English  |  正體中文  |  简体中文  |  Items with full text/Total items : 78111/78111 (100%)
Visitors : 30593577      Online Users : 357
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version

    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/90733

    Title: 整合深度學習與立體視覺之六軸機械手臂夾取系統開發;Development of a six-axis robotic arm gripping system integrating deep learning and stereo vision
    Authors: 陳顥壬;Chen, Hao-Ren
    Contributors: 光機電工程研究所
    Keywords: 夾取姿態估計;目標檢測;深度學習;六軸機械手臂;ROS;運動學;座標轉換;Grasp pose estimation;Object detection;Deep learning;6 DoF robotic arm;ROS;Kinematics;Coordinate transformation
    Date: 2023-02-01
    Issue Date: 2023-05-09 17:42:09 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 本研究使用Linux中的Ubuntu18.04版本作業系統的環境下透過機器人作業系統(Robot Operating System, ROS)開發軟體系統,經由ROS點對點網路與其分散式架構將所有資訊進行資料傳遞並整合工業用IPC、六軸機械手臂、Ensenso深度相機以及自適應夾爪,實現軟、硬體協同的設計。

    本論文的任務目標在於藉由導入影像視覺系統,開發一種不需要使用CAD模型匹配的辨識夾取策略,其中整合了二維與三維點雲資料,透過深度學習網路檢測目標物以及對目標物點雲進行最佳六自由度(6 Degrees of Freedom, 6DoF)夾取姿態估計,並在機構限制下利用逆向運動學控制六軸機械手臂對四種不同的目標物在工作區域內隨意平放的情況下執行夾取與分類任務,夾取與分類任務的成功率為86%,成果顯示本論文確實能成功建立一套物件辨識與夾取分類系統。;Robot operating system (ROS) is used to develop a software system under the Ubuntu 18.04 version of Linux environment in this study. The industrial IPC, the robot arm, the binocular structured light camera and the grippers are integrated by ROS distributed architecture and peer-to-peer network, and all information and data collected can be transferred to them as well. Therefore, the collaborative design is used to realize the integrated software and hardware.

    The main purpose of this paper is to develop a recognition and gripping strategy that does not require CAD model matching through the introduction of the depth image vision system, to identify the target through the neural network and automatically generate a six-degree-offreedom (6DoF) gripping pose for the object, and the success rate of gripping and sorting is 86% under the condition that the six-axis robot arm is controlled to place four different objects in the working area at will under the limitation of the mechanism. The results show that this paper can indeed successfully establish a set of object recognition and gripping system.
    Appears in Collections:[光機電工程研究所 ] 博碩士論文

    Files in This Item:

    File Description SizeFormat

    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 隱私權政策聲明