博碩士論文 955401018 詳細資訊




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姓名 侯俊成(Chun-Cheng Hou)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 智慧型機器人定位與控制之研究
(Intelligent Robot Localization and Control)
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摘要(中) 本論文提出機器人定位與身體姿態平衡控制的方法,分別在輪型移動式機器人與六足機器人兩種硬體設備上實現。
首先,在輪型移動式機器人設計方面,除了具有目標物體影像辨識、目標物夾取與避障功能之外,本論文更著重在機器人的相對位置定位系統,所採用的定位方法是利用陀螺儀及磁力計兩組感測器模組的數值,校正當前機器人的轉動方向角度。角度校正可分為三個部分:第一部分是計算出安裝於機器人上的陀螺儀與磁力計的角度數值;第二部分是探討感測器模組與機器人實際轉動方向角的誤差特性;第三部分是利用這些誤差特性的數據來設計模糊規則庫和卡爾曼濾波器參數,並利用它們來消除誤差,得到更準確的方向角。這些誤差特性可以被描述為規則性及非規則性誤差,前者可使用模糊理論來消除,後者可使用卡爾曼濾波器理論來消除。本研究方法的貢獻是提出感測器與機器人實際轉動角度的誤差校正方法,使得指定路徑、真實路徑及演算路徑三者近似,達到智慧型機器人準確的定位效果。實驗結果表明,模糊補償和卡爾曼濾波器的組合是一個準確的校正方法。
再者,為了驗證本論文所提出的改良型中樞型態產生器(Central Pattern Generator, CPG)應用在機器人的身體姿態平衡控制方面的可行性,本論文採用六足機器人作為理論驗證的硬體。CPG控制器為一種分散式控制方法,其在機器人步態動作設計方面是仿效一般生物的規律性動作控制機制,利用低階神經細胞互相傳遞與影響而產生規律性周期訊號的特性,再藉由外界資訊或大腦刺激修正該訊號,共同合成為最後的運動模式。本論文的設計方法是將機器人的每一條腿用一個改良型CPG控制器控制,並且與機器人其他腿部的CPG彼此互相連結,藉由不同的連結方式產生出不同的運動步態。本論文以Matsuoka神經振盪器作為CPG的基本組成單元,並提出新的CPG架構:在負責振盪器相位調整的環形三連結之雙神經元CPG架構下,另外再加入一個外部神經振盪器,負責調整振盪器的振幅,用來控制機器人腿部踩下的深度。整體的控制架構是利用三軸加速度計與三軸角速度計獲取即時的機器人姿態,再分離各個腿部方向的傾斜角度後作為回授訊號輸入到CPG並改變其振幅大小,再與固定振幅的參考振盪器做比較產生出能夠平衡身軀的腿部高度參考訊號,隨後將此控制訊號經由軌跡產生器轉換為機器人腿部動作的軌跡,此軌跡再經由逆運動學得到實際的伺服馬達轉動角度用來控制馬達轉動角度,如此,可以使機器人在不規則地形行走時,不但能作前進的動作亦能夠即時地恢復水平的姿態。經實驗結果顯示,本研究所提出之步態設計方法能有效地讓六足機器人平穩地行走在崎嶇不平的地形。
摘要(英) This dissertation proposes the methods of localization and body attitude balance, which were adopted in two hardware devices, mobile robot and hexapod robot.
In terms of the indoor service mobile robot design, in addition to target object image recognition, target object gripping, and obstacle avoidance functions, the research of mobile robot focuses on the relative position location system of the robot. The localization method uses the values of two sensor modules, which are gyroscope and magnetometer, to correct the current rotation direction angle of the robot. The angle correction is divided into three parts: the first part calculates the angle values of the gyroscope and magnetometer that are mounted on the robot; the second part obtains the error characteristics between the sensor modules and the actual rotation direction angle of the robot; the third part uses the data of these error characteristics to design a fuzzy rule base and Kalman filter parameter, and uses them to eliminate errors in order to obtain more accurate direction angle. These error characteristics can be described as regular and irregular errors, where the former can be eliminated using the fuzzy theory, and the latter can be eliminated using the Kalman filter theory. The contribution of this dissertation is the error correction method for sensors and actual rotation angle of the robot, where the specified path, actual path, and calculated path can approximate to each other, thus, implementing accurate localization of the intelligent robot. The experimental results show that the combination of fuzzy compensation and Kalman filter is an accurate correction method.
In addition, in order to validate the feasibility of the application of the improved Central Pattern Generator (CPG), as proposed in this research for robot body attitude balance control, the hexapod robot was used as the hardware of theoretical examination. The CPG controller is a type of distributed control method; it imitates the regular motion control mechanism of the organisms in the robot gait motion design, the mutual transmission and effect of low-level neural cells generate regular and periodic signals, and the signal is corrected by external information or cerebral irritation, and synthesized into the final motion mode. In terms of the gait design, each leg of the robot is controlled by an improved CPG controller, connected with the CPG of the other robot legs, and different motion gaits are generated by different connected modes. The Matsuoka neural oscillator as the basic composition unit of CPG, and proposes a new CPG architecture: in the annular three-link double neuron CPG architecture in charge of oscillator phasing, an external neural oscillator is added, which is in charge of adjusting the amplitude of oscillator in order to control the treading depth of the robot legs. The overall control architecture uses an accelerometer and a gyroscope to obtain the real-time robot body attitude, while the tilt angles of the leg directions are separated as feedback signals imported into the CPG to change the amplitude. It is compared with the reference oscillator of fixed amplitude to generate the leg height reference signal, which can balance the body. Afterwards, the control signal is converted by the trajectory generator into the track of the robot leg action. The actual servo motor rotation angle is obtained from this trajectory by inverse kinematics to control the motor rotation angle. Thus, the robot can move forward, and instantly restore horizontal body attitude when walking on rugged terrain. The experimental results show that the gait design method proposed in this research enables the hexapod robot to walk smoothly on rugged terrains.
關鍵字(中) ★ 智慧型機器人
★ 六足機器人
★ 模糊理論
★ 卡爾曼濾波器
★ 中樞型態產生器
關鍵字(英) ★ Intelligent robot
★ hexapod robot
★ Fuzzy theory
★ Kalman filter
★ central pattern generator
論文目次 Table of Contents
中文摘要 i
Abstract iii
誌謝辭 v
中文目錄 vi
Table of Contents I
List of Figures V
List of Tables IX
List of Symbols X
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Review of Previous Works 4
1.3 Purpose and Contributions 7
1.4 Organization and Main Tasks 9
Chapter 2 Brief of System Theory 10
2.1 Introduction 10
2.2 Fuzzy Theory 10
2.3 Kalman Filter Theory 13
2.4 Neural Oscillator and Central Pattern Generator 16
2.5 Summary 19
Chapter 3 Fuzzy and Kalman Filter for Mobile Robot Localization 21
3.1 Introduction 21
3.2 System Architecture and Hardware Structure of the Mobile Robot 22
3.2.1 System Architecture 22
3.2.2 Hardware Structure 24
3.3 Rotation Angle Correction Algorithms of the Robot 31
3.3.1 Rotation Angle Correction 31
3.3.2 Specified Position Tracking 38
3.4 Image Tracking, Avoidance and Object Gripping 42
3.4.1 Image Tracking 42
3.4.2 Avoidance and Transporting Object 50
3.5 Experiment Results 52
3.5.1 Angle Correction Experiments 52
1. Moving distance error analysis (odometer) 52
2. Rotation angle error analysis 53
3. Algorithms to achieve 57
4. Experimental results 62
5. Conclusion 66
3.5.2 Image Tracking, Collision avoidance and Object Gripping Experiments 68
1. Image Tracking Experiment 68
2. Collision avoidance Experiment 71
3. Object Gripping Experiment 72
4. Conclusion 72
3.6 Summary 73
Chapter 4 Design a New CPG Gait for Hexapod Robot Balance Control 75
4.1 Introduction 75
4.2 System Architecture and Hardware Structure of the Hexapod Robot 76
4.2.1 System Architecture 76
4.2.2 Hardware Structure 77
4.3 New CPG Design 79
4.3.1 Matsuoka’s Dual-Neural Oscillator 79
4.3.2 New CPG Design 82
4.3.3 New CPG Topology of Tripod and Quadruped Gait 83
4.4 Gaits Control 89
4.4.1 Leg Trajectory 89
4.4.2 Inverse Kinematics 90
4.4.3 Complementary Filter 92
4.4.4 Feedback Design 94
4.5 Experiment Results 96
4.5.1 Different Modes of Travel 96
4.5.2 Results of Experimental 99
1. Moving forward on flat terrain 99
2. Moving across a big obstacle 100
3. Moving across three obstacles 102
4. Treading on an obstacle on the spot 104
4.5.3 Conclusion 105
4.6 Summary 105
Chapter 5 Conclusion and Future Work 107
5.1 Conclusion 107
5.2 Future Work 108
References 109
參考文獻 References
References
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指導教授 鍾鴻源(Hung-Yuan Chung) 審核日期 2015-8-17
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