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研究生： 王浩丞
Wang, Hao-Cheng
論文名稱： 以模糊Q-學習為基礎之負重自動平衡控制策略於大型人形機器人之研製
Design and Implementation of Fuzzy Q-learning Based Weight-lifting Auto-balancing Control Strategy for Adult-sized Humanoid Robots
指導教授： 李祖聖
Li, Tzuu-Hseng S.
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 71
中文關鍵詞： 大型人形機器人 、模糊Q-學習演算法 、FIRA機器人舉重競賽
外文關鍵詞： Adult-sized Humanoid Robots, Fuzzy Q-Learning Control, FIRA Weight-lifting Competition
相關次數： 點閱：112  下載：1 
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    • 本論文提出一運用模糊Q-學習演算法之控制器，以改進大型人形機器人在負重時行走之穩定性。本論文首先介紹本實驗室第三代大型人形機器人軟硬體架構、硬體規格與設計概念。為了讓機器人能適應不同負重情況，用感測器來獲得環境資訊是必要的，所以在機器人動作控制系統中加入了感測器與回授控制。在本論文所提模糊Q-學習演算法控制器中，係以這些感測器之資料為輸入，並產生相對應之輸出來對馬達做出調整，以適應不同負重時的情況，感測器包含經卡爾曼濾波器處理之九軸慣性感測器與用於計算零力矩點之壓力感測器。本論文所提控制器分為兩個學習階段，第一階段係學習控制器對步態中各姿態之影響權重，第二階段則對模糊Q-學習演算法做出學習，學習完成後，機器人便能藉由此回授控制器在不同的負重情況時進行自主平衡，維持步態的穩定性。最後，以FIRA機器人競賽的舉重項目進行實驗，證明本論文所提回授控制器之可行性與效果。

    This thesis proposes a control method that improves the ability of adult-sized humanoid robots to adapt to weight-lifting situations. First, the architecture of the hardware, software and the design concepts for the third generation adult-sized robot, David III, are introduced. In order to achieve the goal of having humanoid robots automatically balance their motion for weight-lifting situations, feedback control is added to the motion control system. The feedback sensors include a three-axis accelerometer and a three-axis gyroscopic, which would be processed by Kalman filter, as well as eight force sensors providing the zero moment point (ZMP) information on the robot. These feedback signals are used as the input of a Fuzzy Q-learning controller, which adjusts the motions to keep the stabilization of the robot. The Fuzzy Q-learning controller consists of two stages: one is the stage of fitting the output weights of each pose in motion patterns, and the second is training the rule-table of the controller. The experiment shows that the controller allows the adult-sized robot to walk stably no matter whether it lifts a weight or has a backpack on its back. Thus, the developed controller does indeed keep the balance of the robot in different situations, which gives the robot the ability to adapt to various environments in the manner of human beings.

    Abstract I Acknowledgment III Contents IV List of Figures VII List of Tables X Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Thesis Organization 3 Chapter 2. Hardware Specifications of Adult-sized Humanoid Robot 5 2.1 Introduction 5 2.2 The Configurations of David III 7 2.3 Hardware Specification 10 2.3.1 Actuators 10 2.3.2 Motion Controller 11 2.3.3 Zigbee Module 14 2.3.4 9-axes IMU 15 2.3.5 Camera 16 2.3.6 Computer 17 2.3.7 Force Sensor 18 2.3.8 Li-poly Batteries 19 2.3.9 Materials 20 2.3.10 Gear 21 2.4 Special Design for David III 21 2.4.1 Anti-twisting Structure 22 2.4.2 Block of Motors 23 2.5 Summary 25 Chapter 3. Sensor Processing 27 3.1 Introduction 27 3.2 Vision 28 3.3 Accelerometer and Gyroscope with Kalman Filter 32 3.4 Force Sensor and Zero Moment Point (ZMP) 34 3.5 Summary 37 Chapter 4. Auto-balance Control Strategy Based on Fuzzy-Q Learning Control 38 4.1 Introduction 38 4.2 Concept of Fuzzy-Q Learning Control Method 39 4.2.1 Concept of Fuzzy Logic Control 39 4.2.2 Concept of Q-learning 40 4.2.3 Concept of Fuzzy Q-learning 42 4.3 Gait Pattern of David III 44 4.4 The Learning Process of Auto-balance Control Method 45 4.4.1 The first stage learning 45 4.4.2 The second stage learning 49 4.5 The Interface of the learning process 52 4.6 Summary 54 Chapter 5. Experiment Results 55 5.1 Introduction 55 5.2 Experimental Results 56 5.2.1 Comparison of Motions before and after the Learning Process 56 5.2.2 Control Strategy with Different Weight Lifting Situations 62 5.2.3 Auto-balance control when a backpack on robot’s back 63 Chapter 6. Conclusions and Future Works 66 6.1 Conclusions 66 6.2 Future Works 68 References 69

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    https://docs.google.com/document/d/1YgsunoOlx9Bg6bNirxuCqHSWQbIZEIlA9KlDZkQy1RA/pub

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