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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/35907


    Title: 全方位視覺足球機器人之自我定位系統的設計與實現
    Other Titles: Design and implementation of self-localization system for omnidirectional vision-based soccer robot
    Authors: 何丞堯;Ho, Cheng-yao
    Contributors: 淡江大學電機工程學系碩士班
    翁慶昌;Wong, Ching-chang
    Keywords: 蒙地卡羅定位;粒子濾波器;全方位視覺;Monte-Carlo Localization;Particle Filter;Omnidirectional Vision
    Date: 2009
    Issue Date: 2010-01-11 07:18:31 (UTC+8)
    Abstract: 本論文提出一種以機器人全方位視覺(omnidirectional vision)為基礎的自我定位方法,應用蒙地卡羅自我定位法(Monte-Carlo Localization)可自主推算出機器人在環境中的位置。使用蒙地卡羅自我定位法,機器人的視覺系統需要以機器人自身為中心,藉由放射狀的掃瞄方式來收集四周環境特徵,以此來建立感測模型(sensor model)。另外,以機器人的移動系統所回授的里程計資訊來建立運動模型(motion model)。機器人於行進時,交互使用運動模型與感測模型來修正機器人位置的信賴函數,並進一步得知機器人在環境中的絕對位置。將此定位方法裝置在實驗所研製的中型足球機器人上,使機器人在球場上能夠利用自身的全方位視覺系統所辨識出球場四周的標線特徵,經由比對資料庫來修正透過硬體回授資訊所預測的初步位置,實現自我定位的能力,快速得知機器人自我在球場上的絕對座標位置。
    In this thesis, the Monte-Carlo Localization (MCL) algorithm is applied to solve the self-localization problem for soccer robots which have an omnidirectional vision system. For the sensor model of MCL algorithm, an omnidirectional vision system is mounted on the center of the robot to detect color features of the environment. In the motion model, an optical encoder is utilized as the odometer sensor. The database of the color features is built for the feature scan matching by adopting the method of mixture probability distribution. Based on the motion model and the sensor model of the MCL algorithm, the robot system can locate its position in the environment by updating its position belief recursively.
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Thesis

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