Vision Based Multi Sensor Feedback System For Robot System With Intelligent

• Main Author: Syed Mohamad Shazali, Syed Abdul Hamid
• Format: Thesis
• Language: English; English
• Published: 2009
• Subjects: T Technology (General); TJ Mechanical engineering and machinery
• Online Access: http://eprints.utem.edu.my/id/eprint/12870/1/Vision_based_multi_sensor_feedback_system_for_robot_system_with_intelligent.pdf24_pages.pdf; http://eprints.utem.edu.my/id/eprint/12870/2/Vision_based_multi_sensor_feedback_system_for_robot_system_with_intelligent.pdf; http://eprints.utem.edu.my/id/eprint/12870/; http://library.utem.edu.my:8000/elmu/index.jsp?module=webopac-d&action=fullDisplayRetriever.jsp&szMaterialNo=0000051699
• Institution: Universiti Teknikal Malaysia Melaka

This research studies the machine vision system and how it may be integrated to assist a robot system with artificial intelligent (Al). This research focuses on building a vision based feedback system for robotics application that consists of image processor and two vision-based sensor devices. A robot manipulator controller will drive a single arm industrial robot according to the input from vision system. The feedback system also feeds the Artificial Intelligent program necessary information to make the right decision, which is based on rules of a popular game, Tic-Tac-Toe. One of the advantages of this research is that it only uses a low resolution camera and image processing software generated by the algorithms itself without additional sensors such as sonar or IR sensor. This research developed an improved technique for object recognition and space occupancies determination which not affected by the orientation of the subject. This project also implements colored object recognition technique using its color and size without edge detection process along with a self-calibration technique for detecting object location without any parameter of the camera by using only two reference points. Finally, a set of experiments to validate the proposed algorithms has been conducted. The algorithms function with success rate from 74% up to 100% and could handle the orientation of a tilted object up to 45 degrees. The result from this research may be used in manufacturing plant for a robot system equipped with machine vision and artificial intelligent.