Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2018.1437683
A comparative study on the performance of neural networks in visual guidance and feedback applications
Abhilash T. Vijayan
; Department of Electrical Engineering, National Institute of Technology, Calicut, India
Ashok Sankar
; Department of Electrical Engineering, National Institute of Technology, Calicut, India
Sudheer A. P
; Department of Mechanical Engineering, National Institute of Technology, Calicut, India
Sažetak
Vision-based systems increase the flexibility of industrial automation applications by providing non-touching sensory information for processing and feedback. Artificial neural networks (ANNs) help such conformities through prediction in overcoming nonlinear computational
spaces. They transform multiple possibilities of outcomes or regions of uncertainty posed by the system components towards solution spaces. Trained networks impart a certain level of intelligence to robotic systems. This paper discusses two applications of machine vision. The 3
degrees of freedom (DOF) robotic assembly provides an accurate cutting of soft materials with visual guidance using pixel elimination. The 6-DOF robot combines visual guidance from a supervisory camera and visual feedback from an attached camera. Using a switching approach in the control strategy, pick and place applications are carried out. With the inclusion of ANN to make the strategies intelligent, both the systems performed better with regard to computational time and convergence. The networks make use of the extracted image features
from the scene for different applications. Simulation and experimental results validate the proposed schemes and show the effectiveness of ANN in machine vision applications.
Ključne riječi
Machine vision; industrial automation; visual servoing; ANN; condition number
Hrčak ID:
203372
URI
Datum izdavanja:
21.3.2018.
Posjeta: 947 *