Characterizing the influence of surface roughness and inclination on 3D vision sensor performance

2015 ◽  
Author(s):  
John R. Hodgson ◽  
Peter Kinnell ◽  
Laura Justham ◽  
Michael R. Jackson
Keyword(s):  
Surface Roughness ◽  
Vision Sensor ◽  
3D Vision ◽  
Sensor Performance

scholarly journals Applicationof Computer Visionfor Polishing RobotinAutomotive Manufacturing Industries

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Adnan Rachmat Anom Besari ◽  
Ruzaidi Zamri ◽  
Md. Dan Md. Palil ◽  
Anton Satria Prabuwono
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Networks ◽  
Surface Defects ◽  
Vision System ◽  
Real Data ◽  
Surface Measurement ◽  
Vision Sensor ◽  
Skilled Workers ◽  
Artificial Neural

Polishing is a highly skilled manufacturing process with a lot of constraints and interaction with environment. In general, the purpose of polishing is to get the uniform surface roughness distributed evenly throughout part’s surface. In order to reduce the polishing time and cope with the shortage of skilled workers, robotic polishing technology has been investigated. This paper studies about vision system to measure surface defects that have been characterized to some level of surface roughness. The surface defects data have learned using artificial neural networks to give a decision in order to move the actuator of arm robot. Force and rotation time have chosen as output parameters of artificial neural networks. Results shows that although there is a considerable change in both parameter values acquired from vision data compared to real data, it is still possible to obtain surface defects characterization using vision sensor to a certain limit of accuracy. The overall results of this research would encourage further developments in this area to achieve robust computer vision based surface measurement systems for industrial robotic, especially in polishing process.Keywords: polishing robot, vision sensor, surface defects, and artificial neural networks

scholarly journals A ToF-Camera as a 3D Vision Sensor for Autonomous Mobile Robotics

10.5772/61348 ◽  
2015 ◽  
Vol 12 (11) ◽  
pp. 156 ◽  
Author(s):  
Sobers Lourdu Xavier Francis ◽  
Sreenatha G. Anavatti ◽  
Matthew Garratt ◽  
Hyunbgo Shim
Keyword(s):  
Mobile Robotics ◽  
Vision Sensor ◽  
3D Vision ◽  
Tof Camera ◽  
Autonomous Mobile Robotics

scholarly journals Depth Data Error Modeling of the ZED 3D Vision Sensor from Stereolabs

2018 ◽  
Vol 17 (1) ◽  
pp. 1 ◽  
Author(s):  
Luis Enrique Ortiz ◽  
Viviana Elizabeth Cabrera ◽  
Luiz M G Goncalves
Keyword(s):  
Error Modeling ◽  
Vision Sensor ◽  
3D Vision ◽  
Depth Data ◽  
Data Error

3D-Inspektion im Stahlwerk: kein „heißes Eisen“ für das Vision-System

Konstruktion ◽  
2019 ◽  
Vol 71 (07-08) ◽  
pp. 18-20
Author(s):  
Markus Weinhofer
Keyword(s):  
Vision System ◽  
Vision Sensor ◽  
3D Vision ◽  
System P

Optische 3D-Inspektion in der Heißproduktion von Stranggussanlagen, Warm- und Rohrwalzwerken – dort, wo flimmernde Oberflächen, Hitze, Zunder, Schmutz und Feuchtigkeit an der Tagesordnung sind? Für die IMS Messsysteme GmbH – Marktführer auf diesem Gebiet – ist dies eine wesentliche Kernkompetenz. Das Unternehmen setzt nun seit über einem Jahr einen 3D-Vision-Sensor ein, der die Performance der eigenen Inspektionssysteme deutlich verbessert und zugleich neue Anwendungsfelder und Geschäftsmöglichkeiten erschließt.

3D vision sensor and its algorithm on clone seedlings plant system

2007 ◽  
Author(s):  
Jun-ichiro Hayashi ◽  
Takehisa Hiroyasu ◽  
Hirotaka Hojo ◽  
Seiji Hata ◽  
Hiroshi Okada
Keyword(s):  
Vision Sensor ◽  
3D Vision ◽  
Plant System
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