Pick and place industrial robot controller with computer vision

2016 ◽  
Author(s):  
Pratiksha Andhare ◽  
Sayali Rawat
Keyword(s):  
Computer Vision ◽  
Industrial Robot ◽  
Robot Controller ◽  
Pick And Place

Design of spline surface vacuum gripper for pick and place robotic arms

2020 ◽  
Vol 4 (2) ◽  
pp. 48-55
Author(s):  
A. S. Jamaludin ◽  
M. N. M. Razali ◽  
N. Jasman ◽  
A. N. A. Ghafar ◽  
M. A. Hadi
Keyword(s):  
Cycle Time ◽  
Industrial Robot ◽  
Annealing Process ◽  
Vacuum Suction ◽  
Robotic Arms ◽  
Custom Made ◽  
Pick And Place ◽  
Manufacturing Procedure ◽  
Production Flexibility ◽  
The Impact

The gripper is the most important part in an industrial robot. It is related with the environment around the robot. Today, the industrial robot grippers have to be tuned and custom made for each application by engineers, by searching to get the desired repeatability and behaviour. Vacuum suction is one of the grippers in Watch Case Press Production (WCPP) and a mechanism to improve the efficiency of the manufacturing procedure. Pick and place are the important process for the annealing process. Thus, by implementing vacuum suction gripper, the process of pick and place can be improved. The purpose of vacuum gripper other than design vacuum suction mechanism is to compare the effectiveness of vacuum suction gripper with the conventional pick and place gripper. Vacuum suction gripper is a mechanism to transport part and which later sequencing, eliminating and reducing the activities required to complete the process. Throughout this study, the process pick and place became more effective, the impact on the production of annealing process is faster. The vacuum suction gripper can pick all part at the production which will lower the loss of the productivity. In conclusion, vacuum suction gripper reduces the cycle time about 20%. Vacuum suction gripper can help lower the cycle time of a machine and allow more frequent process in order to increase the production flexibility.

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

scholarly journals A Pick and Place Task with an KUKA Industrial Robot, Aided by an Arduino Board

2019 ◽  
Vol 6 (12) ◽  
pp. 398-400
Author(s):  
Rodrigo Barbosa Tudeschini ◽  
Raphael Barbosa Carneiro de Lima ◽  
Luiz Flavio Martins Pereira ◽  
Álvaro Manoel de Souza Soares
Keyword(s):  
Industrial Robot ◽  
Pick And Place ◽  
Place Task

A Review of Pick and Place Operation Using Computer Vision and ROS

2021 ◽  
pp. 411-418
Author(s):  
Aditya Mathur ◽  
Chetan Bansal ◽  
Sandeep Chauhan ◽  
Omprakash Yadav
Keyword(s):  
Computer Vision ◽  
Pick And Place ◽  
Pick And Place Operation

The Architectural Design of an Industrial Robot Controller

1996 ◽  
Vol 29 (1) ◽  
pp. 367-372
Author(s):  
C. Maffezzoni ◽  
G. Magnani ◽  
L. Ferrarini ◽  
G. Ferretti
Keyword(s):  
Architectural Design ◽  
Industrial Robot ◽  
Robot Controller

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.

scholarly journals A New Type of Industrial Robot Trajectory Generation Component Based on Motion Modularity Technology

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhaoming Liu ◽  
Nailong Liu ◽  
Hongwei Wang ◽  
Shen Tian ◽  
Ning Bai ◽  
...  
Keyword(s):  
Industrial Robot ◽  
Trajectory Generation ◽  
Motion Trajectories ◽  
Robot Controller ◽  
Robot Trajectory ◽  
Fieldbus System ◽  
Sampling Points ◽  
Dynamic Movement Primitives ◽  
Complex Trajectories ◽  
The Brain

Motion modularity is the main method of motion control for higher animals. That means the complex movements of the muscles are made up of basic motion primitives, and the brain or central nervous system does not care about the specific details of the movement. However, the industrial robot control system does not adopt the technical roadmap of motion modularity, it generates complex trajectories by providing a large number of sampling points. This approach is equivalent to using the brain to directly guide the specific movement of the muscle and has to rely on a faster Fieldbus system to obtain complex motion trajectories. This work constructs a modularized industrial robot trajectory generation component based on Dynamic Movement Primitives (DMP) theory. With this component, the robot controller can generate complex trajectories without increasing the sampling points and can obtain good trajectory accuracy. Finally, the rationality of this system is proved by simulations and experiments.

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