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系統識別號 U0026-2207201415035300
論文名稱(中文) 植基於人類思考行為之認知學習演算法實現人形機器人之FIRA投籃競賽策略
論文名稱(英文) Design and Implementation of a Human Thinking Based Cognition Learning Algorithm for Humanoid Robots at a Basketball Competition of the FIRA HuroCup
校院名稱 成功大學
系所名稱(中) 電機工程學系
系所名稱(英) Department of Electrical Engineering
學年度 102
學期 2
出版年 103
研究生(中文) 于庭婕
研究生(英文) Ting-Chieh Yu
學號 N26014168
學位類別 碩士
語文別 英文
論文頁數 105頁
口試委員 指導教授-李祖聖
口試委員-郭逸平
口試委員-孔蕃鉅
口試委員-郭昭霖
召集委員-邱俊賢
中文關鍵字 大型人形機器人  機器學習  認知心理學 
英文關鍵字 Humanoid Robot  Machine Learning  Cognitive Psychology 
學科別分類
中文摘要 本論文旨在設計一演算法,其允許人形機器人模仿人類思考行為以用於學習投球姿態,並實際應用於FIRA國際機器人競賽之籃球比賽;本系統主要由影像處理、學習演算法與大型人形機器人之硬體架構等構成。其中,影像處理與學習行為均採用筆記型電腦為中央運算模組;在影像處理部分,除連接一台網路攝影機做為視覺感知器,亦於籃框上方及旁側裝置兩台網路攝影機加以監測。為了辨識各式的物體特徵及追蹤特定目標,我們應用快速且簡易的搜尋與追蹤法加以實現。接著,根據2002年諾貝爾經濟學獎得主丹尼爾‧卡內曼所著“Thinking, Fast and Slow”書中之概念設計一創新的學習演算法,其核心思想是將人類的認知心理學分為兩種模式:快思與慢想,快思系統傾向直覺式思考,而慢想系統屬邏輯式思考;另外,此演算法亦將書中所提之錨點效應與峰終定律等概念加諸於設計與應用,目的為使機器人得以藉學習過程獲得經驗曲線,並完成FIRA國際機器人競賽之籃球競賽之任務。最後經實驗結果可充分展現此仿生的學習方法應用於機器人上是合理且效能優越的。
英文摘要 The main concept of this thesis is to design an algorithm that allows a humanoid robot to imitate human thinking behavior so as to learn the shooting pose for the FIRA HuroCup basketball competition. The systems proposed in this thesis include image processing algorithms, a learning algorithm, and hardware architecture of adult-sized humanoid robots. Before instructing the robot to do some motions, a vision feedback control system is processed first and then executes the learning on the computer. In an image processing system, a CMOS webcam sensor is used on the robot as the eye, and two internet protocol cameras are installed on the side and above the basket separately. To recognize and segment the objects, a recursive searching algorithm is developed for the issue. Then, a novel learning algorithm is designed by a human thinking conception proposed in “Thinking, Fast and Slow” by Daniel Kahneman, the Nobel Memorial Prize winner in 2002 Economic Science. This algorithm is based on cognitive psychology, which divides human thinking into two modes, fast and slow. The fast mode favors intuitive thinking while the slow mode favors rational thinking. Furthermore, the cognitive bias of the anchoring effect and the peak-end rule are integrated. This algorithm is implemented and applied on the robot for it to learn the experience curves. After the learning process, the robot also accomplishes the basketball game in the FIRA HuroCup. Eventually, the experimental results show that the performance of its bionic learning method is very efficient. In other words, the learning method also verifies that the thinking mode of the human being is reasonable and can be applied on the robot.
論文目次 Abstract I
Acknowledgment III
Contents IV
List of Figures VII
List of Tables XI


Chapter 1. Introduction 1
1.1 Motivation 1
1.2 Review of Some Intelligent Algorithms Derived from Biology 3
1.3 Thesis Organization 6

Chapter2. Hardware and Software of the Robot 9
2.1 Introduction 9
2.2 Hardware Configuration and Specification of David II 10
2.2.1 Actuators 13
2.2.2 Motion Controller 15
2.2.3 Circuit Board 18
2.2.4 ZigBee Module 19
2.2.5 9-axes IMU 20
2.2.6 Force Sensor 21
2.2.7 Li-poly Batteries 22
2.2.8 Camera 23
2.2.9 Computer 24
2.3 Overview of Vision and Strategy System 25
2.3.1 Pre-procedure of Image Processing 25
2.3.2 The Main Image Processing 29
2.3.3 The Human Machine Interface of Vision System 34
2.3.4 The Human Machine Interface of Strategy System 35
2.3.5 Summary 36

Chapter 3. The Psychological Background of the Cognition Learning Algorithm 38
3.1 Introduction 40
3.2 Cognition psychology 40
3.2.1 Experimental Cognitive Psychology 40
3.2.2 Cognitive neuroscience 41
3.2.3 Computer analogy 42
3.2.4 Summary 44
3.3 Overview of the Two Systems in “Thinking, Fast and Slow” 46
3.4 Anchoring Effect and Peak-end Rule 50
3.4.1 Anchoring Effect 50
3.4.2 Peak-End Rule 53

Chapter 4. Basketball Strategy and Cognition Learning Algorithm 56
4.1 Introduction 56
4.2 Basketball Competition 58
4.2.1 Object Recognition 59
4.2.2 Control Strategy 61
4.3 Cognition Learning Algorithm 67
4.3.1 The hardware for learning environment 67
4.3.2 The Human Machine Interface Used in the Learning 70
4.3.3 The application of the fitting curve 73
4.3.4 The procedure of the algorithm 77

Chapter 5. Experiment Results 88
5.1 Introduction 88
5.2 The performances of the two types for System 2 89
5.3 The results of the cognition learning algorithm 94

Chapter 6. Conclusions and Future Works 97
6.1 Conclusions 97
6.2 Future Works 100

References 101
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