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研究生：	張銘祥 Chang, Ming-Shaung
論文名稱：	具友善人機介面之智慧型戶外清潔機器人 Intelligent Outdoor Clean Robot with Friendly Human-Robot Interface System
指導教授：	周榮華 Chou, Jung-Hua
學位類別：	博士 Doctor
系所名稱：	工學院 - 工程科學系 Department of Engineering Science
論文出版年：	2010
畢業學年度：	98
語文別：	英文
論文頁數：	110
中文關鍵詞：	戶外清潔機器人、單影像三維測距、全域最短路徑、人機介面系統、動態手勢辨識
外文關鍵詞：	Outdoor clean robot, Single image 3D measurement model, Global-path planning, Human-robot interface system, Hand gesture recognition
相關次數：	點閱：214 下載：9
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本論文研發一套具友善人機介面之智慧型戶外清潔機器人系統，包含在戶外環境下工作的清潔機器人設計與實現，和在未知環境下基於Floyd-A*演算法、平滑化全域最短路徑規劃與其三維工作環境地圖重建之自動導航系統研發，以及基於使用者動態連續手勢指令之遠端友善人機介面設計。
為克服各種未知戶外清潔環境的干擾，例如崎嶇地形、落葉偵測或是天候變化等等因素，吾人首先使用履帶驅動設計來取代一般輪式驅動，賦與清潔機器人自主行走於不平路面之能力；而於工作環境之清潔區域偵測，則利用影像-超音波感測模組完成，其中演算流程包括外部光源補償、清潔區域之高斯顏色濾波與K-Means分類器去除背景、感測資料模糊化融合與二維清潔環境地圖建立。最後以特製清潔機構模組，在牛耕田全域式封閉路徑上清掃落葉，並依道路邊緣或落葉集中人工標記做機器人自我定位與垃圾傾倒。
另一方面，為求得機器人在未知環境下起點-終點之全域最短路徑，系統先使用單影像三維測距模型計算場景中障礙物的實際位置，並依此結果建立三維工作環境地圖。在感測範圍內以Floyd-Warshall 演算法求得起點到區域子目標之最短子路徑，再依其路徑節點外切圓演算法平滑此路徑，最後以A*演算法計算適當之區域子目標與全域最短平滑路徑。
此外，為提供遠端使用者友善的智慧型人機介面系統，全系統以使用者頭部位置與動態手勢辨識命令控制此清潔機器人。吾人先使用包含橢圓樣版、臉部特徵與主成分分析法相似度等三種方式偵測人臉及fuzzy-Kalman方法追蹤人臉，並作手部偵測與類神經-主成分分析法辨識，再依手指指示方向資訊和動態規劃法求得之連續指令控制整個清潔機器人系統。
本研究以模擬及實際環境下之實驗驗證所提方法之通用性，結果證實所開發之系統快速且準確地達成其目標。

In this dissertation, an intelligent outdoor-clean robot system with friendly human-robot interface (HRI) is developed. The system consists of three main topics, including the design of unmanned clean robots, the search of optimal global-path based on Floyd-A* algorithm in the unknown environments as well as 3D map-building from 2D scene images, and the implementation of the friendly HRI.
Due to the internal and external disturbances in various unknown outdoor-environments, e.g. rough terrain, leaf category or varying weather, novel designs are implemented in this dissertation to overcome the above-mentioned issues. Firstly, the track motion mechanism is adopted instead of wheels. Secondly, both image and ultrasonic sensors are used to detect cleaning-regions in the working environment. Algorithms such as white balance for varying light source, Gaussian color-filter and K-means clustering method for background segmentation, and fuzzy data-fusion as well as 2D cleaning-map generation are used in this procedure. Thirdly, the robot cleans these leaves in a closed cleaning region with a special cleaning-brush mechanism based on boustrophedon path planning. Finally, the robot localizes and damps trash autonomously by road-edge detection and garbage-collection mark recognition, respectively.
Besides, we also investigate the fast and safe global-path planning algorithms from the starting point to a certain goal in the unknown environments for robot navigation, by using only a single image 3D measurement model to orientate the 3D obstacles in the 2D images. A 3D working-map in the unknown environment is also built accordingly. The shortest sub-path candidates in the sensing range are computed and smoothed based on Floyd Algorithm and excircle calculation of path-nodes, respectively. The optimal global-path to the goal in the unknown environment is then determined by the A* Algorithm.
Furthermore, we establish a friendly and intelligent HRI system for the distant users, based only on their facial positions and natural dynamic gestures to control the robot. Face detection methods, including ellipse shape, facial feature and PCA similarity detection, and fuzzy-Kalman face tracking, are implemented first. Then after dynamic programming, the real-time hand gesture recognition via the PCA-BPANN classifier and finger-pointing detection helps users instruct the robot to perform its tasks.
Both simulations and experiments in the real environments are conducted to prove the feasibility of this dissertation. The results show that our clean robot with the proposed HRI performs fast and accurately.

Contents
Abstract (English)……	I
Abstract (Chinese)..	III
Acknowledgement (Chinese)……………….	IV
Contents…	V
List of Tables	VIII
List of Figures	VIII
Chapter 1 Introduction	1
1.1 Motivation	1
1.2 Literature Review	2
1.3 Key Contributions of this Study	5
1.4 Dissertation Organization	7
Chapter 2 Outdoor Clean Robot Design Ι	9
2.1 System Skeleton	9
2.1.1 Hardware Framework	9
2.1.2 Cleaning Flowchart	10
2.2 Sensor Modules and Data Fusion	10
2.2.1 Ultrasonic Measurement Module	10
2.2.2 Single Image Measurement Model	11
2.2.3 Power Monitor Module	12
2.3 Clean-region Detection	13
2.4 Boustrophedon Complete Coverage Path-planning and Self-localization	15
2.4.1 2D Image to Grid-cell Map	16
2.4.2 Boustrophedon Complete Coverage Path Planning	16
2.4.3 Self-localization	16
2.5 Results and Discussions	17
Chapter 3 Outdoor Clean Robot Design ΙΙ	19
3.1 System Skeleton	19
3.1.1 Hardware Framework	19
3.1.2 Cleaning Flowchart	20
3.2 Dual Sensor Module and Cleaning Region Detection	21
3.2.1 Dual Sensor Module	21
3.2.2 Cleaning-region Detection	22
3.3 Data Fusion Based on Fuzzy Reliability	23
3.3.1 Multi-measuring Mode	23
3.3.2 Fuzzy Reliability	24
3.4 Autonomous Trash-Dumping and Self-localization	25
3.4.1 Mark Detection	25
3.4.2 Mark Search and Robot Self-localization	26
3.5 Results and Discussions	26
Chapter 4 Machine Vision-based Mobile Robot Navigation System in an Unknown Environment………..	28
4.1 System Skeleton	28
4.1.1 System Equipment	28
4.1.2 System Flowchart	29
4.2 Machine-vision Measurement, Object Detection and 3D Map-building	29
4.2.1 Machine Vision-based 3D Measurement	29
4.2.2 Object Detection	30
4.2.3 3D Map-building	31
4.3 Global Path Planning for Smoothing and Robot Localization	32
4.3.1 A* Path Planning Based on Floyd Path in an Unknown Environment 	32
4.3.2 Path Smoothing	33
4.3.3 Robot Localization	35
4.4 Results and Discussions	36
Chapter 5 Human-Robot Interface System Based on Natural Human Gestures	38
5.1 System Skeleton	38
5.1.1 Image Pre-processing	39
5.1.2 Human Detection	40
5.1.3 Human Tracking	41
5.2 Human Gesture Detection	43
5.2.1 Hand Detection	43
5.2.2 Finger-pointing Direction Measurement	43
5.3 Dynamic Command Control of the HRI System	45
5.3.1 Combination of PCA and BPANN	45
5.3.2 Single Command	46
5.3.3 Successive Command	47
5.4 Results and Discussions……………………………………………………………………..	47
Chapter 6 Conclusions and Future Suggestions	49
6.1 Conclusions	49
6.2 Future Suggestions	50
References	………………………………………………………………………………51
Tables	………………………………………………………………………………………61
Figures	………………………………………………………………………………….63

 List of Tables
Table 2.1	Fuzzy rule table of dual sensor module…………………………………….	61
Table 3.1	Symbol definition…………………………………………………………...	61
Table 4.1	Path planning simulation results……………………………………………	61
Table 5.1	Facial geometric relationship rules.………………………………………...	62
Table 5.2	Fuzzy rule table of face tracking……………………………………………	62
Table 5.3	Command database…………………………………………………………	62

List of Figures
Figure 1.1	An intelligent outdoor clean robot system with friendly HRI……….…….	 63
Figure 2.1	The outdoor clean robot, SI-lab AW Robot Ι..…………………………….	 64
Figure 2.2	Cleaning mechanism of SI-lab AW Robot Ι………………………………	 64
Figure 2.3	Cleaning task flowchart of SI-lab AW Robot Ι……………………………	 65
Figure 2.4	Polaroid 6500 sonar ranging module…………………………………….	 65
Figure 2.5	Experimental result of ultrasonic module within 110 cm…………………….	 66
Figure 2.6	Single image measurement model…………………………………………	 66
Figure 2.7	Experimental result of single image measurement model…………………	 67
Figure 2.8	Characteristic function of supply batteries…………………………………	 67
Figure 2.9	Clean-region detection for cleaning task of SI-lab AW Robot Ι……………	 68
Figure 2.10	4-direction search rule………………………………………………………	 69
Figure 2.11	Results of boustrophedon path planning…………………………………...	 69
Figure 2.12	Line detection procedure and self-localization…………………………………	 70
Figure 2.13	Boundary line for self-localization of a scene with a curb…………………	 71
Figure 2.14	Boundary line for self-localization of a scene without any wall……………	 71
Figure 2.15	Experimental results of clean-region detection……………………………..	 72
Figure 2.16	Results of boustrophedon path planning……………………………………	 72
Figure 2.17	Experimental results of practical outdoor cleaning work……………………..	 73
Figure 3.1   	Hardware framework of SI-lab AW Robot ΙI…………………….…………	 74
Figure 3.2	Function architecture of SI-lab AW Robot ΙI ………………………………	 74
Figure 3.3	Track-driven module of SI-lab AW Robot ΙI………………………………	 75
Figure 3.4	PID controller with track-driven module…………………………………..	 75
Figure 3.5	Cleaning mechanism of SI-lab AW Robot ΙI……………………………….	 75
Figure 3.6	Switch of the gate of the trash box……………………………………………….	 76
Figure 3.7	System flowchart of SI-lab AW Robot ΙI…………………………………...	 77
Figure 3.8	Performance of dual sensor module……………………………………...	 78
Figure 3.9	Image measuring results from 100 cm to 500 cm…………………………..	 78
Figure 3.10	Sonar measuring results from 40 cm to 350 cm…………………………….	 78
Figure 3.11	2D map-building for cleaning-region polygon…………………………………	 79
Figure 3.12	Cleaning-region detection of SI-lab AW Robot ΙI………………………….	 79
Figure 3.13	Multi-measuring mode…………………………………………………………….	 80
Figure 3.14	Fuzzy membership function of dual sensor module………………………..	 80
Figure 3.15	Example of multi-measuring mode and Fuzzy reliability………………......	 81
Figure 3.16	Autonomous trash-dumping flowchart……………………………………..	 82
Figure 3.17	Trash-dumping mark………………………………………………………..	 82
Figure 3.18	Mark detection………………………………..………………………………...	 82
Figure 3.19	Cleaning-region detection of SI-lab AW Robot ΙI………………………….	 83
Figure 3.20	Cleaning region detection in the outdoor sandlot environment…………….	 84
Figure 3.21	Boustrophedon complete coverage path planning for Fig. 3.20……………	 84
Figure 3.22	Outdoor cleaning in the sandlot environment………………………………	 85
Figure 3.23	Outdoor cleaning in the lawn environment…………………………………	 86
Figure 4.1	System flowchart of robot auto-navigation…………………….…………...	 87
Figure 4.2	3D measurement model from a single image ………………………………	 88
Figure 4.3	Reference white……………………………………………………………..	 89
Figure 4.4	Edge enhancement and area filtering……………………………………….	 89
Figure 4.5	3D measurement and 3D map-building…………………………………….	 90
Figure 4.6	A* path planning based on Floyd algorithm……………………………………	 91
Figure 4.7	Path smoothing……………………………………………………………..	 92
Figure 4.8	Tangent line restriction……………………………………………………..	 92
Figure 4.9	Path planning simulation……………………………………………………	 93
Figure 4.10	PID motor controller………………………………………………………..	 93
Figure 4.11	Path planning simulations compared with other methods…………………….	 94-95
Figure 4.12	Practice navigation result of case 1…………………………………………	 96
Figure 4.13	Practice navigation result of case 2………………………………………………	 97
Figure 4.14	Simulation navigation result of case 1……………………………………..	 98
Figure 4.15	Simulation navigation result of case 2……………………………………..	 98
Figure 4.16	3D map-building for practical navigation result 1………………………….	 99
Figure 4.17	3D map-building for practical navigation result 2………………………….	 99
Figure 5.1	Function framework of HRI system…………………….………………….	 100
Figure 5.2	System flowchart of HRI system …………………………………………..	 101
Figure 5.3	Skin-color filter……………………………………………………………..	 102
Figure 5.4	Simply varying ellipse template…………………………………………….	 102
Figure 5.5	Fuzzy membership function of face tracking……………………………….	 103
Figure 5.6	Hand segmentation…………………………………………………………………	 103
Figure 5.7	Templates of hand gestures…………………………………………………	 104
Figure 5.8	Finger-pointing direction definition………………………………………...	 104
Figure 5.9	Shoulder detection………………………………………………………….	 105
Figure 5.10	Finger-tip detection…………………………………………………………	 105
Figure 5.11	Finger-pointing direction measurement……………………………………..	 106
Figure 5.12	Dynamic successive command recognition………………………………...	 107
Figure 5.13	Gesture command recognition on remote PC…………………………….	 108
Figure 5.14	Image process on the clean robot…………………………………………...	 109
Figure 5.15	Practical actions on the clean robot…………………………………………	 110
 

                                    

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