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研究生： 柯昆廷
CORREA, Quentin
論文名稱： 四旋翼機影像伺服控制之測試平台研究與開發
Testbed Development and Simulation Study of a Visually Controlled Quadrotor
指導教授： 陳介力
Chen, C.L.
學位類別： 碩士
Master
系所名稱： 工學院 - 航空太空工程學系
Department of Aeronautics & Astronautics
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 86
中文關鍵詞： 四旋翼機 、影像處理 、OpenCV 、SURF 、測試平台 、XY工作平台
外文關鍵詞： Quadrotor, Image processing, OpenCV, SURF, Testbed, XY table
相關次數： 點閱：102  下載：6 
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    • 在過去十年中，隨著機器視覺和影像處理技術的蓬勃發展，其相關技術也逐漸應用至工業製造和無人載具上。如今大多數之四旋翼機皆有裝載無線機載攝影機，除了用以提供飛行中之航拍照片之外，也可透過其影像以獲得四旋翼機當前之航行方位與位置資訊。
    本研究將透過Arduino訊號處理板處理回授之影像，實現四旋翼機之自主飛行。主要工作為利用一XY工作平台模擬四旋翼機之飛行，藉以驗證本文之四旋翼機影像導航系統之可行性。而此處使用之XY工作平台是以兩顆馬達分別帶動攝影機進行X、Y方向之移動，藉此模擬四旋翼機以定姿態進行平面運動。在數據傳輸方面，本文亦提出一款全新之人機界面，令使用者得以即時觀察各項感測器(sensor)之數據與無線攝影機回傳之影像，其操作模式可分為下列兩種：第一種模式下，使用者可直接以滑鼠於攝影機回傳之影像上指定飛行目的地，接著影像導航系統便可自動演算出對應之導航指令，並以無線通訊方式回傳指令至XY工作平台，將攝影機移動至目標點之上空，即目標點影像坐落於攝影機回傳圖像之中心，完成導航。第二種模式下，使用者則可指定一系列之目標點，使四旋翼機能沿指定之路徑飛行至最終目的地。在此模式下，本系統以K-鄰近點(K-nearest neighbors)演算法實現高速強健性影像處理(Speed-Up Robust Features algorithm)，使其能快速、準確的於即時影像中辨識與目標物匹配之特徵點。實驗結果顯示，本文之影像處理導航系統皆能快速、準確的將四旋翼機導航至目的地，驗證本研究之可行性。

    Machine vision and image processing receive more and more attention and are applied to the Unmanned Aerial Vehicles research and industry in the last decade. Indeed, most of the unmanned quadrotors are equipped with an onboard camera, which provides beautiful aerial pictures as well as the information about the drone orientation and positioning.
    This study is the first step towards an Arduino-based quadrotor to perform autonomous flight by vision feedback. The work is a prequel to the actual flight of the drone, regarding that the image processing for navigation is tested on a XY table. The XY table acts as a testbed for 2 dimensional flight of a static attitude. A Human-Machine Interface is designed to supervise the data from the sensors and visual image from the wireless camera. Two operation modes are studied. The first mode allows the user to assign a destination by clicking the target on the image frame received. The navigation command then can be determined to guide the drone to that destination point, where the destination appears on the image frame center. The second mode permits to reach coordinates specified by the user by targeting sequential points. The image matching processing necessary to recognize the same point in real time image frames is based on the Speed-Up Robust Features algorithm which gives accurate and rapid results with K-nearest neighbors matcher. The efficiency of the image processing algorithm and navigation strategy is demonstrated by the testbed.

    ABSTRACT I 中文摘要 II ACKNOWLEDGEMENTS III CONTENTS IV LIST OF TABLES IX LIST OF FIGURES X NOMENCLATURE AND ACRONYMS XIII Chapter 1. Introduction 1 1.1 General Introduction on quadrotors 1 1.2 Previous and related studies 3 1.2.1 Early history of quadrotors 3 1.2.2 Modern quadrotors 5 1.2.2.1 Control 5 1.2.2.2 Communication 8 1.2.2.3 Size 10 1.2.3 Quadrotors using image feedback 11 1.2.3.1 Control of a quadrotor using dual visual feedback 12 1.2.3.2 Vision-Based Autonomous Mapping and Exploration 12 1.3 Description of the software used 14 1.3.1 Arduino Integrated Development Environment (IDE) 14 1.3.2 MFC library of Visual Basics 2010 15 1.3.3 OpenCV 16 1.4 General overview of the project 18 1.4.1 Autonomous flight assisted by image feedback 18 1.4.2 Architecture of the system 21 1.4.2.1 The quadrotor 21 1.4.2.2 The image processing testbed 22 Chapter 2. Integration of the sensors 24 2.1 The HC-SR04 ultrasonic altimeter 24 2.1.1 Description 24 2.1.2 Operating principle 25 2.2 The Inertial Measurement Unit GY-81 26 2.2.1 Description 26 2.2.2 Utilization of the sensors 27 2.2.2.1 Magnetometers 27 2.2.2.2 Gyroscopes 27 2.2.2.3 Accelerometers 28 2.2.3 I²C Communication 28 2.2.4 Direction Cosine Matrix 30 2.2.5 Results 32 2.3 Control of the quadrotor 34 2.3.1 Preliminary work 34 2.3.2 Sliding Mode Control (SMC) 35 2.3.3 Results 35 Chapter 3. communication modules 37 3.1 Manual and Transparent piloting 37 3.1.1 The WFLY WFT06II 37 3.1.2 ESC calibration 39 3.2 Toward automatic piloting 39 3.2.1 The RF modules xBee-Pro 900 39 3.2.2 Communication protocol 41 3.2.3 Emission-Reception matters 44 3.2.4 Results 46 Chapter 4. image processing 48 4.1 Objectives 48 4.1.1 Click and Track Mode 48 4.1.2 Coordinates Reaching Mode 49 4.2 Processing the image step by step 50 4.2.1 Define the Region of Interest 50 4.2.2 Calculation of the ground distance 51 4.2.3 Speeded-Up Robust Features algorithm 52 4.2.4 Kth Nearest Neighbors (KNN) matcher 54 4.2.5 Homography 55 4.3 Testing the algorithms on the XY Table 56 4.3.1 Preparing the testbed 56 4.3.2 Tuning the parameters 57 4.3.3 Results 58 Chapter 5. Human-machine interface 63 5.1 Motors testing panel 63 5.1.1 Testing the quadrotor 63 5.1.2 Commanding the motors 64 5.2 Data exchange panels 65 5.2.1 Serial Box 65 5.2.2 Quadrotor’s state display 66 5.2.3 Vertical flight commands 66 5.2.4 Dialog Box 67 5.3 Image Processing Interface 68 5.3.1 Selection of the processing mode 68 5.3.2 Frame and ROI Displays 69 5.3.3 Information on the image processing 71 Chapter 6. Conclusion and recommendations for future work 72 6.1 Advancement of the project 72 6.2 Future work 74 Appendix 76 References 85

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