]华中科技大学国家CAD支撑软件工程技术研究中心,湖北武汉430074

出　　处：《机床与液压》2023年第5期1-7,共7页Machine Tool & Hydraulics

基　　金：国家重点研发计划项目(2019YFB1706501)。

摘　　要：提出一种基于改进学生心理学优化(BSPBO)算法的机器人动力学参数辨识方法。通过Newton-Euler法构建关节型机器人动力学模型,设计符合运动约束的五阶傅里叶级数作为激励轨迹;引入SPBO算法并对其进行如下改进:增加Good Student分类在学生种群中所占比例、改善迭代过程中求解变量越界的处理方式,以提高算法的开发能力和全局探索能力,克服SPBO易陷入局部最优的缺陷;以具备关节力矩测量功能的机器人平台为对象,开展动力学参数辨识实验。结果表明:BSPBO算法的收敛精度更高、收敛速度更快,能稳定高效地完成各关节动力学参数的辨识。A dynamic identification method for joint robot based on better student psychology based optimization(BSPBO)algorithm was proposed.The dynamics model of an articulated robot was established using Newton-Euler method,and a fifth-order Fourier series conforming to the motion constraints was designed as the excitation trajectory;the SPBO algorithm was introduced and improved as follows:increasing the proportion of Good Student classification in the student population,improving the treatment of solving variables out of bounds in the iterative process,in order to improve the exploitation ability and global exploration ability of the algorithm,and overcoming the defect that SPBO tended to fall into local optimum.Finally,the robot platform with joint torque measurement function was used to conduct experiments on the identification of dynamics parameters.The results show that the BSPBO algorithm has higher convergence accuracy and faster convergence speed,and can finish the identification of the kinetic parameters of each joint stably and efficiently.

关 键 词：学生心理学优化算法 动力学参数 参数辨识 关节型机器人 

分 类 号：TP242.2[自动化与计算机技术—检测技术与自动化装置]