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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/120901

    题名: Visually Guided Picking Control of an Omnidirectional Mobile Manipulator Based on End-to-End Multi-Task Imitation Learning
    作者: Chi-Yi Tsai;Yung-Shan Chou;Ching-Chang Wong;Yu-Cheng Lai;Chien-Che Huang
    关键词: Omnidirectional mobile manipulator;visually guided picking control;deep learning;multi-task imitation learning;end-to-end control
    日期: 2019-12-25
    上传时间: 2021-08-03 12:11:15 (UTC+8)
    摘要: In this paper, a novel deep convolutional neural network (CNN) based high-level multi-task
    control architecture is proposed to address the visual guide-and-pick control problem of an omnidirectional
    mobile manipulator platform based on deep learning technology. The proposed mobile manipulator control
    system only uses a stereo camera as a sensing device to accomplish the visual guide-and-pick control
    task. After the stereo camera captures the stereo image of the scene, the proposed CNN-based high-level
    multi-task controller can directly predict the best motion guidance and picking action of the omnidirectional mobile manipulator by using the captured stereo image. In order to collect the training dataset, we manually controlled the mobile manipulator to navigate in an indoor environment for approaching and picking up an object-of-interest (OOI). In the meantime, we recorded all of the captured stereo images and the corresponding control commands of the robot during the manual teaching stage. In the training stage, we employed the end-to-end multi-task imitation learning technique to train the proposed CNN model by
    learning the desired motion and picking control strategies from prior expert demonstrations for visually
    guiding the mobile platform and then visually picking up the OOI. Experimental results show that the
    proposed visually guided picking control system achieves a picking success rate of about 78.2% on average.
    關聯: IEEE Access 8, p.1882-1891
    DOI: 10.1109/ACCESS.2019.2962335
    显示于类别:[電機工程學系暨研究所] 期刊論文


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