High precise and zero-cost solution for fully automatic industrial robot TCP calibration

2019 ◽  
Vol 46 (5) ◽  
pp. 650-659
Author(s):  
Mustafa Cakir ◽  
Cengiz Deniz
Keyword(s):  
Fixed Point ◽  
Measuring Equipment ◽  
Industrial Robot ◽  
Absolute Calibration ◽  
Robot Calibration ◽  
Special Equipment ◽  
Content Type ◽  
Robot Controller ◽  
Contact Points ◽  
Plane Plate

Purpose The purpose of this study is to present a novel method for industrial robot TCP (tool center point) calibration. The proposed method offers fully automated robot TCP calibration within a defined cycle time. The method is applicable for large-scale installations due to its zero cost for each robot. Design/methodology/approach Precise and expensive measuring equipment or specially designed reference devices are required for robot calibration. The calibration can be performed by using only one plane plate in this method, and the calibration procedure is defined step by step: the robot moves to the target plane position. Then, the TCP touches the plane and the actual robot configuration is recorded. Then robot moves back into position and the same step is repeated for a new sample. Alternatively, the robot can be stationary and the plane can be moved towards the robot TCP. TCP is calculated by processing the difference of the contact points recorded at different positions. The process is fully automated. No special equipment is used. The calculations are very simple, and the robot controller can easily be realized. Findings The conventional manual robot TCP calibration process takes about 15 min and takes more time in case of the high accuracy. The proposed method reduces this time to less than 3 min without operator support. Practical tests have shown that TCP calibration can be performed with 0.1-0.6 mm of accuracy. This solution is an automated process and does not require special installation and it also has approximately zero cost. For this reason, this study recommends using the proposed solution widely in areas where even one or hundreds of robots are located. Research limitations/implications In this study, the data were directly taken from the robot controller without using any special measuring equipment. The industrial robot used in the tests has no absolute calibration. The classical “four-point method” was used for reference TCP data. It is the initial acceptance that this process conducted with extreme care and by using a needle-tipped tool will not produce exact values. It was observed that deviation of the TCP from a fixed point in reorient motions was not more than 0.5 mm. This method has been validated for different bits. The pilot works for different robot applications in Ford Otosan Gölcük Plant have been completed and dissemination has started. Originality/value Although the approach uses is clear and simple, it is surprising that the calculation of TCP using plane equations has so far not been mentioned in the literature. The disadvantage of using either fixed point or sphere as a reference is that the TCP cannot automatically guide to the target. This problem was overcome with the use of a larger target plane plate and the process was fully automated. The proposed method can be widely used in practical applications.

A simple and rapid calibration methodology for industrial robot based on geometric constraint and two-step error

2018 ◽  
Vol 45 (6) ◽  
pp. 715-721 ◽  
Author(s):  
Jiabo Zhang ◽  
Xibin Wang ◽  
Ke Wen ◽  
Yinghao Zhou ◽  
Yi Yue ◽  
...  
Keyword(s):  
Coordinate System ◽  
Large Scale ◽  
Industrial Robot ◽  
Geometric Constraint ◽  
Machining Process ◽  
Kinematic Parameters ◽  
Robot Calibration ◽  
Positioning Accuracy ◽  
Content Type ◽  
Rapid Calibration

Purpose The purpose of this study is the presentation and research of a simple and rapid calibration methodology for industrial robot. Extensive research efforts were devoted to meet the requirements of online compensation, closed-loop feedback control and high-precision machining during the flexible machining process of robot for large-scale cabin. Design/methodology/approach A simple and rapid method to design and construct the transformation relation between the base coordinate system of robot and the measurement coordinate system was proposed based on geometric constraint. By establishing the Denavit–Hartenberg model for robot calibration, a method of two-step error for kinematic parameters calibration was put forward, which aided in achievement of step-by-step calibration of angle and distance errors. Furthermore, KUKA robot was considered as the research object, and related experiments were performed based on laser tracker. Findings The experimental results demonstrated that the accuracy of the coordinate transformation could reach 0.128 mm, which meets the transformation requirements. Compared to other methods used in this study, the calibration method of two-step error could significantly improve the positioning accuracy of robot up to 0.271 mm. Originality/value The methodology based on geometric constraint and two-step error is simple and can rapidly calibrate the kinematic parameters of robot. It also leads to the improvement in the positioning accuracy of robot.

Interfacing an industrial robot and MATLAB for predictive visual servoing

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Megha G. Krishnan ◽  
Abhilash T. Vijayan ◽  
Ashok S.
Keyword(s):  
Real Time ◽  
Control Algorithm ◽  
Visual Servoing ◽  
Industrial Robot ◽  
Effective Communication ◽  
Industrial Robots ◽  
Content Type ◽  
Robot Controller ◽  
Predictive Controller ◽  
And Control

Purpose Real-time implementation of sophisticated algorithms on robotic systems demands a rewarding interface between hardware and software components. Individual robot manufacturers have dedicated controllers and languages. However, robot operation would require either the knowledge of additional software or expensive add-on installations for effective communication between the robot controller and the computation software. This paper aims to present a novel method of interfacing the commercial robot controllers with most widely used simulation platform, e.g. MATLAB in real-time with a demonstration of visual predictive controller. Design/methodology/approach A remote personal computer (PC), running MATLAB, is connected with the IRC5 controller of an ABB robotic arm through the File Transfer Protocol (FTP). FTP server on the IRC5 responds to a request from an FTP client (MATLAB) on a remote computer. MATLAB provides the basic platform for programming and control algorithm development. The controlled output is transferred to the robot controller through Ethernet port as files and, thereby, the proposed scheme ensures connection and control of the robot using the control algorithms developed by the researchers without the additional cost of buying add-on packages or mastering vendor-specific programming languages. Findings New control strategies and contrivances can be developed with numerous conditions and constraints in simulation platforms. When the results are to be implemented in real-time systems, the proposed method helps to establish a simple, fast and cost-effective communication with commercial robot controllers for validating the real-time performance of the developed control algorithm. Practical implications The proposed method is used for real-time implementation of visual servo control with predictive controller, for accurate pick-and-place application with different initial conditions. The same strategy has been proven effective in supervisory control using two cameras and artificial neural network-based visual control of robotic manipulators. Originality/value This paper elaborates a real-time example using visual servoing for researchers working with industrial robots, enabling them to understand and explore the possibilities of robot communication.

scholarly journals Robot Calibration for Cooperative Process under Typical Installation

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Yahui Gan ◽  
Xianzhong Dai ◽  
Donghui Dong
Keyword(s):  
Industrial Robot ◽  
Industrial Process ◽  
Industrial Robots ◽  
Cooperative Robots ◽  
Robot Calibration ◽  
Contact Points ◽  
Calibration Problem ◽  
Operation Procedure ◽  
Cooperative Process ◽  
Easy Operation

A method with easy operation procedure and simple calibration condition is presented in this paper to solve the base frame calibration problem for cooperative robots. It is carried out through constructing a series of handclasp configurations and recording coordinates of the contact points, respectively, in base frame of each robot. Then the rotation matrix and translation matrix between base frame of cooperative robots can be calculated which is just the calibration result for cooperative robots. Based on typical installation mode for industrial robot, the floor mounted, wall mounted and ceiling mounted, constraints between base frames of these robots are further explored. These constraints are used to improve the calibration results for base frame calibration problem. In order to validate the correctness and effectiveness of our method, experiments on two industrial robots (Motoman VA1400 and HP20) are carried out at the end of the paper. The calibration errors are less than 8 mm in most cases, which satisfies the requirement of positioning accuracy for most industrial process, such as arc welding, transporting, and cutting. These experiment results assert the correctness of our method which can be used effectively to solve the base frame calibration problem for cooperative robots in manufacturing process.

The parameter identification model considering both geometric parameters and joint stiffness

2019 ◽  
Vol 47 (1) ◽  
pp. 76-81
Author(s):  
Jing Bai ◽  
Le Fan ◽  
Shuyang Zhang ◽  
Zengcui Wang ◽  
Xiansheng Qin
Keyword(s):  
Parameter Identification ◽  
Industrial Robot ◽  
Joint Stiffness ◽  
Geometric Parameters ◽  
Least Square ◽  
Experimental Result ◽  
Positional Accuracy ◽  
Content Type ◽  
The Absolute ◽  
Identification Model

Purpose Both geometric and non-geometric parameters have noticeable influence on the absolute positional accuracy of 6-dof articulated industrial robot. This paper aims to enhance it and improve the applicability in the field of flexible assembling processing and parts fabrication by developing a more practical parameter identification model. Design/methodology/approach The model is developed by considering both geometric parameters and joint stiffness; geometric parameters contain 27 parameters and the parallelism problem between axes 2 and 3 is involved by introducing a new parameter. The joint stiffness, as the non-geometric parameter considered in this paper, is considered by regarding the industrial robot as a rigid linkage and flexible joint model and adds six parameters. The model is formulated as the form of error via linearization. Findings The performance of the proposed model is validated by an experiment which is developed on KUKA KR500-3 robot. An experiment is implemented by measuring 20 positions in the work space of this robot, obtaining least-square solution of measured positions by the software MATLAB and comparing the result with the solution without considering joint stiffness. It illustrates that the identification model considering both joint stiffness and geometric parameters can modify the theoretical position of robots more accurately, where the error is within 0.5 mm in this case, and the volatility is also reduced. Originality/value A new parameter identification model is proposed and verified. According to the experimental result, the absolute positional accuracy can be remarkably enhanced and the stability of the results can be improved, which provide more accurate parameter identification for calibration and further application.

Human skill integrated motion planning of assembly manipulation for 6R industrial robot

2019 ◽  
Vol 46 (1) ◽  
pp. 171-180
Author(s):  
Yi Liu ◽  
Ming Cong ◽  
Hang Dong ◽  
Dong Liu
Keyword(s):  
Deep Learning ◽  
Motion Planning ◽  
Industrial Robot ◽  
Robotic Assembly ◽  
3D Vision ◽  
Content Type ◽  
Human Skill ◽  
Depth Images ◽  
Positioning Method ◽  
Assembly Technology

Purpose The purpose of this paper is to propose a new method based on three-dimensional (3D) vision technologies and human skill integrated deep learning to solve assembly positioning task such as peg-in-hole. Design/methodology/approach Hybrid camera configuration was used to provide the global and local views. Eye-in-hand mode guided the peg to be in contact with the hole plate using 3D vision in global view. When the peg was in contact with the workpiece surface, eye-to-hand mode provided the local view to accomplish peg-hole positioning based on trained CNN. Findings The results of assembly positioning experiments proved that the proposed method successfully distinguished the target hole from the other same size holes according to the CNN. The robot planned the motion according to the depth images and human skill guide line. The final positioning precision was good enough for the robot to carry out force controlled assembly. Practical implications The developed framework can have an important impact on robotic assembly positioning process, which combine with the existing force-guidance assembly technology as to build a whole set of autonomous assembly technology. Originality/value This paper proposed a new approach to the robotic assembly positioning based on 3D visual technologies and human skill integrated deep learning. Dual cameras swapping mode was used to provide visual feedback for the entire assembly motion planning process. The proposed workpiece positioning method provided an effective disturbance rejection, autonomous motion planning and increased overall performance with depth images feedback. The proposed peg-hole positioning method with human skill integrated provided the capability of target perceptual aliasing avoiding and successive motion decision for the robotic assembly manipulation.

The Pransky interview: Dr Yoky Matsuoka, Vice President Technology, Nest Labs

2014 ◽  
Vol 41 (6) ◽  
pp. 481-486
Author(s):  
Joanne Pransky
Keyword(s):  
Computer Science ◽  
Mechanical Engineering ◽  
Academic Career ◽  
Industrial Robot ◽  
Vice President ◽  
Early Career ◽  
Smoke Detector ◽  
Foundation Engineering ◽  
Content Type ◽  
The University

Purpose – This article is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned entrepreneur regarding the evolution, commercialization and challenges of bringing a technological invention to market. Design/methodology/approach – The interviewee is Dr Yoky Matsuoka, the Vice President of Nest Labs. Matsuoka describes her career journey that led her from a semi-professional tennis player who wanted to build a robot tennis buddy, to a pioneer of neurobotics who then applied her multidisciplinary research in academia to the development of a mass-produced intelligent home automation device. Findings – Dr Matsuoka received a BS degree from the University of California, Berkeley and an MS and PhD in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT). She was also a Postdoctoral Fellow in the Brain and Cognitive Sciences at MIT and in Mechanical Engineering at Harvard University. Dr Matsuoka was formerly the Torode Family Endowed Career Development Professor of Computer Science and Engineering at the University of Washington (UW), Director of the National Science Foundation Engineering Research Center for Sensorimotor Neural Engineering and Ana Loomis McCandless Professor of Robotics and Mechanical Engineering at Carnegie Mellon University. In 2010, she joined Google X as one of its three founding members. She then joined Nest as VP of Technology. Originality/value – Dr Matsuoka built advanced robotic prosthetic devices and designed complementary rehabilitation strategies that enhanced the mobility of people with manipulation disabilities. Her novel work has made significant scientific and engineering contributions in the combined fields of mechanical engineering, neuroscience, bioengineering, robotics and computer science. Dr Matsuoka was awarded a MacArthur Fellowship in which she used the Genius Award money to establish a nonprofit corporation, YokyWorks, to continue developing engineering solutions for humans with physical disabilities. Other awards include the Emerging Inventor of the Year, UW Medicine; IEEE Robotics and Automation Society Early Academic Career Award; Presidential Early Career Award for Scientists and Engineers; and numerous others. She leads the development of the learning and control technology for the Nest smoke detector and Thermostat, which has saved the USA hundreds of billions of dollars in energy expenses. Nest was sold to Google in 2013 for a record $3.2 billion dollars in cash.

scholarly journals An Experimental Security Analysis of an Industrial Robot Controller

2017 ◽  
Author(s):  
Davide Quarta ◽  
Marcello Pogliani ◽  
Mario Polino ◽  
Federico Maggi ◽  
Andrea Maria Zanchettin ◽  
...  
Keyword(s):  
Industrial Robot ◽  
Security Analysis ◽  
Robot Controller

The Pransky interview: Dr Nabil Simaan, Vanderbilt University Professor of Mechanical Engineering, Computer Science and Otolaryngology, Thought Leader in Medical Robotics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Joanne Pransky
Keyword(s):  
Computer Science ◽  
Mechanical Engineering ◽  
Single Port ◽  
Industrial Robot ◽  
Medical Robotics ◽  
Surgical Robotics ◽  
Robotic Systems ◽  
Content Type ◽  
Thought Leader ◽  
Vanderbilt University

Purpose The following article is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD and innovator regarding his pioneering efforts. The paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr Nabil Simaan, Professor of Mechanical Engineering, Computer Science and Otolaryngology at Vanderbilt University. He is also director of Vanderbilt’s Advanced Robotics and Mechanism Applications Research Laboratory. In this interview, Simaan shares his unique perspective and approaches on his journey of trying to solve real-world problems in the medical robotics area. Findings Simaan received his BSc, MSc and PhD in mechanical engineering from the Technion – Israel Institute of Technology. He served as Postdoctoral Research Scientist in Computer Science at Johns Hopkins University. In 2005, he joined Columbia University, New York, NY, as an Assistant Professor of Mechanical Engineering until 2010, when he joined Vanderbilt. His current applied research interests include synthesis of novel robotic systems for surgical assistance in confined spaces with applications to minimally invasive surgery of the throat, natural orifice surgery, cochlear implant surgery and dexterous bimanual microsurgery. Theoretical aspects of his research include robot design and kinematics. Originality/value Dr Simaan is a leading pioneer on designing robotic systems and mechanisms for medical applications. Examples include technologies for snake robots licensed to Intuitive Surgical; technologies for micro-surgery of the retina, which led to the formation of AURIS Surgical Robotics; the insertable robotic effector platform (IREP) single-port surgery robot that served as the research prototype behind the Titan Medical Inc. Sport (Single Port Orifice Robotic Technology). Simaan received the NSF Career award for young investigators to design new algorithms and robots for safe interaction with the anatomy. He has served as the Editor for IEEE International Conference on Robotics and Automation, Associate Editor for IEEE Transactions on Robotics, Editorial Board Member of Robotica, Area Chair for Robotics Science and Systems and corresponding Co-chair for the IEEE Technical Committee on Surgical Robotics. In January 2020, he was bestowed the award of Institute of Electrical and Electronics Engineers (IEEE) Fellow for Robotics Advancements. At the end of 2020, he was named a top voice in health-care robotics by technology discovery platform InsightMonk and market intelligence firm BIS Research. Simaan holds 15 patents. A producer of human capital, his education goal is to achieve the best possible outcome with every student he works with.

The Pransky interview: Dr Jun Ho Oh, Professor and Director of Humanoid Robot Research Center, KAIST; Cofounder, Rainbow Robotics Co.

2017 ◽  
Vol 44 (6) ◽  
pp. 695-699
Author(s):  
Joanne Pransky
Keyword(s):  
Mechanical Engineering ◽  
Humanoid Robot ◽  
Industrial Robot ◽  
Science And Technology ◽  
Vice President ◽  
Humanoid Robots ◽  
Real Time Control ◽  
Commercial Development ◽  
Content Type ◽  
The University

Purpose The following paper is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD-turned-entrepreneur regarding the commercialization and challenges of bringing a technological invention to market. This paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr Jun Ho Oh, Professor of Mechanical Engineering at the Korea Advanced Institute of Science and Technology (KAIST) and Director of KAIST’s Hubolab. Determined to build a humanoid robot in the early 2000s to compete with Japan’s humanoids, Dr Oh and KAIST created the KHR1. This research led to seven more advanced versions of a biped humanoid robot and the founding of the Robot for Artificial Intelligence and Boundless Walking (Rainbow) Co., a professional technological mechatronics company. In this interview, Dr Oh shares the history and success of Korea’s humanoid robot research. Findings Dr Oh received his BSc in 1977 and MSc in Mechanical Engineering in 1979 from Yonsei University. Oh worked as a Researcher for the Korea Atomic Energy Research Institute before receiving his PhD from the University of California (UC) Berkeley in mechanical engineering in 1985. After his PhD, Oh remained at UC Berkeley to do Postdoctoral research. Since 1985, Oh has been a Professor of Mechanical Engineering at KAIST. He was a Visiting Professor from 1996 to 1997 at the University of Texas Austin. Oh served as the Vice President of KAIST from 2013-2014. In addition to teaching, Oh applied his expertise in robotics, mechatronics, automatic and real-time control to the commercial development of a series of humanoid robots. Originality/value Highly self-motivated and always determined, Dr Oh’s initial dream of building the first Korean humanoid bipedal robot has led him to become one of the world leaders of humanoid robots. He has contributed widely to the field over the nearly past two decades with the development of five versions of the HUBO robot. Oh led Team KAIST to win the 2015 DARPA Robotics Challenge (DRC) and a grand prize of US$2m with its humanoid robot DRC-HUBO+, beating 23 teams from six countries. Oh serves as a robotics policy consultant for the Korean Ministry of Commerce Industry and Energy. He was awarded the 2016 Changjo Medal for Science and Technology, the 2016 Ho-Am Prize for engineering, and the 2010 KAIST Distinguished Professor award. He is a member of the Korea Academy of Science and Technology.

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.

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