| dc.description.abstract | This thesis aims to design a robotic arm-grabbing system for recyclables classification. The unknown recyclables in the image will be identified by RGB-D camera and deep learning network, and the six-axis robotic arm will be controlled to grab the recyclables to the specified boxes. The considered recyclables are plastic, glass, paper container, metal container, and styrofoam.
This thesis focuses on utilizing the output images from an RGB-D camera mounted at the end of a robotic arm to achieve several objectives. They are as follows. (1) A deep learning instance segmentation network is employed to segment and classify objects in the captured images effectively. (2) Image processing techniques are applied to enhance the depth information of transparent things, such as plastic and glass, which may have incomplete depth measurements. (3) The instance segmentation results are utilized to extract the contours of each identified object and then we can enable to calculate their respective areas and centers. (4) Using the camera′s internal parameters, the depth information is transformed into a point cloud. (5) Input the point cloud information into the deep learning grasp detection (grasp detection) network and output the grasping parameters. (6) Convert the grasping parameters into the actual grasping position and angle and then perform center screening. In addition, for the process of the six-axis robot arm picking up the target object, the following tasks are completed. (1) Using forward kinematics to set up different initial points and end points for the robot arm operation. (2) Establishing a virtual environment to prevent collision happening during robot movement. (3) Using inverse kinematics to calculate the rotation angle of each axis when the center of the end of the robot arm reaches the grab point. (4) Setting the joint angle constraints to avoid a major shift at the end of the robot. (5) Calculating the transformation matrix of grasp detection network under the camera frame for the robot arm to reach the grasping point. After the above tasks completed, it is possible to use inverse kinematics to control the robotic arm to grab the target object under the limitation of vision range and the constraints of the mechanism.
This thesis uses the Robot Operating System (Robot Operating System, ROS) to develop the software system in the Linux environment. All information is collected, transmitted, and integrated through the distributed architecture of ROS and the peer-to-peer network to achieve software and hardware collaboration. | en_US |