指導教授：黃漢邦臺灣大學：機械工程學研究所張哲軒Chang, Che-HsuanChe-HsuanChang2014-11-292018-06-282014-11-292018-06-282014http://ntur.lib.ntu.edu.tw//handle/246246/263214人型機器人行走於外界環境時會遭受許多干擾，例如: 未知的推力，機器人與環境接觸力和機器人腳底板與地面的瞬間作用力。其中最常見的干擾就是未知的推力。對於不同類型之推力，機器人必須以不同的因應對策處理。本論文主要討論為機器人遭受瞬間推力情況，此推力施加於機器人行走之前進方向，分別從機器人前方與後方施予推力。 本論文所提出的機器人質心角動量穩定控制器，此控制器基於CMP準則與地面接觸力資訊作為回授，能有效產生相對應的上身姿態抵銷外力與未知干擾。除此之外，本論文也提出當機器人遭遇上述兩種推力情況時，產生相對應之質心軌跡修正與腳步軌跡修正之方法。綜合以上兩種因應對策，機器人能在遭遇外力後能迅速回復穩定狀態。When a humanoid robot walks in the environment, it will face many disturbances, including unknown external push, contact forces on the body, and impact ground reaction forces. There are many unpredicted disturbance when a humanoid robot operates in a real environment. One of the most common disturbances is “push”. Different types of pushes should be handled with different strategies, and the strategy introduced in this thesis involves dealing with an external push from both the front and back of the robot in a sagittal direction. The proposed COG angular momentum regulator effectively counteracts external forces and disturbances by generating motion of the upper body via using the CMP criterion and the ground reaction forces as feedback. Furthermore, the online COG and footstep planning strategy address two different types of pushes that are also introduced. The overall push recovery strategy developed in this thesis shows the successful recovery of the robot from a push state.Contents 致謝 i 摘要 ii Abstract iii Contents iv List of Figures vi List of Tables ix Nomenclature x Chapter 1 Introduction 1 1.1 Motivation and Introduction 1 1.2 Related Works 2 1.2.1 Sensor Based Feedback Control 2 1.2.2 Stability Control 4 1.2.3 Push Recovery Control 6 1.3 Thesis Organization 7 1.4 Contributions 7 Chapter 2 Background Knowledge 10 2.1 Introduction 10 2.2 Walking Pattern Generator 11 2.3 Online COG Trajectory Modification 17 2.4 Summary 19 Chapter 3 COG State Estimator 22 3.1 Introduction 22 3.2 Introduction of Kalman Filter 24 3.3 Structure of COG State Estimator 27 3.3.1 System Model 27 3.3.2 Sensor Model 28 3.3.3 The Estimator Parameters Design 30 3.4 Procedure of COG State Estimator 32 3.5 Sensor Implementations 34 3.5.1 Six-Axis Force Torque Sensor 34 3.5.2 Inertia Moment Unit (IMU) Sensor 36 3.5.3 Signal Processing of IMU Sensor with Complementary Filter 38 3.6 Simulation Results 40 3.7 Summary 50 Chapter 4 Push Recovery Strategy 52 4.1 Introduction 52 4.2 Definitions of Different Types of Push 54 4.3 COG Angular Momentum Regulator 55 4.3.1 Introduction to Centroidal Moment Pivot (CMP) Criterion 55 4.3.2 CMP Model of Humanoid Robot 57 4.3.3 Push State Detection 58 4.3.4 Push State Classifications 59 4.4 Angular Momentum Regulator Based on Admittance Control 61 4.5 Upper Body Angular Momentum Jacobian 63 4.6 Overall Push Recovery Strategy 67 4.7 Regulator Stability Analysis 70 4.8 Simulation Results 72 4.9 Summary 85 Chapter 5 Experimental Results 88 5.1 The Scenario of Experiments 88 5.2 Hardware Platform 89 5.3 Experimental Results 90 5.3.1 Experimental Results of Chapter 3 90 5.3.2 Experimental Results of Chapter 4 92 5.3.3 Screenshot of Experimental Videos 99 Chapter 6 Conclusions and Future Works 106 6.1 Conclusions 106 6.2 Future Works 107 References 1089646428 bytesapplication/pdf論文公開時間：2017/08/17論文使用權限：同意有償授權(權利金給回饋學校)人型機器人機器人推動回復角動量穩定控制器重心力矩樞軸thesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/263214/1/ntu-103-R01522806-1.pdf