臺灣博碩士論文加值系統

本論文主要探討人形機器人與情緒音樂伴奏之智慧型學習系統之設計與實現。在雙足機器人步態學習方面，本論文使用粒子群體最佳化演算法來讓機器人自動學習到最快、最穩定的走路姿態。並透過機器人重心偏移位置計算出其適應值，再將每次更新的步態參數導入自身開發的模擬器驗證之。此外，本論文亦使用中樞模式產生器來產生快又穩定的步態，此兩組神經振盪器可控制腳踝位置以產生步態軌跡。最後再透過加速度計與陀螺儀回授，以及人工蜂群演算法來得到中樞模式產生器的最佳參數。人形機器人競賽之智慧型演算法方面，本論文提出針對運球避障與舉重之視覺與策略系統。根據機器人與物體之間的相對關係，機器人的世界座標位置可由改良的粒子濾波器推估得知。甚者，本論文亦提出模仿人類思考行為的認知學習演算法，並應用在人形機器人投籃比賽。而此認知學習演算法是根據丹尼爾‧卡內曼所著「快思慢想」書中之概念設計。在情緒音樂伴奏方面，本論文提出智慧型旋律譜曲與模糊邏輯節奏控制器來自動產生相對應的情緒音樂。最後，實驗結果顯示本論文提出之智慧型學習系統具可行性與實用性。

This dissertation mainly confers the intelligent learning systems for humanoid robots and emotional music accompaniment. In the aspect of the biped gait learning, the humanoid robot is trained on the self-developed simulator system with Particle Swarm Optimization (PSO) method which chooses the trajectory of robot’s centroid as the fitness value to learn faster and stable gaits automatically. Moreover, Central Pattern Generator (CPG) is used to build a fast and stable walking locomotion. Two pairs of neural oscillators are arranged to control positions of the robot’s ankles. In order to find the best parameters of central pattern generator, artificial bee colony algorithm is also chosen to train the parameters with feedback signals of accelerometer and gyro. In the aspect of the intelligent algorithms for humanoid robot competitions, the strategy and the vision system for the obstacle avoidance and weight lifting competition are proposed in this dissertation. Basing on the relation between the object and the robot, the position of the robot at the world coordinate could be figured out with the ameliorative particle filter. Moreover, an algorithm that allows a humanoid robot to imitate human thinking behavior so as to learn the shooting motion for the basketball game is also proposed in this dissertation. This learning algorithm is inspired by a human thinking conception addressed in “Thinking, Fast and Slow” by Daniel Kahneman. In the aspect of the emotional music accompaniment, the intelligent melody composition algorithm and fuzzy logic tempo controller are proposed for the melody music generation. Finally, the experimental results show the feasibility and practicality of the proposed intelligent learning systems.

Abstract I
Acknowledgment III
Contents IV
List of Figures VII
List of Tables XI

Chapter 1. Introduction 1
1.1 Motivation and Literature Survey 1
1.2 Dissertation Organization 10

Chapter 2. Gait Learning Algorithms for Humanoid Robots 12
2.1 Introduction 12
2.2 Particle Swarm Optimization for Gait Learning 13
2.2.1 Humanoid Robot Simulator 14
2.2.2 PSO Gait Learning Method 17
2.3 Environmental Impact Assessed ABC-CPG Gait Learning Method 21
2.3.1 CPG Based Gait Locomotion 22
2.3.2 ABC-CPG Gait Learning 27
2.3.3 Humanoid Robot Simulation System 37
2.4 Experimental Results 43

Chapter 3. Intelligent Algorithms for Humanoid Robot Competitions 57
3.1 Introduction 57
3.2 Obstacle Avoidance and Dribbling Strategy for Humanoid Soccer Robots 58
3.2.1 The algorithm for localization 59
3.2.2 The control strategy for obstacle avoidance and dribbling 65
3.3 Vision and Control Schemes of Weight Lifting Competition 67
3.3.1 Vision System 68
3.3.2 Control Strategy for Weight Lifting 72
3.4 Cognitive Psychology Learning Method for Basketball Event 77
3.4.1 Background Knowledge of the Two Systems in “Thinking, Fast and Slow” 77
3.4.2 Architectures of the Basketball Learning System 79
3.4.3 Learning Methods for Playing Basketball 84
3.5 Experimental Results 94

Chapter 4. Development of Automatic Emotional Music Accompaniment System 103
4.1 Introduction 103
4.2 Overview of the Music Accompaniment System 104
4.3 Emotion-based Composition Methods 108
4.3.1 Instrument configuration 108
4.3.2 Fuzzy Tempo Controller 111
4.3.3 Chord Progression Method 113
4.3.4 APEGA Melody Generation 115
4.3.5 Musical Form 117
4.4 Experimental Results 118

Chapter 5. Conclusions and Future Works 122
5.1 Conclusions 122
5.2 Discussions and Future Works 125

References 131

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