scholarly journals Online pose correction of an industrial robot using an optical coordinate measure machine system

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878791 ◽  
Author(s):  
Sepehr Gharaaty ◽  
Tingting Shu ◽  
Ahmed Joubair ◽  
Wen Fang Xie ◽  
Ilian A Bonev
Keyword(s):  
Root Mean Square ◽  
Spot Welding ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Mean Square ◽  
End Effector ◽  
Proportional Integral ◽  
Accuracy Requirement ◽  
Robot Controller ◽  
Correction Scheme

In this article, a dynamic pose correction scheme is proposed to enhance the pose accuracy of industrial robots. The dynamic pose correction scheme uses the dynamic pose measurements as feedback to accurately guide the robot end-effector to the desired pose. The pose is measured online with an optical coordinate measure machine, that is, C-Track 780 from Creaform. A root mean square method is proposed to filter the noise from the pose measurements. The dynamic pose correction scheme adopts proportional-integral-derivaitve controller and generates commands to the FANUC robot controller. The developed dynamic pose correction scheme has been tested on two industrial robots, FANUC LR Mate 200iC and FANUC M20iA. The experimental results on both robots demonstrate that the robots can reach the desired pose with an accuracy of ±0.050 mm for position and ±0.050° for orientation. As a result, the developed pose correction can make the industrial robots meet higher accuracy requirement in the applications such as riveting, drilling, and spot welding.

Finding Features of Positioning Error for Large Industrial Robots Based on Convolutional Neural Network

2021 ◽  
Author(s):  
Daiki Kato ◽  
Kenya Yoshitugu ◽  
Naoki Maeda ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Production Systems ◽  
Industrial Robot ◽  
Three Dimensional ◽  
Industrial Robots ◽  
Numerical Control ◽  
Robot Motion ◽  
Positioning Error ◽  
End Effector

Abstract Most industrial robots are taught using the teaching playback method; therefore, they are unsuitable for use in variable production systems. Although offline teaching methods have been developed, they have not been practiced because of the low accuracy of the position and posture of the end-effector. Therefore, many studies have attempted to calibrate the position and posture but have not reached a practical level, as such methods consider the joint angle when the robot is stationary rather than the features during robot motion. Currently, it is easy to obtain servo information under numerical control operations owing to the Internet of Things technologies. In this study, we propose a method for obtaining servo information during robot motion and converting it into images to find features using a convolutional neural network (CNN). Herein, a large industrial robot was used. The three-dimensional coordinates of the end-effector were obtained using a laser tracker. The positioning error of the robot was accurately learned by the CNN. We extracted the features of the points where the positioning error was extremely large. By extracting the features of the X-axis positioning error using the CNN, the joint 1 current is a feature. This indicates that the vibration current in joint 1 is a factor in the X-axis positioning error.

scholarly journals Modular fastening system and tool–holder working unit for incremental forming

2019 ◽  
Vol 299 ◽  
pp. 05005
Author(s):  
Melania Tera ◽  
Claudia–Emilia Gîrjob ◽  
Cristina–Maria Biriș ◽  
Mihai Crenganiș
Keyword(s):  
Machine Tools ◽  
Incremental Forming ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Cnc Milling ◽  
End Effector ◽  
Tool Holder ◽  
Cnc Milling Machine ◽  
Milling Machines ◽  
Sheet Metal Workpiece

Incremental forming can be usually unfolded either on CNC milling machine–tools or serial industrial robots. The approach proposed in this paper tackles the problem of designing a modular fastening system, which can be adapted for both above mentioned technological equipment. The fastening system of the sheet–metal workpiece is composed of a fixing plate and a retaining plate. The fixing and retaining plates will be made up of different individual elements, which can be easily repositioned to obtain different sizes of the part. Moreover, the fastening system has to be able to be positioned either horizontally (to be fitted on CNC milling machines) or vertically (to be fitted on industrial robots. The paper also presents the design of a tool–holder working unit which will be fitted on KUKA KR 210 industrial robot. The working unit will be mounted as end–effector of the robot and will bear the punch, driving it on the processing toolpaths.

scholarly journals Event-driven industrial robot control architecture for the Adept V+ platform

2019 ◽  
Author(s):  
Oleksandr Semeniuta ◽  
Petter Falkman
Keyword(s):  
Industrial Robot ◽  
Discrete Event ◽  
Industrial Robots ◽  
Coupled Systems ◽  
Level Control ◽  
Control Module ◽  
Computing Device ◽  
Robot Controller ◽  
Event Driven ◽  
Event Based

Modern industrial robotic systems are highly interconnected. They operate in a distributed environment and communicate with sensors, computer vision systems, mechatronic devices, and computational components. On the fundamental level, communication and coordination between all parties in such distributed system are characterized by discrete event behavior. The latter is largely attributed to the specifics of communication over the network, which, in terms, facilitates asynchronous programming and explicit event handling. In addition, on the conceptual level, events are an important building block for realizing reactivity and coordination. Event-driven architecture has manifested its effectiveness for building loosely-coupled systems based on publish-subscribe middleware, either general-purpose or robotic-oriented. Despite all the advances in middleware, industrial robots remain difficult to program in context of distributed systems, to a large extent due to the limitation of the native robot platforms. This paper proposes an architecture for flexible event-based control of industrial robots based on the Adept V+ platform. The architecture is based on the robot controller providing a TCP/IP server and a collection of robot skills, and a high-level control module deployed to a dedicated computing device. The control module possesses bidirectional communication with the robot controller and publish/subscribe messaging with external systems. It is programmed in asynchronous style using pyadept, a Python library based on Python coroutines, AsyncIO event loop and ZeroMQ middleware. The proposed solution facilitates integration of Adept robots into distributed environments and building more flexible robotic solutions with event-based logic.

scholarly journals Predicting Positioning Error and Finding Features for Large Industrial Robots Based on Deep Learning

2021 ◽  
Vol 15 (2) ◽  
pp. 206-214
Author(s):  
Daiki Kato ◽  
Kenya Yoshitsugu ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
Kenichi Takahashi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computer Aided Design ◽  
Joint Angle ◽  
Industrial Robot ◽  
Movement Time ◽  
Vertical Axis ◽  
Industrial Robots ◽  
Positioning Error ◽  
Design Data ◽  
End Effector

In this study, we evaluated the motion accuracy of a large industrial robot and its compensation method and constructed an off-line teaching operation based on three-dimensional computer aided design data. In this experiment, we used a laser tracker to measure the coordinates of the end effector of the robot. Simultaneously, the end-effector coordinates, each joint angle, the maximum current of the motors attached to each joint, and rotation speed of each joint were measured. This servo information was converted into image data as visible information. For each robot movement path, an image was created; the horizontal axis represented the movement time of the robot and the vertical axis represented the servo information. A convolutional neural network (CNN), a type of deep learning, was used to predict the positioning error with high accuracy. Subsequently, to identify the features of the positioning error, the image was divided into several analysis areas, one of which was filled with various colors and analyzed by the CNN. If the prediction accuracy of the CNN decreased, then the analysis area would be identified as a feature. Thus, the features of the Y-axis positioning error were observed for teaching each joint angle in the opposite direction just after the start of the motion, overshoot of the rotational joint current, and the change in the swivel joint current.

scholarly journals Event-driven industrial robot control architecture for the Adept V+ platform

2019 ◽  
Vol 5 ◽  
pp. e207
Author(s):  
Oleksandr Semeniuta ◽  
Petter Falkman
Keyword(s):  
Industrial Robot ◽  
Discrete Event ◽  
Industrial Robots ◽  
Coupled Systems ◽  
Level Control ◽  
Control Module ◽  
Computing Device ◽  
Robot Controller ◽  
Event Driven ◽  
Event Based

Modern industrial robotic systems are highly interconnected. They operate in a distributed environment and communicate with sensors, computer vision systems, mechatronic devices, and computational components. On the fundamental level, communication and coordination between all parties in such distributed system are characterized by discrete event behavior. The latter is largely attributed to the specifics of communication over the network, which, in terms, facilitates asynchronous programming and explicit event handling. In addition, on the conceptual level, events are an important building block for realizing reactivity and coordination. Event-driven architecture has manifested its effectiveness for building loosely-coupled systems based on publish-subscribe middleware, either general-purpose or robotic-oriented. Despite all the advances in middleware, industrial robots remain difficult to program in context of distributed systems, to a large extent due to the limitation of the native robot platforms. This paper proposes an architecture for flexible event-based control of industrial robots based on the Adept V+ platform. The architecture is based on the robot controller providing a TCP/IP server and a collection of robot skills, and a high-level control module deployed to a dedicated computing device. The control module possesses bidirectional communication with the robot controller and publish/subscribe messaging with external systems. It is programmed in asynchronous style using pyadept, a Python library based on Python coroutines, AsyncIO event loop and ZeroMQ middleware. The proposed solution facilitates integration of Adept robots into distributed environments and building more flexible robotic solutions with event-based logic.

scholarly journals TRAJECTORY ERROR MINIMISATION METHOD DUE TO TRANSIENT REGIME VARIATION OF INDUSTRIAL ROBOTS KINEMATIC LINKS

2019 ◽  
Vol 25 (3) ◽  
Author(s):  
ANDREI LUNCANU ◽  
GHEORGHE STAN
Keyword(s):  
Industrial Robot ◽  
Transient Regime ◽  
Industrial Robots ◽  
End Effector ◽  
Trajectory Error ◽  
Transient Regimes ◽  
Method Of Measurement ◽  
The Difference

<p>In the current industry, industrial robots are gaining more and more ground to classical positioning methods, especially due to the ratio of workspace / volume of the industrial robot. For this reason, methods of minimization of trajectory errors are necessary. Among the multitudes of factors that affect the trajectory precision is the difference between the programmed transient regime and the measured transient regime of the kinematic link used in the structure of the industrial robots. In this paper is presented the method of measurement the transient regimes of the end-effector and a method of compensation of the trajectory error.</p>

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 Analysis of the Compliance Properties of an Industrial Robot with the Mozzi Axis Approach

Robotics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 80 ◽  
Author(s):  
Doria ◽  
Cocuzza ◽  
Comand ◽  
Bottin ◽  
Rossi
Keyword(s):  
Industrial Robot ◽  
Industrial Robots ◽  
Robotic Assembly ◽  
Robot Arm ◽  
Machined Surface ◽  
End Effector ◽  
Chatter Vibrations ◽  
Modes Of Vibration ◽  
Machining Robot ◽  
Robot Performance

In robotic processes, the compliance of the robot arm plays a very important role. In some conditions, for example, in robotic assembly, robot arm compliance can compensate for small position and orientation errors of the end-effector. In other processes, like machining, robot compliance may generate chatter vibrations with an impairment in the quality of the machined surface. In industrial robots, the compliance of the end-effector is chiefly due to joint compliances. In this paper, joint compliances of a serial six-joint industrial robot are identified with a novel modal method making use of specific modes of vibration dominated by the compliance of only one joint. Then, in order to represent the effect of the identified compliances on robot performance in an intuitive and geometric way, a novel kinematic method based on the concept of “Mozzi axis” of the end-effector is presented and discussed.

scholarly journals Event-driven industrial robot control architecture for the Adept V+ platform

2019 ◽  
Author(s):  
Oleksandr Semeniuta ◽  
Petter Falkman
Keyword(s):  
Industrial Robot ◽  
Discrete Event ◽  
Industrial Robots ◽  
Coupled Systems ◽  
Level Control ◽  
Control Module ◽  
Computing Device ◽  
Robot Controller ◽  
Event Driven ◽  
Event Based

Modern industrial robotic systems are highly interconnected. They operate in a distributed environment and communicate with sensors, computer vision systems, mechatronic devices, and computational components. On the fundamental level, communication and coordination between all parties in such distributed system are characterized by discrete event behavior. The latter is largely attributed to the specifics of communication over the network, which, in terms, facilitates asynchronous programming and explicit event handling. In addition, on the conceptual level, events are an important building block for realizing reactivity and coordination. Event-driven architecture has manifested its effectiveness for building loosely-coupled systems based on publish-subscribe middleware, either general-purpose or robotic-oriented. Despite all the advances in middleware, industrial robots remain difficult to program in context of distributed systems, to a large extent due to the limitation of the native robot platforms. This paper proposes an architecture for flexible event-based control of industrial robots based on the Adept V+ platform. The architecture is based on the robot controller providing a TCP/IP server and a collection of robot skills, and a high-level control module deployed to a dedicated computing device. The control module possesses bidirectional communication with the robot controller and publish/subscribe messaging with external systems. It is programmed in asynchronous style using pyadept, a Python library based on Python coroutines, AsyncIO event loop and ZeroMQ middleware. The proposed solution facilitates integration of Adept robots into distributed environments and building more flexible robotic solutions with event-based logic.

scholarly journals Positioning Error Calibration of Industrial Robots Based on Random Forest

2021 ◽  
Vol 15 (5) ◽  
pp. 581-589
Author(s):  
Daiki Kato ◽  
Kenya Yoshitsugu ◽  
Naoki Maeda ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
...  
Keyword(s):  
Random Forest ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Target Point ◽  
Calibration Model ◽  
Positioning Error ◽  
End Effector ◽  
Positioning Errors ◽  
Random Forest Method ◽  
Kinematic Errors

Because most industrial robots are taught using the direct teaching and playback method, they are unsuitable for variable production systems. Alternatively, the offline teaching method has limited applications because of the low accuracy of the position and posture of the end-effector. Therefore, many studies have been conducted to calibrate the position and posture. Positioning errors of robots can be divided into kinematic and non-kinematic errors. In some studies, kinematic errors are calibrated by kinematic models, and non-kinematic errors are calibrated by neural networks. However, the factor of the positioning errors has not been identified because the neural network is a black box. In another machine learning method, a random forest is constructed from decision trees, and its structure can be visualized. Therefore, we used a random forest method to construct a calibration model for the positioning errors and to identify the positioning error factors. The proposed calibration method is based on a simulation of many candidate points centered on the target point. A large industrial robot was used, and the 3D coordinates of the end-effector were obtained using a laser tracker. The model predicted the positioning error from end-effector coordinates, joint angles, and joint torques using the random forest method. As a result, the positioning error was predicted with a high accuracy. The random forest analysis showed that joint 2 was the primary factor of the X- and Z-axis errors. This suggests that the air cylinder used as an auxiliary to the servo motor of joint 2, which is unique to large industrial robots, is the error factor. With the proposed calibration, the positioning error norm was reduced at all points.

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