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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/18433

Title: 應用影像處理與模糊理論於自走車動態避障
Application of Image Process and Fuzzy Theory to Dynamic Obstacle Avoidance for an Autonomous Vehicle
Authors: Chen-Han Wu
吳承翰
Contributors: NTOU:Department of Communications Navigation and Control Engineering
國立臺灣海洋大學:通訊與導航工程系
Keywords: 影像處理;自走車;動態避障;模糊控制系統
image process;WMR;dynamic obstacle avoidance;fuzzy control system
Date: 2010
Issue Date: 2011-07-04
Abstract: 本篇論文主要研究自走車動態避障控制,結合CCD 攝影機和室 內定位系統所提供之資訊作為模糊控制器的輸入,以這兩種感測器為 基礎,加上經過模糊控制器運算的輸出,不需要非常複雜的數學方程 式,就可以設計出一個不錯的動態避障控制器。本論文第一部分是利 用室內定位系統所給予的座標和角度,使自走車行進在預設路線上, 再以模糊理論設計模糊控制法則,使自走車經過動態避障後能及時自 我修正回預設路徑上。第二部分主要是藉由CCD 攝影機捕捉的影 像,經過影像處理去擷取出動態障礙物的座標資料,並且計算出動態 障礙物距離自走車的距離,以及判斷動態障礙物的移動方向角度,再 以模糊法則進行動態避障控制。硬體部分使用LabView 8.5 來撰寫人 機介面,將感測器資料傳回電腦運算,產生輸出轉速,藉由WiFi (802.11b)無線網路傳遞運算數值及自走車回授信號,影像處理用C 語 言編寫程式並結合LabView 8.5 來控制自走車。經由實驗證實,本論 文所設計的動態避障控制器運行良好。
This thesis presents dynamic obstacle avoidance control of a wheeled mobile robot (WMR). A CCD camera and a localization system are integrated in the control scheme, which provide the inputs of the fuzzy controller. Based on these sensors and output operation of the fuzzy controller, an adequate dynamic obstacle avoidance controller can be designed without complex mathematical equation. The first part of this thesis is the use of localization system coordinates and angles to make the WMR move on the default path, then design fuzzy control rules by fuzzy theory, so that after the WMR avoiding dynamic obstacle, it can be self-corrected and return to default path promptly. The second part is the use of CCD camera to capture images and through image processing to obtain the coordinate data from the image of a dynamic obstacle. The distance between dynamic obstacle and the WMR, as well as the direction of movement of the moving obstacle can be calculated. Fuzzy rules are applied to realize dynamic obstacle avoidance control. Hardware implementation uses LABVIEW 8.5 to realize interface between human and machine. Command signals are transferred through WiFi (802.11b) wireless to communicate data between the WMR and the computer. Image processing is handled by C code and then is integrated by LABVIEW 8.5 to control the WMR. The experiments confirm that the proposed dynamic obstacle avoidance control scheme works properly.
URI: http://ethesys.lib.ntou.edu.tw/cdrfb3/record/#G0M97670007
http://ntour.ntou.edu.tw/ir/handle/987654321/18433
Appears in Collections:[通訊與導航工程學系] 博碩士論文

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