Recycling of Circuit Boards by Robot Manipulator Using Stereo Vision and Deep Learning
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formatting, style, stylingAbstract
To make possible the recycling of printed circuit boards, automated systems for object classification and degree of overlapping are needed. In this thesis, a recycling system with a robot manipulator was developed by using Deep learning and a stereo vision approach. The system operates in the following order: object detection by deep learning, calculation of 3D point clouds by stereo vision, and grasping by the robot manipulator. Four experiments were conducted to evaluate the developed recycling system. The four experiments were: measurement of object detection accuracy, measurement of stereo vision responsiveness, measurement of vertical judgment accuracy and grasping accuracy, and operation test of the actual machine. Deep learning and stereo vision for the robot manipulator were found to be effective for the printed circuit board recycling system. The results also shed light on the challenges of automating the recycling process.
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