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研究生: 吳宗瀚
Zong-Han Wu
論文名稱: 空中機器人之軌跡追蹤系統
Trajectory Tracking System in Aerial Mobile Robot
指導教授: 李敏凡
Min-Fan Ricky Lee
口試委員: 蔡明忠
Ming-Jong Tsai
鄒杰烔
Zou, Jie-Tong
石大明
none
學位類別: 碩士
Master
系所名稱: 工程學院 - 自動化及控制研究所
Graduate Institute of Automation and Control
論文出版年: 2015
畢業學年度: 103
語文別: 英文
論文頁數: 102
中文關鍵詞: 定位系統模糊邏輯控制自主控制軌跡追蹤無人飛行載具
外文關鍵詞: Odometry system, fuzzy logic control, Autonomous control, Trajectory tracking, Unmanned aerial vehicle
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  • 多旋翼無人機(Multicopter)開始普及化且受到高度的關注,大多數的研究應用在環境 檢測、災區監控、空中拍攝。大多數的多旋翼無人機都還是遠端遙控並未達到一個自主 無人飛行的情況。因此限制了多旋翼無人機的任務,當它處於視距外時人很有可能會誤 判情況,為了改善這個狀況本論文採用自主控制讓多旋翼無人機可以依照環境的不同來 判別及控制。本論文集中在多旋翼無人機軌跡追蹤的自主控制系統,該系統由兩個子系 統所組成,分別是「自主控制系統」與「定位系統」所組成。
    本論文定位系統採用航位推測法,運用航位推測法推算多旋翼無人機所在之位置,使定位系統能夠運用在室內或室外不受到環境以及感測器的限制。此外,本論文的自主控制系統採用了模糊控制器,經由模糊控制器的強健性以及較高容錯性的特性使本文的控制系統能夠使用在各種環境下。
    在本論文中,在定位系統的實驗結果顯示了使用航位推測法定位它的誤差範圍室內誤差在0.2~0.3m 之間,室外誤差在0.7~0.8m 之間,從實驗結果證明了航位推測法較不會受到外界的干擾相較於影像系統且資料傳遞的速度遠高於GPS 和AIS。在自主控制系統的實驗結果顯示在室內的誤差範圍在0.4~0.5m 之間,在室外的誤差範圍在0.6~0.7m之間,透過實驗驗證此系統可以在不確定的環境下為成了任務。


    Multicopters are beginning to gain popularity and appeal to many people. Most published research applies to environmental monitoring, search and rescue, and delivery, but most multicopters use remote controls and lack autonomous control. As a result, multicopters are restricted by visual range. In an attempt to improve this condition, the author adopted the use of intelligent controllers to allow for control and adaptation in unknown environments. This thesis focuses on autonomous trajectory tracking for aerial mobile robots. This system consists of two subsystems to be classified into the autonomous control system and Odometry system.
    This thesis proposes an Odometry system that uses a Dead reckoning (DR) algorithm. The author applies DR to locate the multicopter position so that the Odometry system isn’t restricted by the environment. Furthermore, this thesis adopts the fuzzy logic controller for autonomous control system. Fuzzy is a robust autonomous control system with good fault tolerance enabling it to work in any environment.
    Our experiment result shows that the DR for Odometry estimate system had an error range between 0.2 ~ 0.3 meter indoors and error range between 0.7 ~ 0.8 meter outdoors. The result also demonstrates that the DR is more robust than the visual sensor and its output rate if faster than the GPS and AIS. The experimental result of the autonomous control system shows that its trajectory tracking error ranges between 0.4 ~ 0.5 meters indoors and between 0.6 ~ 0.7 meters outdoors.

    ABSTRACT 中文摘要 致謝 Table of Contents List of Figures Chapter 1 Introduction 1.1 Background and Motivation 1.2 Literature Review 1.3 Purpose 1.4 Contribution 1.5 Organization Chapter 2 Analysis 2.1 System overview 2.2 Survey of Multicopter platform 2.3 Multicopter System Overview 2.3.1 Hardware and Firmware of Multicopter 2.3.2 Dynamic Model of “X-type” Quadcopter 2.3.3 PID Controller 2.4 Odometry system 2.4.1 Visual servo 2.4.2 Motion capture system 2.4.3 Inertial measurement unit 2.4.4 Global positioning system 2.4.5 Automatic identification system 2.4.6 Dead reckoning 2.4.7 Summary 2.5 Autonomous control system 2.5.1 Fuzzy logic control 2.5.2 Optimal control 2.5.3 Robust control 2.5.4 Summary Chapter 3 Methodology 3.1 System Overview 3.2 Odometry System 3.2.1 Particle Filter 3.2.2 Kalman Filter 3.2.3 Dead reckoning algorithm 3.3 Autonomous control system 3.3.1 Fuzzy logic Controller 3.3.2 Knowledge Rule Base 3.3.3 Inference Mechanism 3.3.4 Defuzzification 3.4 Simulation Program Design 3.4.1 Setup Desired trajectory Chapter 4 Experiments and Results 4.1 Odometry system Result 4.1.1 Odometry system use IMU sensor for Indoor Navigation 4.1.2 Vision Sensor for Indoor Navigation 4.1.3 Compare Odometry system with vision sensor accuracy for Indoor navigation 4.1.4 GPS for Outdoor Navigation 4.1.5 Odometry system use GPS for outdoor Navigation 4.1.6 AIS for Outdoor Navigation 4.1.7 Compare GPS, AIS with Odometry system accuracy for Outdoor navigation 4.1.8 Summary 4.2 Simulation Result of Autonomous control system 4.3 Actual Experiment Result of Autonomous control system 4.3.1 Indoor Experiment Result 4.3.2 Outdoor Experiment Result 4.3.3 Summary Chapter 5 Conclusion and Future Work 5.1 Conclusion 5.2 Future Work Reference

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