摘要
Warehouse automation is greatly beneficial in improving a wide variety of industry. However, the prevalent automation methods apply industrial fields where systems are difficult to initialize the system and hard to recognize the system status. In this work, 3D visual-guided robot arm system with marker detection and object detection proposed.There are two main parts in this study, including the system initialization and validation using marker detection and the storage and retrieval using magazine detection. The system is composed of two cameras for the stereo system, a robot arm and computer vision algorithms to form the system for detecting, classifying and picking objects by a robot arm. Besides, magazines which can store items such as nuts and bolts and a frame which can store magazines into its grids are used. Firstly, the system is initialized by marker detection method which detect markers positions on a frame and save frame and grid positions where the robot arm can approach to store or retrieve magazines. After that, using contour detection of deep learning method [12] and Hough line transform [17], correct magazine center position in a grid can be estimated. If an impact occurs such as earthquake, warehouse system must check the status if the system can be run perfectly. This study introduces solutions which avoid the above problem.
| 獎項日期 | 2020 |
|---|---|
| 原文 | English |
| 監督員 | James Jenn-Jier Lien (Supervisor) & Shu-Mei Guo (Supervisor) |