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.

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.

Research on the forcefree control of cooperative robots based on dynamic parameters identification

2019 ◽  
Vol 46 (4) ◽  
pp. 499-509 ◽  
Author(s):  
Juliang Xiao ◽  
Fan Zeng ◽  
Qiulong Zhang ◽  
Haitao Liu
Keyword(s):  
Dynamic Model ◽  
Control Algorithm ◽  
Angular Displacement ◽  
Friction Model ◽  
Friction Torque ◽  
Parameters Identification ◽  
Dynamic Parameters ◽  
Cooperative Robots ◽  
Content Type ◽  
Direct Teaching

Purpose This paper aims to propose a forcefree control algorithm that is based on a dynamic model with full torque compensation is proposed to improve the compliance and flexibility of the direct teaching of cooperative robots. Design/methodology/approach Dynamic parameters identification is performed first to obtain an accurate dynamic model. The identification process is divided into two steps to reduce the complexity of trajectory simplification, and each step contains two excitation trajectories for higher identification precision. A nonlinear friction model that considers the angular displacement and angular velocity of joints is proposed as a secondary compensation for identification. A torque compensation algorithm that is based on the Hogan impedance model is proposed, and the torque obtained by an impedance equation is regarded as the command torque, which can be adjusted. The compensatory torque, including gravity torque, inertia torque, friction torque and Coriolis torque, is added to the compensation to improve the effect of forcefree control. Findings The model improves the total accuracy of the dynamic model by approximately 20% after compensation. Compared with the traditional method, the results prove that the forcefree control algorithm can effectively reduce the drag force approximately 50% for direct teaching and realize a flexible and smooth drag. Practical implications The entire algorithm is verified by the laboratory-developed six degrees-of-freedom cooperative robot, and it can be applied to other robots as well. Originality/value A full torque compensation is performed after parameters identification, and a more accurate forcefree control is guaranteed. This allows the cooperative robot to be dragged more smoothly without external sensors.

Optimization design of drive system for industrial robots based on dynamic performance

2017 ◽  
Vol 44 (6) ◽  
pp. 765-775 ◽  
Author(s):  
LianZheng Ge ◽  
Jian Chen ◽  
Ruifeng Li ◽  
Peidong Liang
Keyword(s):  
Optimization Model ◽  
Optimization Problem ◽  
Industrial Robot ◽  
Dynamic Performance ◽  
System Optimization ◽  
Drive System ◽  
Industrial Robots ◽  
Mixed Integer ◽  
Light Weight ◽  
Content Type

Purpose The global performance of industrial robots partly depends on the properties of drive system consisting of motor inertia, gearbox inertia, etc. This paper aims to deal with the problem of optimization of global dynamic performance for robotic drive system selected from available components. Design/methodology/approach Considering the performance specifications of drive system, an optimization model whose objective function is composed of working efficiency and natural frequency of robots is proposed. Meanwhile, constraints including the rated and peak torque of motor, lifetime of gearbox and light-weight were taken into account. Furthermore, the mapping relationship between discrete optimal design variables and component properties of drive system were presented. The optimization problem with mixed integer variables was solved by a mixed integer-laplace crossover power mutation algorithm. Findings The optimization results show that our optimization model and methods are applicable, and the performances are also greatly promoted without sacrificing any constraints of drive system. Besides, the model fits the overall performance well with respect to light-weight ratio, safety, cost reduction and others. Practical implications The proposed drive system optimization method has been used for a 4-DOF palletizing robot, which has been largely manufactured in a factory. Originality/value This paper focuses on how the simulation-based optimization can be used for the purpose of generating trade-offs between cost, performance and lifetime when designing robotic drive system. An applicable optimization model and method are proposed to handle the dynamic performance optimization problem of a drive system for industrial robot.

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.

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.

Uncertainty analysis and evaluation of measurement of the positioning repeatability for industrial robots

2018 ◽  
Vol 45 (4) ◽  
pp. 492-504 ◽  
Author(s):  
Yang Chuangui ◽  
Liu Xingbao ◽  
Yue Xiaobin ◽  
Mi Liang ◽  
Wang Junwen ◽  
...  
Keyword(s):  
Probability Distribution Function ◽  
Design Methodology ◽  
Industrial Robot ◽  
Measurement Model ◽  
Measurement Procedure ◽  
Industrial Robots ◽  
Key Factors ◽  
Content Type ◽  
Normal Distributions ◽  
Analysis And Evaluation

PurposeThis paper aims to solve the nonlinear problem in the uncertainty evaluation of the measurement of the positioning repeatability (RP) of industrial robots and provide guidance to restrict the uncertainty of measurement of RP (uRP).Design/methodology/approachFirstly, some uncertain sources existing in the measurement procedure of RP are identified. Secondly, the probability distribution function (PDF) of every source is established on the basis of its measurements. Some spatial combined normal distributions are adopted. Then, a method, based on Monte Carlo method (MCM) and established measurement model, is developed for the estimation ofuRP. Thirdly, some tests are developed for the identification and validation of the selected PDFs of uncertain sources. Afterwards, the proposed method is applied for the evaluation and validation of theuRP. Finally, influence analyses of some key factors are proposed for the quantification of their relative contributions touRP.FindingsResults show that the proposed method can reasonably and objectively estimate theuRPof the selected industrial robot, and changes of the industrial robots’ position and the laser trackers measurement are correlated. Additionally, theuRPof the selected industrial robot can be restricted by using the results of its key factors onuRP.Originality/valueThis paper proposes the spatial combined normal distribution to model the uncertainty of the repeatability of the laser tracker and industrial robot. Meanwhile, the proposed method and influence analyses can be used in estimating and restricting theuRPand thus useful in determining whether the RP of a tested industrial robot meets its requirements.

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.

The integration of contactless static pose recognition and dynamic hand motion tracking control system for industrial human and robot collaboration

2015 ◽  
Vol 42 (5) ◽  
pp. 416-428 ◽  
Author(s):  
Gilbert Tang ◽  
Seemal Asif ◽  
Phil Webb
Keyword(s):  
Control System ◽  
Motion Tracking ◽  
Industrial Robot ◽  
Integrated System ◽  
Industrial Robots ◽  
Content Type ◽  
Hand Motion ◽  
Pose Recognition ◽  
Gesture Control ◽  
Potential Applications

Purpose – The purpose of this paper is to describe the integration of a gesture control system for industrial collaborative robot. Human and robot collaborative systems can be a viable manufacturing solution, but efficient control and communication are required for operations to be carried out effectively and safely. Design/methodology/approach – The integrated system consists of facial recognition, static pose recognition and dynamic hand motion tracking. Each sub-system has been tested in isolation before integration and demonstration of a sample task. Findings – It is demonstrated that the combination of multiple gesture control methods can increase its potential applications for industrial robots. Originality/value – The novelty of the system is the combination of a dual gesture controls method which allows operators to command an industrial robot by posing hand gestures as well as control the robot motion by moving one of their hands in front of the sensor. A facial verification system is integrated to improve the robustness, reliability and security of the control system which also allows assignment of permission levels to different users.

Acquisition of welding skills in industrial robots

2015 ◽  
Vol 42 (2) ◽  
pp. 156-166 ◽  
Author(s):  
J.F. Aviles-Viñas ◽  
I. Lopez-Juarez ◽  
R. Rios-Cabrera
Keyword(s):  
Real Time ◽  
Learning Process ◽  
Vision System ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Content Type ◽  
Welding Machine ◽  
Vision Algorithm

Purpose – The purpose of this paper was to propose a method based on an Artificial Neural Network and a real-time vision algorithm, to learn welding skills in industrial robotics. Design/methodology/approach – By using an optic camera to measure the bead geometry (width and height), the authors propose a real-time computer vision algorithm to extract training patterns and to enable an industrial robot to acquire and learn autonomously the welding skill. To test the approach, an industrial KUKA robot and a welding gas metal arc welding machine were used in a manufacturing cell. Findings – Several data analyses are described, showing empirically that industrial robots can acquire the skill even if the specific welding parameters are unknown. Research limitations/implications – The approach considers only stringer beads. Weave bead and bead penetration are not considered. Practical implications – With the proposed approach, it is possible to learn specific welding parameters despite of the material, type of robot or welding machine. This is due to the fact that the feedback system produces automatic measurements that are labelled prior to the learning process. Originality/value – The main contribution is that the complex learning process is reduced into an input-process-output system, where the process part is learnt automatically without human supervision, by registering the patterns with an automatically calibrated vision system.

Assisted Optimization of the New Robot Dynamic Parameters with Neural Smart Damper

2011 ◽  
Vol 403-408 ◽  
pp. 4167-4173 ◽  
Author(s):  
Adrian Olaru ◽  
Serban Olaru
Keyword(s):  
Mathematical Model ◽  
Dynamic Behavior ◽  
Industrial Robot ◽  
Dynamic Compliance ◽  
Industrial Robots ◽  
Magnetorheological Damper ◽  
Dynamic Parameters ◽  
Theoretical Simulation ◽  
Global Dynamic ◽  
New Research

The paper showed the assisted research of some new dynamic behavior parameters and his influences to the dynamic behavior of the industrial robots. The research contents the mathematical model of these new dynamic parameters, assisted theoretical simulation of the new mathematical model after applies the rheological damper and the assisted establish of the influences of the model coefficients to the characteristics parameters. In the assisted experimental research by data acquisition were established the influences of some different dampers to the dynamic behavior and was proposed one proper smart damper system with magnetorheological damper (MRD), proper neural network with some time delays and recurrent links and soft control with acquisition board. By applying this new research was possible developing the new matrix -vector form of the torsor force- moment and the research of the global dynamic damper behavior and the global dynamic compliance of the industrial robot with proper smart damper system.

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