The remote control robotic arm with mobile application to barcode identification and inventory of goods

碩士 === 國立高雄應用科技大學 === 機械工程系 === 105 === Nowadays, a variety of terminal retailers are around us everywhere. Every store is selling more and more goods, as consumers enjoy shopping at stores. With the rapid flow of goods, manual management and control of merchandise is confronted with more challenges...

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Bibliographic Details
Main Authors: Chen, Zhi-Ying, 陳致穎
Other Authors: Chen, Chin-Tai
Format: Others
Language:zh-TW
Published: 2017
Online Access:http://ndltd.ncl.edu.tw/handle/9bg9cr
Description
Summary:碩士 === 國立高雄應用科技大學 === 機械工程系 === 105 === Nowadays, a variety of terminal retailers are around us everywhere. Every store is selling more and more goods, as consumers enjoy shopping at stores. With the rapid flow of goods, manual management and control of merchandise is confronted with more challenges. If automation can be applied for the retailers, it is expected to solve the manual problems such as goods out of stock and being misplaced that staffs are usually unware of. In this study, we made a 6-axis robotic arm embedded with a tiny computer Raspberry Pi, which is widely used for remote control robotic arm via Bluetooth to operate the robot arm. And we used computer vision for barcode identification and inventory of goods. The system designed in this study can identify the common one-dimensional bar codes (EAN code) of goods in stores. In addition, we designed a mobile application (APP) program, which can monitor and operate the robotic arm via the mobile devices. The gripper of this robotic arm had a function of force sensing for real-time control of clamping force. With the goods packaged by different material and rigidity, the gripper can be set to give different clamping force, in order to avoid damage or deformation of them. Finally, through proper mechanical design, we made the force of the gripper uniform applied to the sensor, in order to avert the problem about inaccurate measurement of the force. The overall system can be implemented in an embedded computer to avoid using a personal computer with large volume, weight, and power consumption. Finally, some tests were performed for robotic arm movement and clamping force of gripper. From the experimental results of motion, we found that the average errors of X, Y, and Z-axis were 2.51 %, 0.15 %, and 0.28 %, respectively. With gripping, the average errors increased by 0.05 % in X-axis, 0.01 % in Y-axis, but remained the same in Z-axis. The gripper picked up a maximum weight of 250 gw, while a maximum of 70 gw was affordable as it being installed on the arm.