Integration Of Machine Vision In Industrial Robot For Automated Composite Panel Punching - Mohamed Hanafee, Ameer Mohamed Abdeel Aziz (2018)
This thesis employs visual servoing control to provide accurate information for repetitive task on the known object. The problem statement on this thesis involves two main points. Firstly, by using the conventional method it is not possible to have one workstation to machine different pattern of composite panels, nor using different fixtures and tools at the same time. Secondly, the current KUKA Robot Controller (KRC) 4 does not have the capability to communicate with an external device due to the controller safety protocol. The aim of this research is to build a machine vision algorithm for assisting KUKA robot in recognizing and calibrating various shapes of composite panels precisely. The second objective is to develop and integrate communication interface program for KRC4 and Raspberry Pi (RPi) microcontroller and to evaluate the performance of visual servoing control system for automated composite panel punching. By using socket module for Python programming language, the feedback control system is built by communicating RPi with KRC4 through Ethernet using KUKA Robot Language (KRL) to enable the robot to execute the whole process. The image are traced using the machine vision function. Shape factor value of each four different panels sorted through the graph for 15 process cycle. Position and rotation of panels are compared to three coordinate systems which are camera, workpiece and machining center. The differences of these coordinates with the reference coordinate translated as error and adjustment was made to remove the error. As a conclusion, KUKA robot integration with RPi enables the machining process of different panels executed in a workstation only.