scholarly journals Dynamic Model Identification for 6-DOF Industrial Robots

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Li Ding ◽  
Hongtao Wu ◽  
Yu Yao ◽  
Yuxuan Yang
Keyword(s):  
Robot Manipulator ◽  
Industrial Robot ◽  
Model Identification ◽  
Friction Model ◽  
Industrial Robots ◽  
Parameters Identification ◽  
Identification Algorithm ◽  
Unknown Parameters ◽  
Joint Friction ◽  
Dynamical Parameters

A complete and systematic procedure for the dynamical parameters identification of industrial robot manipulator is presented. The system model of robot including joint friction model is linear with respect to the dynamical parameters. Identification experiments are carried out for a 6-degree-of-freedom (DOF) ER-16 robot. Relevant data is sampled while the robot is tracking optimal trajectories that excite the system. The artificial bee colony algorithm is introduced to estimate the unknown parameters. And we validate the dynamical model according to torque prediction accuracy. All the results are presented to demonstrate the efficiency of our proposed identification algorithm and the accuracy of the identified robot model.

scholarly journals A new joint friction model for parameter identification and sensor-less hand guiding in industrial robots

2020 ◽  
Vol 47 (6) ◽  
pp. 847-857
Author(s):  
Guanghui Liu ◽  
Qiang Li ◽  
Lijin Fang ◽  
Bing Han ◽  
Hualiang Zhang
Keyword(s):  
Industrial Robot ◽  
Angular Displacement ◽  
Weighted Least Squares ◽  
Friction Model ◽  
Industrial Robots ◽  
Model Parameters ◽  
Dynamic Parameters ◽  
Content Type ◽  
Joint Friction ◽  
Six Dof

Purpose The purpose of this paper is to propose a new joint friction model, which can accurately model the real friction, especially in cases with sudden changes in the motion direction. The identification and sensor-less control algorithm are investigated to verify the validity of this model. Design/methodology/approach The proposed friction model is nonlinear and it considers the angular displacement and angular velocity of the joint as a secondary compensation for identification. In the present study, the authors design a pipeline – including a manually designed excitation trajectory, a weighted least squares algorithm for identifying the dynamic parameters and a hand guiding controller for the arm’s direct teaching. Findings Compared with the conventional joint friction model, the proposed method can effectively predict friction factors during the dynamic motion of the arm. Then friction parameters are quantitatively obtained and compared with the proposed friction model and the conventional friction model indirectly. It is found that the average root mean square error of predicted six joints in the proposed method decreases by more than 54%. The arm’s force control with the full torque using the estimated dynamic parameters is qualitatively studied. It is concluded that a light-weight industrial robot can be dragged smoothly by the hand guiding. Practical implications In the present study, a systematic pipeline is proposed for identifying and controlling an industrial arm. The whole procedure has been verified in a commercial six DOF industrial arm. Based on the conducted experiment, it is found that the proposed approach is more accurate in comparison with conventional methods. A hand-guiding demo also illustrates that the proposed approach can provide the industrial arm with the full torque compensation. This essential functionality is widely required in many industrial arms such as kinaesthetic teaching. Originality/value First, a new friction model is proposed. Based on this model, identifying the dynamic parameter is carried out to obtain a set of model parameters of an industrial arm. Finally, a smooth hand guiding control is demonstrated based on the proposed dynamic model.

Modelling and Identification of a Six Axes Industrial Robot

2005 ◽  
Author(s):  
Rob Waiboer ◽  
Ronald Aarts ◽  
Ben Jonker
Keyword(s):  
Least Squares ◽  
Industrial Robot ◽  
Equations Of Motion ◽  
Experimental Parameter ◽  
Friction Model ◽  
Unknown Parameters ◽  
Joint Friction ◽  
Inertia Parameters ◽  
Value Decomposition ◽  
Least Squares Techniques

This paper deals with the modelling and identification of a six axes industrial Sta¨ubli RX90 robot. A non-linear finite element method is used to generate the dynamic equations of motion in a form suitable for both simulation and identification. The latter requires that the equations of motion are linear in the inertia parameters. Joint friction is described by a friction model that describes the friction behaviour in the full velocity range necessary for identification. Experimental parameter identification by means of linear least squares techniques showed to be very suited for identification of the unknown parameters, provided that the problem is properly scaled and that the influence of disturbances is sufficiently analysed and managed. An analysis of the least squares problem by means of a singular value decomposition is preferred as it not only solves the problem of rank deficiency, but it also can correctly deal with measurement noise and unmodelled dynamics.

Development of the Industrial Robot Electric Drive Model

2021 ◽  
Vol 64 (6) ◽  
pp. 52-57
Author(s):  
Konstantin Litsin ◽  
◽  
Sergei Baskov ◽  
Yaroslav Makarov ◽  
◽  
...  
Keyword(s):  
Electric Drive ◽  
Robot Manipulator ◽  
Block Diagram ◽  
Industrial Robot ◽  
Permanent Magnet Synchronous Motor ◽  
Industrial Robots ◽  
Full Turn ◽  
Position Controller ◽  
Power Part ◽  
The Cost

Currently, the penetration of industrial robots into all sectors of the economy is increasing. However, there is an acute problem of conducting preliminary tests. The use of the digital twin as a replacement for the industrial robot is driven by high economic costs. In order to reduce the cost of the project, a solution is proposed to conduct preliminary tests on the developed model. The article developed a mathematical model of one of the drives of the industrial robot manipulator Yaskawa Motoman MH50-35. The model is suitable for researching the movement of the robot' tool. A mathematical description of a permanent magnet synchronous motor SGMJV-09A in a rotating coordinate system is given and a block diagram of the power part of the drive is made. A system for regulating the position of the robot's tool with a nonlinear position controller has been synthesized. Based on the results of modeling the operation of an electric drive in the Matlab Simulink environment, the degree of correspondence of the developed model to a real object was assessed and conclusions were drawn about the limits of its applicability for studying the operation of an electric drive of a robotic arm. The accuracy of working out the task for turning the wrist is 0.0001 rad, there is no overshoot in position, and the time for completing a full turn of the tool is 1.07 s.

Industrial Robot Kinematics Parameter Identification

2014 ◽  
Vol 889-890 ◽  
pp. 1136-1143
Author(s):  
Yong Gui Zhang ◽  
Chen Rong Liu ◽  
Peng Liu
Keyword(s):  
Experimental Data ◽  
Genetic Algorithms ◽  
Parameter Identification ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Robot Kinematics ◽  
Kinematic Parameters ◽  
Unknown Parameters ◽  
Preliminary Measurement ◽  
Kinematics Modeling

For an industrial robots with unknown parameters, on the basis of preliminary measurement and data of the Cartesian and joints coordinates which are shown on the FlexPendant, the kinematic parameters is identified by using genetic algorithms and accurate kinematics modeling of the robot is established. Experimental data could prove the validity of this method.

A Semiparametric Model-Based Friction Compensation Method for Multi-Joint Industrial Robot

2021 ◽  
Author(s):  
Miao He ◽  
Xiaomin Wu ◽  
Guifang Shao ◽  
Yuhua Wen ◽  
Tundong Liu
Keyword(s):  
Industrial Robot ◽  
Lyapunov Theory ◽  
Industrial Robots ◽  
Learning Ability ◽  
Robot System ◽  
Joint Friction ◽  
Tracking Tasks ◽  
Physical Information ◽  
The Stability ◽  
Six Degree Of Freedom

Abstract Industrial robots have received enormous attention due to their widespread uses in modern manufacturing. However, due to the frictional discontinuous and other unknown dynamics in robotic system, existing researches are limited to simulation and single- or double-joint robot. In this paper, we introduce a semiparametric controller combined by a radial basis function neural network (RBFNN) and complete physical model considering joint friction. First, to extend the NN controller to real-world problems, the continuously differentiable friction (CDF) model is adopted to bring physical information into the learning process. Then, RBFNN is employed to approximate the model error and other unmolded dynamics, and the parameters of CDF model are updated online according to its learning ability. The stability of the robot system can be guaranteed by the Lyapunov theory. The primary parameters of CDF model are determined by the identification experiment and subsequently iteratively updated by the NN. Real-time tracking tasks are performed on a six degree of freedom (DoF) manipulator to follow the desired trajectory. Experimental results demonstrate the effectiveness and superiority of the proposed controller, especially at low speed.

scholarly journals Joint Stiffness Identification and Deformation Compensation of Serial Robots Based on Dual Quaternion Algebra

2018 ◽  
Vol 9 (1) ◽  
pp. 65 ◽  
Author(s):  
Guozhi Li ◽  
Fuhai Zhang ◽  
Yili Fu ◽  
Shuguo Wang
Keyword(s):  
Quaternion Algebra ◽  
Industrial Robot ◽  
Joint Stiffness ◽  
Industrial Robots ◽  
Identification Algorithm ◽  
Dual Quaternion ◽  
Compensation Algorithm ◽  
Serial Robots ◽  
Stiffness Identification ◽  
Deformation Compensation

As the application of industrial robots is limited by low stiffness that causes low precision, a joint stiffness identification algorithm for serial robots is presented. In addition, a deformation compensation algorithm is proposed for the accuracy improvement. Both of these algorithms are formulated by dual quaternion algebra, which offers a compact, efficient, and singularity-free way in robot analysis. The joint stiffness identification algorithm is derived from stiffness modeling, which is the combination of the principle of virtual work and dual quaternion algebra. To validate the effectiveness of the proposed identification algorithm and deformation compensation algorithm, an experiment was conducted on a dual arm industrial robot SDA5F. The robot performed a drilling operation during the experiment, and the forces and torques that acted on the end-effector (EE) of both arms were measured in order to apply the deformation compensation algorithm. The results of the experiment show that the proposed identification algorithm is able to identify the joint stiffness parameters of serial industrial robots, and the deformation compensation algorithm can improve the accuracy of the position and orientation of the EE. Furthermore, the performance of the forces and torques that acted on the EE during the operation were improved as well.

Practical application of a safe human-robot interaction software

2020 ◽  
Vol 47 (3) ◽  
pp. 359-368 ◽  
Author(s):  
Mustafa Can Bingol ◽  
Omur Aydogmus
Keyword(s):  
Robot Manipulator ◽  
Industrial Robot ◽  
Wavelet Packet ◽  
Human Robot Interaction ◽  
Industrial Robots ◽  
Robot Interaction ◽  
Content Type ◽  
Depth Sensors ◽  
Working Space ◽  
Vision Module

Purpose Because of the increased use of robots in the industry, it has become inevitable for humans and robots to be able to work together. Therefore, human security has become the primary noncompromising factor of joint human and robot operations. For this reason, the purpose of this study was to develop a safe human-robot interaction software based on vision and touch. Design/methodology/approach The software consists of three modules. Firstly, the vision module has two tasks: to determine whether there is a human presence and to measure the distance between the robot and the human within the robot’s working space using convolutional neural networks (CNNs) and depth sensors. Secondly, the touch detection module perceives whether or not a human physically touches the robot within the same work environment using robot axis torques, wavelet packet decomposition algorithm and CNN. Lastly, the robot’s operating speed is adjusted according to hazard levels came from vision and touch module using the robot’s control module. Findings The developed software was tested with an industrial robot manipulator and successful results were obtained with minimal error. Practical implications The success of the developed algorithm was demonstrated in the current study and the algorithm can be used in other industrial robots for safety. Originality/value In this study, a new and practical safety algorithm is proposed and the health of people working with industrial robots is guaranteed.

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