Design of Deep Learning-based Visually Guided Picking Control of Omnidirectional Mobile Manipulators

• Main Authors: Chien-Che Huang, 黃建哲
• Other Authors: Chi-Yi Tsai
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/c54669

碩士 === 淡江大學 === 電機工程學系碩士班 === 106 === This thesis presents a novel neural network design for the application of visual guidance and picking control of an omnidirectional mobile manipulator platform through deep learning. In the experimental setting, a stereo camera was used to capture the front area of the mobile platform. Next, the proposed neural network used the stereo image as input to determine the best motion behavior of the platform. This procedure was performed recursively until the mobile platform arrives in front of the target. Finally, the proposed neural network calculated a six-degree-of-freedom (6-DoF) picking control command to the 6-DoF manipulator to pick up the target. We divide the proposed system into two sub-systems, one is the omnidirectional mobile platform control system, and the other one is the manipulator control system. We trained two convolutional neural networks (CNNs) separately and used them to control the omnidirectional mobile platform and the 6-DoF manipulator, respectively. A training database that contains stereo images of the scene and the corresponding control commands was manually recorded. Through a supervised imitation learning, the proposed two control systems learn the best motion and picking control strategies to visually guide the platform and to visually pick up the target, respectively. Experimental results the proposed visually guidance control system achieves a picking success rate about 78% in average.