Open AccessIntegrating Vision System to a Pick and Place Cartesian Robot
Author(s) -
K. T. Tan,
Mohd Nasir Ayob,
H. B. Hassrizal,
Abdul Halim Ismail,
M. S. Muhamad Azmi,
M S M Hashim,
Siti Marhainis Othman,
A. B. Shahriman,
Yi Hui Low
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2107/1/012037
Subject(s) - computer vision , artificial intelligence , computer science , machine vision , template matching , sorting , edge detection , image processing , enhanced data rates for gsm evolution , transformation (genetics) , robot , canny edge detector , matching (statistics) , image (mathematics) , mathematics , algorithm , biochemistry , chemistry , gene , statistics
Vision aided pick and place cartesian robot is a combination of machine vision system and robotic system. They communicate with each other simultaneously to perform object sorting. In this project, machine vision algorithm for object sorting to solve the problem in failure sorting due to imperfection of images edges and different types of colours is proposed. The image is acquired by a camera and followed by image calibration. Pre-processing of image is performed through these methods, which are HSI colour space transformation, Gaussian filter for image filtering, Otsu’s method for image binarization, and Canny edge detection. LabVIEW edge-based geometric matching is selected for template matching. After the vision application analysed the image, electrical signal will send to robotic arm for object sorting if the acquired image is matched with template image. The proposed machine vision algorithm has yielded an accurate template matching score from 800 to 1000 under different disturbances and conditions. This machine vision algorithm provides more customizable parameters for each methods yet improves the accuracy of template matching.