無人搬運車之雙導引功能開發

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題名:	無人搬運車之雙導引功能開發
作者:	蔣錫沅;Chiang, Shi-Yuan
貢獻者:	電機工程學系
關鍵詞:	無人搬運車;ROS(機器人作業系統);雷射導航;室內導航與定位;深度學習;AGV;ROS (Robot Operating System);Indoor-navigation;Laser-guiding;Deep learning
日期:	2017-07-27
上傳時間:	2017-10-27 16:12:02 (UTC+8)
出版者:	國立中央大學
摘要:	本論文主要目的改良了現有廠房內的自動導引車(無人搬運車)系統，目前廠房內的自動導引車主要是循磁帶行進，功能簡單。因此增強現有導引系統並賦予以機器視覺辨識充電站及電梯之功能，再利用已建立的WIFI網路介面存取技術，讓控制中心可以即時監控車身電量及車行速度。本論文提出一個基於機器人作業系統(Robot Operating System, ROS)之實現方法，ROS為分散式的架構，並以點對點網路將所有的處理序連接在一起交換資訊。本論文在Linux環境下以ROS開發軟體系統，並整合嵌入式系統NVIDIA Jetson TX1、86Duino one，實現軟硬體協同設計。首先著重在增強現有導引技術，原磁帶導引的車輛加上雷射測距儀，建立起室內座標系統，讓自動導引車實踐室內建圖、定位與導航，並透過室內導航達成四個主要新增功能：(1)室內自主導航；(2) 閃避動態障礙物；(3)自動尋找並導航至最近磁軌點；(4)記憶tag點位置達成切換路徑。如此以磁帶及雷射互相交替使用，磁帶就可以少裝設一些，也可以不用淨空所有地面，並且依據流程自動地切換磁帶導引與雷射導引模式。另外則是以目前最新的即時物件檢測架構YOLO (You- Only-Look-Once)融合ROS架構中，以深度學習的技術成功利用影像辨識出充電站及電梯之功能。結合以上所述，成功使其運作模式更為智慧與方便。 ;The key purpose of this paper is to improve the performances of the existing automatic guided vehicle (AGV) system in the factory. Usually, the main function of the AGV only simply follows the magnetic tape in the factory. Therefore, our thought is to enhance the current guiding system and to recognize charging stations and elevators via the machine vision, after that, combining the system with the Internet so that the control center can immediately monitor the power and the speed of the AGV. In this paper, a proposed method is implemented our goals base on Robot Operating System (ROS). The architecture of ROS is a distributed system. The ROS uses peer-to-peer network to link all processes to exchange data. In the Linux environment, the AGV system is built to develop the software system by means of the ROS which combines with embedded system NVIDIA Jetson TX1, 86Duino one to achieve hardware and software co-design. Firstly, a laser rangefinder is added to the AGV to help enhance the existing guiding technologies. Then establishing an indoor coordinate system to detect the AGV position and orientation anytime, and using the indoor coordinates to carry out the following four main functions:(1) Indoor autonomous navigation;(2) Dynamic obstacle avoidance;(3) Finding the nearest magnetic point and navigation automatically;(4) Recording the position and number of RFID tags. Using laser and magnetic tape automatically alternate with each other to implement guiding mode, so the magnetic tape can be installed less, the AGV also become more intelligent and convenient. Next, we use the real-time deep learning based object detection system, namely YOLO (You-Only-Look-Once), which incorporates with the ROS to recognize the charging station and the elevator by pure image. According to the previous description, we have succeeded to make the AGV more intelligent and convenient.
顯示於類別:	[電機工程研究所] 博碩士論文

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