臺灣博碩士論文加值系統

在科技進步與人力結構的變遷下，人們開始追求更高的生活品質，在社會福利、醫療照護、居家看護、教育與各種服務等等的需求也相對提升。隨著智慧型機器人產業的發展逐漸受到重視，使機器人逐漸進入人們的生活中，並提供舒適安全與健康的生活是近年來學界與業界共同追求的遠景。因此，智慧型機器人產業的發展在世界各國中也列為前瞻優先發展的科技產業之一，而整合了許多功能的服務型居家機器人也成為人機互動領域中相當重要的領域。
本論文主題在於發展一具七軸雙手臂全自動機器人之位置控制演算法以及人機互動之人體姿態辨識系統，智慧型居家服務機器人利用深度影像系統取得環境中使用者的骨架進行判別並與人們產生互動。具七軸雙手臂全自動機器人之位置控制演算法利用系統鑑別方法得到各軸馬達轉移函數，其中包含機械負載結構之等效模型，並採用了模型預測控制(Model Predictive Control)取代了一般所使用的PID位置迴路控制器，其概念是使用馬達以及其負載等效後的預測模型為基礎，即時計算出最佳化性能指標其中包含實際回授修正的方法，以克服未知系統結構參數及模型建構誤差和外在環境之不確定因素的影響。此外，有效地改善一般控制理論面對受控對象具有某些特性時所無法處理的問題，同時也彌補了無窮時域最佳控制只能用於非時變系統的不足。
人機互動部分使用的平台是微軟公司(Microsoft)所生產的Kinect具有擷取深度影像資訊的體感測器，它能同時提供色彩，深度，以及紅外線等影像資料以供使用者作進一步處理，依據姿態辨識結果並將軟體整合應用於本實驗室自行開發之雙手臂全自動服務型機器人(Panda robot)平台上。

Since technology developed rapidly and societal evolvement progressively, people begin to pursue higher quality of life and the requirements in social welfare, medical care, home care, education and other services are increased. With the progress of intelligent robot industry, how to integrate robots into the daily life and how robots provide a comfortable, safe and healthy life become common visions in both academia and industry in recently years. Thus, the development of intelligent robot industry has been one of the priority prospects of industries. Among these, the service robot integrated multi-functions has become one of significant issues in Human-Robot Interactions and Services.
This thesis is focus on the development of a position control algorithm for a seven-DOF dual arm autonomous robot and the human body gesture recognition system in human-robot interaction. The intelligent service robot utilizes the depth image system to obtain the skeleton of the user in environment, recognize and interact with the users. The seven-DOF dual arm autonomous robot position control strategy utilizes system identification method to obtain the motor transfer function for each axis, which involves the equivalent model of the mechanical loading structure, and implement Model Predictive Control to replace the traditional PID position loop controller. The concept is based on the predictive model of the equivalent mechanical model, and instantly calculates the optimal performance index including practical feedback information to improve the influence of unknown system structure parameters, modeling error, and the uncertainties of the environment. Moreover, it effectively improves the problems that the model having some characteristics which general control theory is not able to deal with. It also improves the deficiency of the infinite horizon optimal control which is only able to handle time-invariant system.
All the systems, user interface, software and applications proposed in this thesis are implemented with C++ programming language in Windows platform. With relative stable facial features extraction, the proposed algorithm is implemented in robots developed in our laboratory at the International Center of Excellence on Intelligent Robotics and Automation Research (iCeiRA) at National Taiwan University.

Table of Contents
誌謝 I
中文摘要 II
Abstract III
Table of Contents V
List of Figures VII
List of Tables XI
Chapter 1 Introduction 1
1.1 Era of Robot 1
1.2 Objective 2
1.3 Literature Review 3
1.4 Thesis Organization 6
Chapter 2 System Architecture 7
2.1 Hardware Introduction 7
2.2 Software Introduction 16
2.3 Robot Coordinate System 18
Chapter 3 Model Predictive Control 22
3.1 Basic Concept and Theory 22
3.2 Input Increment Form 26
3.3 Derivation of Predictive Model 28
3.3.1 Single-Step Prediction 29
3.3.2 Multi-Step Prediction 32
3.4 Derivation of Control Sequence and System Stability 36
3.4.1 Derivation of Control Sequence 37
3.4.2 Closed-Loop System Stability 41
3.5 Flow Chart of Model Predictive Control 45
Chapter 4 DC Motor Control Strategy 47
4.1 Motor Model Introduction 47
4.1.1 DC Motor 47
4.1.2 Field-controlled DC motor 51
4.1.3 AC Servomotor 54
4.1.4 Motor system with gear box 56
4.2 General PID Control Strategy 59
4.3 Proposed Model Predictive Control Strategy 62
4.3.1 Cascade Control Architecture of Motor System 62
4.3.2 Proposed Control Strategy 65
4.4 Selection of Performance Index 67
4.5 Derivation of Control Sequence and Predictive Model 70
Chapter 5 Human-Robot Interaction 75
5.1 Introduction of Open NI 2.0 & Nite2 75
5.2 Algorithm of Gesture Recognition 76
5.3 Experimental Results 78
Chapter 6 Experimental Result 80
6.1 Simulation of the Proposed Control Strategy 80
6.2 System Identification of Servomechanism 82
6.3 Tracking Performance 88
6.3.1 Parameters of MPC Controller 88
6.3.2 Trajectory Tracking 91
Chapter 7 Conclusion and Future Works 104
References 106
VITA 108

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