| dc.description.abstract | The thesis aims to design a shuttlecock automatic collecting and checking system. At first, the shuttlecocks on the court are detected and picked up by the AGV (Automated Guided Vehicle) and then brought back to the base. After collecting all shuttlecocks, the six degrees of freedom (6DOF) robot arm picked up each and identified its integrity.
The research topics of this thesis are described as follows: At the part of AGV collecting the shuttlecocks and through the monocular vision from the Web camera which is mounted on the AGV, we complete (1) detecting and identifying shuttlecocks, (2) calculating the relative position between the target object and the camera and (3) guiding the AGV back to the base based on the AprilTag recognition. At the part of the shuttlecocks image, based on the images from a depth camera installed at the end of the robotic arm and the webcam, we complete (1) using a deep learning technique to calculate the position and angle of the shuttlecock’s head and body center, (2) calculating the relative position between the shuttlecock and the camera, (3) measuring the integrity of the shuttlecock. In addition, the following procedures are required to be complete regarding motion control of the robotic arm. (1) build a virtual environment, (2) calculate the transformation matrix of the robot arm operation, (3) obtain the coordinates of the shuttlecock’s head, and control the robot arm to the target point of the shuttlecock head with inverse kinematics. After all, the AGV can complete shuttlecocks collection, and the robotic arm can complete shuttlecocks picking up and integrity checking.
This thesis uses the Robot Operating System (ROS) to develop a software system in the Linux environment. Through the distributed architecture of ROS and the peer-to-peer network, all information is collected, transmitted, and integrated to achieve the design of software and hardware collaboration. In the experiment of this study on the actual badminton court, the AGV can collect all the shuttlecocks on the court, and the accuracy rates of the robot arm clamping and classification are 92.5% and 83%, respectively. It is concluded that the thesis establishes the system that can pick up the shuttlecocks on the court and identify the shuttlecock’s integrity.
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