| Summary: | 碩士 === 國立清華大學 === 動力機械工程學系 === 106 === This study uses a 7-DOF robotic arm combined with image and machine learning for grasping. Traditionally, multiple sets of equations are needed to describe the dynamics and models of the grabs, and then to find the appropriate gripping points. This system uses machine learning to find the best gripping points, eliminate the gap between mathematical simulation and practice. First, we use machine learning to identify the object and find the target. Second, the system builds the model of the centroid and posture of the target in three-dimensional space. Finally, the system uses the three-dimensional information of the target as The data to reinforcement learning and learn the best gripping points. The machine learning used in this study is divided into three parts, object identification, centroid and posture estimation, and the best grab point system. The first two parts use the Convolutional Neural Network as the learning framework, and the best grab point system uses the Deep Q Network to determine the grasping strategy. In the part of the hardware, the system uses a 6-DOF robotic arm plus 1-DOF underactuated adaptive gripper, which automatically converts parallel and angled jaws for various types of items for elastic clamping.
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