English  |  正體中文  |  简体中文  |  Items with full text/Total items : 58015/91561 (63%)
Visitors : 13704971      Online Users : 47
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU 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://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/80809

    Title: Efficient Neural Network Approach of Self-Localization for Humanoid Robot
    Authors: Chang, Shih-hung;Chang, Wei-hsuan;Hsia, Chih-hsien;Ye, Fun;Chiang, Jen-shiun
    Contributors: 淡江大學資訊工程學系
    Keywords: Self-Localization;Humanoid Soccer Robot;Precision;Neural Network;Accuracy Ration
    Date: 2009-12
    Issue Date: 2013-03-07 14:00:53 (UTC+8)
    Publisher: Taipei : Institute of electrical and electronics engineers (IEEE)
    Abstract: Robot soccer game is one of the significant and interesting areas among most of the autonomous robotic researches. Following the humanoid soccer robot basic movement and strategy actions, the robot is operated in a dynamic and unpredictable contest environment and must recognize the position ofitselfin the field all the time. Therefore, the localization system of the soccer robot becomes the key technology to improve the performance. This work proposes efficient approachesfor humanoid robot and uses one landmark to accomplish the self-localization. This localization
    mechanism integrates the information from the pan/tilt motors and a single camera on the robot head together with the artificial neural network technique to adaptively adjust the humanoid robot position. The neural network approach can improve the precision of the localization. The experimental results indicate that the average accuracy ratio is 88.5% underframe rate of 15 frames per second (fps), and the average error for the distance between the actual position and the measured position ofthe object is 6.68cm.
    Relation: Proceedings of the 2009 joint conferences on pervasive computing, pp.149-154
    DOI: 10.1109/JCPC.2009.5420197
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

    Files in This Item:

    File Description SizeFormat
    Efficient Neural Network Approach of Self-Localization for Humanoid Robot.pdf全文檔7589KbAdobe PDF261View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback