基于极大似然估计的工业机器人腕部6维力传感器在线标定
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摘要
针对工业机器人实时监测腕部工具工作受力情况的需求,提出了基于极大似然估计的在线标定算法.首先,在机器人末端工具的活动负载部分安装6维力传感器,并实时采集力与力矩数据以及机器人工具运动轨迹.然后,考虑到不同运行速度下运动振动的干扰,根据系统力学关系求解工具重心坐标、机器人安装倾角、力传感器零点以及负载重力.最后,在10 mm/s～1000 mm/s不同速度条件下进行实验,分析求解结果的一致性,并与最小二乘法进行对比.对比结果显示基于极大似然估计可以把工具重心平均标准差从0.67 mm降到0.23 mm,力零点标准差从0.73 N降到0.27 N,力矩零点标准差从0.29 N·m降到0.05 N·m.实验结果表明极大似然估计能有效抵抗机器人高速运动引起的干扰,可以应用于机器人高速运动情况下的实时在线零点标定.
An online calibration algorithm based on the maximum likelihood estimation is proposed to monitor the forces on wrist tools in real-time for industrial robots. Firstly, the 6-axis force sensor is installed in the robot end tools to collect the force, the torque and the motion path of the robot tool in real-time. Then, the gravity coordinate of the tool, the installation angle of the robot, the zero offset of the force sensor and the gravity of the load are calculated according to the force relation of the system while considering the motion vibration interferences at different speeds. Finally, the experiments at different speeds between 10 mm/s～1000 mm/s are conducted, and the consistence of the solution results are analyzed and compared with the results of the least square method. The comparison results show that the maximum likelihood estimation method reduces the average standard deviation of the gravity center from 0.67 mm to 0.23 mm, the standard deviation of the force zeros from 0.73 N to 0.27 N, and the standard deviation of the torque zeros from 0.29 N·m to 0.05 N·m. The experimental results show that the maximum likelihood estimation method can effectively resist the interference caused by high-speed motion of the robot, and can be applied to real-time and online zero calibration of the robot in the case of high-speed motion.
An online calibration algorithm based on the maximum likelihood estimation is proposed to monitor the forces on wrist tools in real-time for industrial robots. Firstly, the 6-axis force sensor is installed in the robot end tools to collect the force, the torque and the motion path of the robot tool in real-time. Then, the gravity coordinate of the tool, the installation angle of the robot, the zero offset of the force sensor and the gravity of the load are calculated according to the force relation of the system while considering the motion vibration interferences at different speeds. Finally, the experiments at different speeds between 10 mm/s～1000 mm/s are conducted, and the consistence of the solution results are analyzed and compared with the results of the least square method. The comparison results show that the maximum likelihood estimation method reduces the average standard deviation of the gravity center from 0.67 mm to 0.23 mm, the standard deviation of the force zeros from 0.73 N to 0.27 N, and the standard deviation of the torque zeros from 0.29 N·m to 0.05 N·m. The experimental results show that the maximum likelihood estimation method can effectively resist the interference caused by high-speed motion of the robot, and can be applied to real-time and online zero calibration of the robot in the case of high-speed motion.
引文
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[4]Fujimoto Y, Murakami T, Oboe R. Advanced motion control for next-generation industrial applications[J]. IEEE Transactions on Industrial Electronics, 2016, 63(3):1886-1887.
[5]柳倩,桂建军,杨小薇,等.工业机器人传感控制技术研究现状及发展态势——基于专利文献计量分析视角[J].机器人,2016,38(5):612-620.Liu Q, Gui J J, Yang X W, et al. Research status and development trends for sensing and control technologies of industrial robot from the viewpoint of patent analysis[J]. Robot, 2016,38(5):612-620.
[6]侯澈,王争,赵忆文,等.面向直接示教的机器人负载自适应零力控制[J].机器人,2017,39(4):439-448.Hou C, Wang Z, Zhao Y W, et al. Load adaptive force-free control for the direct teaching of robots[J]. Robot, 2017, 39(4):439-448.
[7]李二超,李战明.基于力/力矩信息的面向位控机器人的阻抗控制[J].控制与决策,2016,31(5):957-960.Li E C, Li Z M. Impedance control for positional-controlled robotic manipulators based on force/torque information[J].Control and Decision, 2016, 31(5):957-960.
[8]黄婷,孙立宁,王振华,等.基于被动柔顺的机器人抛磨力/位混合控制方法[J].机器人,2017,39(6):776-785.Huang T, Sun L N, Wang Z H, et al. Hybrid force/position control method for robotic polishing based on passive compliance structure[J]. Robot, 2017, 39(6):776-785.
[9]张立建,胡瑞钦,易旺民.基于六维力传感器的工业机器人末端负载受力感知研究[J].自动化学报,2017,43(3):439-447.Zhang L J, Hu R Q, Yi W M. Research on force sensing for the end-load of industrial robot based on a 6-axis force/torque sensor[J]. Acta Automatica Sinica, 2017, 43(3):439-447.
[10]张光辉,王耀南.末端F/T传感器的重力环境下大范围柔顺控制方法[J].智能系统学报,2015,10(5):675-683.Zhang G H, Wang Y N. A wide range compliance control method in gravity environment based on end force/torque sensor[J]. CAAI Transactions on Intelligent Systems, 2015, 10(5):675-683.
[11]高强,田凤杰,杨林,等.机器人自动研抛系统平台搭建及重力补偿研究[J].工具技术,2015,49(8):47-50.Gao Q, Tian F J, Yang L, et al. Research on platform of robot automatic polishing system and gravity compensation[J]. Tool Engineering, 2015, 49(8):47-50.
[12]刘文波.基于力控制方法的工业机器人磨削研究[D].广州:华南理工大学,2014.Liu W B. Research on industrial robot grinding based on force control[D].Guangzhou:South China University of Technology, 2014.
[13]熊有伦,唐立新,丁汉,等.机器人技术基础[M].武汉:华中科技大学出版社,1996:61-62.Xiong Y L, Tang L X, Ding H, et al. Fundamentals of robotic technology[M]. Wuhan:Huazhong University of Science and Technology Press, 1996:61-62.