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系統識別號 U0026-0308201518551600
論文名稱(中文) 結合影像處理技術與模糊邏輯控制器於自主式水下載具避障之研究
論文名稱(英文) Research on Autonomous Underwater Vehicle Obstacle Avoidance by Incorporating Image Processing Techniques and Fuzzy Logic Controller
校院名稱 成功大學
系所名稱(中) 系統及船舶機電工程學系
系所名稱(英) Department of Systems and Naval Mechatronic Engineering
學年度 103
學期 2
出版年 104
研究生(中文) 陳璿帆
研究生(英文) Hsuan-Fan Chen
學號 P16021253
學位類別 碩士
語文別 中文
論文頁數 84頁
口試委員 指導教授-李建興
口試委員-方銘川
口試委員-陳永裕
口試委員-吳先晃
中文關鍵字 影像處理  模糊邏輯控制器  自主式水下載具  障礙物閃避 
英文關鍵字 Image processing  fuzzy logic controller  AUV  obstacle avoidance 
學科別分類
中文摘要 本論文應用影像處理技術搭配模糊邏輯控制器於清澈且平靜水域,進行自主式水下載具於直線定速航行途中,遇到於載具正前方靜止障礙物之閃避實驗。首先,裝置網路攝影機於水下載具前端透明殼內,用以於載具直線航行時擷取環境影像,再搭配灰階、二值化、侵蝕、膨脹、斷開、閉合與中值濾波等運算用以過濾背景與雜訊,進而擷取載具前方障礙物影像資訊。當得到水下障礙物影像資訊後,需統計障礙物像素點並將其作為模糊控制器之輸入值,經模糊邏輯控制器內部歸屬函數推算後,所得輸出值為伺服舵機閃避障礙物所需轉動的角度。本實驗以兩種型式之控制器進行測試,於Form I控制器對障礙物A作閃避實驗後,得知障礙物像素介於5k至10k間,伺服舵機的轉向角度不足而導致避障失敗。是故,將Form I控制器改良為Form II控制器,再對障礙物A作閃避實驗,並比較兩型式控制器對障礙物A之閃避結果。接著,再以Form II控制器對障礙物B作閃避實驗,並統計與整理實驗結果。有關水下載具製作部份乃是先以簡單材料製作,於測試水密與基本運動功能後,將其放大並升級內部資料處理單元而成為吾人所使用之自主式水下載具。
英文摘要 SUMMARY

This thesis incorporates image processing techniques and fuzzy logic controller for researching on an autonomous underwater vehicle (AUV) that allows static obstacle avoidance in clear and confined water conditions as the AUV moves along a straight line with a constant speed. A webcam is first installed insides a transparent acrylic hemisphere cap located in front of the AUV to capture images of the underwater environment, and an image processing algorithm consisting of grayscale, binary, dilation, erosion, opening, closing and median filter operations is utilized to exclude background and noise of the captured image in order to obtain obstacle information. After obtaining the information of obstacle in the image, the number of pixels in the image obtained through statistical analysis is used as input variables to the fuzzy controller. The fuzzy set for output variable with resulting membership function is the angle of rotation of a servo motor. This research examines two types of controllers, namely Forms I and II. After testing Form I controller in avoidance of obstacle A, it has been improved to enhance reliable obstacle avoidance as called Form II controller since Form I controller does not deliver enough the servo motor angle rotation to perform obstacle avoidance for an obstacle ranging from 5k to 10k pixels. Results of obstacle avoidance with both controllers are compared and obstacle avoidance of obstacle B using the Form II controller is then performed. As for the AUV, it was initially implemented from locally available materials and tested with waterproofing and simple navigation motions, and then was enlarged with upgrading the data processing unit.

Key word: Image processing, fuzzy logic controller, AUV, obstacle avoidance

INTRODUCTION

AUV avoidance usually uses sonar sensors to construct underwater pictures and the obstacle location in the image constructed by the side scan sonar can then be used to plan the best route. However, this thesis proposes to use image processing technology combined with cheap and readily available equipment which is to generate avoidance strategies without making a sophisticated collision system since the image processing technology combined with many algorithms has often been used to recognize obstacles in the international AUV competition. Moreover, an AUV will be able to be given more protection during the AUV’s cruise course if the proposed method can be combined with sonar sensors since they can scan large-scale and construct underwater images. To enhance underwater image quality, this thesis is a preliminary research to use a fuzzy logic controller to overcome underwater visibility. The experimental results are static obstacle avoidance in clear and confined water conditions as the AUV moves along a straight line with a constant speed.

MATERIALS AND METHODS

The experimental step in this thesis is devided into seven steps as described below:
Step1: Use image processing to convert a three-dimension color image into one-dimension grayscale image.
Step2: Use a median filter to filter noises in the background of grayscale image that obtained in step1.
Step3: Use the binary image processing and set a threshold value to separate background and obstacle.
Step4: Perform an erosion to eliminate isolated pixels in the background.
Step5: Follow step4 to use a dilation to repair damaged pixels of an obstacle and restore the initial ratio of the obstacle.
Step6: Follow the above steps and sum the obstacle pixels used as the input value of the fuzzy logic controller.
Step7: Change the obstacle pixels to linguistic variables and input to the fuzzy logic controller along with the membership functions and the fuzzy rules derived in order to obtain an output for controlling the servo motor.
RESULTS AND DISCUSSION

After dodging obstacle A to obtain the distance between AUV and obstacle A, obstacle pixels, servo motor rotation angle with controller Form I, the avoidance probability of dodging obstacle A has been increased from 75% to 90% with improving controller Form I to controller Form II. Then, controller Form II is implemented in AUV to dodge obstacle B for obtaining obstacle’s image information. Since obstacle B is larger than obstacle A (about nine times), servo motor rotation angle with controller Form II approaches the maximum value that a 100% avoidance probability of dodging obstacle B has succeeded in the course of 20 experiments. Figure 1 shows the input-output relation describing an obstacle with controllers of Form I and Form II.


Figure 1 The input-output relation describing an obstacle with controllers of Form I and Form II

CONCLUSION

Since the designed AUV did not have good balance, an external weight was placed outside the AUV. As for avoidance function of the AUV, some errors of calculating obstacle pixels may occur because light is scattered and absorbed by the unstable environment When light is transmitted in water from a subject to an observer. Thus, fuzzy control theory was chosen for the design of obstacle avoidance controllers in this thesis.
論文目次 摘要 i
Extended Abstract ii
誌謝 v
目錄 vi
表目錄 ix
圖目錄 xi
符號說明 xv
第一章 緒 論 1
1.1 研究動機與目的 1
1.2 水下載具簡介 2
1.3 水下載具之發展回顧與實際應用 3
1.4 文獻回顧 5
1.5 本論文的貢獻 7
1.6 論文架構 7
第二章 自主式水下載具機構介紹與系統架構 9
2.1 載具機構概述 9
2.2 載具機構與零組件介紹 14
2.2.1 唇型油封與O型環 14
2.2.2 載具尾段 15
2.2.3 內部支架與載具外部壓克力外管 16
2.2.4 艙間轉接座 17
2.2.5 前端蓋 19
2.3 載具系統架構 20
2.3.1 電力系統 20
2.3.2 動力系統 23
2.3.3 轉向系統 24
2.3.4 控制系統 26
2.3.5 遙控系統 30
2.3.6 沉浮系統 31
2.3.7 影像系統 31
2.4 實驗軟體介面 32
第三章 水下影像處理 34
3.1 概述 34
3.2 色彩空間 35
3.2.1 RGB色彩空間 35
3.2.2 HSL色彩空間 35
3.3 中值濾波器(Median filter) 37
3.4 影像直方圖(Histogram) 38
3.5 影像二值化(Binary) 39
3.6 影像形態學 40
3.6.1 膨脹 40
3.6.2 侵蝕 41
3.6.3 閉合與斷開運算 42
第四章 模糊邏輯控制器應用於舵機控制 44
4.1 概述 44
4.2 模糊集合與傳統集合之比較 44
4.2.1 傳統集合與特徵函數 45
4.2.2 模糊集合與歸屬函數 46
4.3 模糊邏輯控制器 47
4.3.1 模糊化 47
4.3.2 模糊知識庫 48
4.3.3 模糊推論 49
4.3.4 解模糊化(Defuzzification) 51
4.4 控制器設計結果 52
第五章 實驗結果 56
5.1 實驗環境與障礙物介紹 56
5.2 實驗步驟 58
5.3 二值化影像辨識實驗結果 60
5.4 中值濾波器應用於影像雜訊去除之實驗結果 62
5.5 各距離下障礙物影像辨識之實驗結果 62
5.6 模糊控制器避障實驗結果 67
5.6.1 障礙物A實驗結果 67
5.6.2 障礙物B實驗結果 71
5.7 避障路徑與水下航行動作之紀錄 74
第六章 結論與未來展望 78
6.1 結論 78
6.2 未來研究方向 79
參考文獻 81

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