IMPLEMENTATION AND EVALUATION OF IMAGE PROCESSING TECHNIQUES ON A VISION NAVIGATION LINE FOLLOWER ROBOT
Walaa E. Elhady, Heba A. Elnemr and Gamal Selim
DOI : 10.3844/jcssp.2014.1036.1044
Journal of Computer Science
Volume 10, Issue 6
In a fast growing industrial world, carriers are required to carry products from one manufacturing plant to another which are usually in different buildings or separate blocks. This study intends to automate this sector using vision controlled mobile robots instead of laying railway tracks which are both expensive and inconvenient. To achieve this purpose an autonomous robot with computer vision as its primary sensor for gaining information about its environment for path following is developed. The proposed Line Follower Robot (LFR) consists of web cam mounted on the vehicle and connected to Matlab platform. A PID control algorithm will be applied to adjust the robot on the line. The proposed LFR is accomplished through the following stages: Firstly, the image is acquired using the web cam. The acquired RGB image is converted to another color coordinates for testing and comparing to choose the best color space. After that, the image contrast is enhanced using histogram equalization and then Wiener, Lee and Kuan filters are implemented to decide the best filter to be implemented. Subsequently, the basic morphological operations are carried out to choose the suitable operation to be utilized. The results are evaluated qualitatively and quantitatively from the points of Peak Signal-to-Noise Ratio (PSNR), entropy and image smoothness. The results show that the closing process is more suitable for the vision enhancement purpose, as well as the wiener filter gives the best result as regards the time and efficiency. Besides, the demonstrated LFR is capable of tracking a pre specified colored line as long as it is different from the surroundings.
© 2014 Walaa E. Elhady, Heba A. Elnemr and Gamal Selim. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.