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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/118139


    Title: Visually Guided Picking Control of an Omindirectional Mobile Manipulator Based on End-to-End Imitation Learning
    Authors: Tsai, Chi-Yi;Huang, Chien-Che;Lai, Yu-Cheng;Chou, Yung-Shan;Wong, Ching-Chang
    Keywords: Omnidirectional mobile manipulator;visually guided picking control;deep learning, multi-task imitation learning;end-to-end control
    Date: 2020-01-06
    Issue Date: 2020-02-25 12:11:00 (UTC+8)
    Publisher: IEEE
    Abstract: 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.
    Relation: IEEE Access 8, p.1882-1891
    DOI: 10.1109/ACCESS.2019.2962335
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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