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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/6607完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃漢邦(Han-Pang Huang) | |
| dc.contributor.author | Hung-Yi Lee | en |
| dc.contributor.author | 李泓逸 | zh_TW |
| dc.date.accessioned | 2021-05-17T09:15:12Z | - |
| dc.date.available | 2014-08-01 | |
| dc.date.available | 2021-05-17T09:15:12Z | - |
| dc.date.copyright | 2012-08-17 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-08-13 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/6607 | - |
| dc.description.abstract | 在這篇論文裡，我們主要在試著控制我們實驗室所研發的整隻機器人。我們的目標是藉由控制機器人的重心和手腳末端點的位置來完成一些任務。
我們使用preview control來生出重心軌跡，接著把重心軌跡和末端點軌跡送進IK裡面做運算，運算過後就可以得到每一軸的軌跡。我們的IK運算了24個軸，其中手跟腳都各有六個軸。使用裝在機器人手腕和腳踝的六軸力規，機器人可以用三種不同的方式來移動物體。他可以使用推車推重物，用雙手抬起物體，或是使用一隻手提物體並且讓另外一隻手做其他的任務。在手腕的六軸力規可以量到物體的重量或是施於推車的力量。在腳踝的力規可以量到腳底板的反作用力，並且以此計算機器人的零力矩點。機器人可以利用這些資訊來調整重心的軌跡。 雖然我們一開始就讓機器人知道物體的位置和形狀，但機器人在一開始並不知道物體的重量。如果機器人可以辨識出物體的種類的話，他就可以自己選擇適當的方式移動物體，如此一來就更有機會融入我們的生活裡。 | zh_TW |
| dc.description.abstract | In this thesis, we attempt to control the entire body of a humanoid robot that was developed by our laboratory. Our goal is to allow the humanoid robot to use its body to perform some tasks that are controlled by the Center of Gravity (COG) trajectory and the trajectory of the robot legs and arms.
Our study uses preview control to generate the robot’s COG trajectory and then the COG and end-effector trajectory are sent to the IK-solver to determine the trajectory of all of the joints. The IK-solver makes calculations for 24 joints, which include the six joints in each of the robot legs and arms. Using force sensors that was installed on the wrists and ankles, the robot can move objects in three different ways. It can push a cart to move a heavy object, use both arms to lift an object, and have one arm holding an object, while the other performs another task. The force sensors on the robot wrists can also measure the weight of the object or the force that is exerted on the cart. Similarly, the force sensor on its ankles can measure the reaction force of the footpad and calculate the Zero Moment Point (ZMP) of the robot. Based on this information, the robot is able to adjust the COG trajectory. Although we have provided the robot with information about the shape and position of the object, the robot does not initially know the object’s weight. If the robot is able to recognize the shape of the object, it can choose a suitable method for moving it. This will allow the robot operate more easily in daily lift. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-17T09:15:12Z (GMT). No. of bitstreams: 1 ntu-101-R99522821-1.pdf: 8549952 bytes, checksum: 137aa6260f9811f7ed5f16b382cc5c68 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 致謝 i
摘要 ii Abstract iii Content iv List of Tables vi List of Figures vii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Development of Humanoid Robots 2 1.3 Interaction with the environment for humanoid robots 4 1.4 Thesis Organization 5 1.5 Contributions 7 Chapter 2 Kinematics and Dynamics 9 2.1 Introduction 9 2.2 Forward Kinematics 10 2.3 Inverse Kinematics 11 2.3.1 Conventional Jacobian Matrix 12 2.3.2 Fixed Jacobian 14 2.3.3 COG Jacobian 15 2.3.4 RDLS: Singularity Avoidance 17 2.3.5 WLN: Weighted Least-Norm Method 18 2.3.6 RWLN: Robust Weighted Least Norm Method 19 2.4 Walking Pattern Generation 20 2.4.1 Cart-Table Model with ZMP 21 2.4.2 Pattern Generation Using Preview Control 23 2.5 Summary 26 Chapter 3 Whole Body Motion Planning 28 3.1 Introduction 28 3.2 Trajectory Generation 28 3.3 Pushing an Object 31 3.4 Lifting an Object 36 3.4.1 Using Two Arms to Carry a Box 36 3.4.2 Using One Arm to Lift a Bag 39 3.5 Summary 42 Chapter 4 Whole Body Control System 44 4.1 Introduction 44 4.2 Mechanical Design 45 4.3 Mechatronic System 49 4.4 Whole Body Feedback Sensor 53 4.4.1 6-axis Force Sensor 54 4.4.2 Power Monitoring System 55 4.5 Summary 56 Chapter 5 Simulations and Experiments 58 5.1 Hardware Assembly 58 5.2 Software and Firmware Setting 59 5.3 Simulations 62 5.3.1 The First Simulations: Lift Object Using Two Arms 63 5.3.2 The Second Simulation: Lift an Object Using Right Arm 66 5.4 Experiments 69 5.4.1 The First Experiment: Push a Cart Using Two Arms 69 5.4.2 The Second Experiment: Lift an Object Using Two Arms 71 5.4.3 The Third Experiment: Lift an Object Using Right Arm 73 Chapter 6 Conclusions and Future Works 76 6.1 Conclusions 76 6.2 Future Works 77 References 80 | |
| dc.language.iso | en | |
| dc.title | 人型機器人之全身控制與規劃 | zh_TW |
| dc.title | Whole Body Control and Planning for Humanoid Robots | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李祖聖(Tzuu-Hseng Li),施慶隆(Ching-Long Shih) | |
| dc.subject.keyword | 人形機器人,即時步態生成,零力矩點,全身運動規劃,逆向運動學, | zh_TW |
| dc.subject.keyword | Humanoid Robot,Real-time Walking Pattern Generation,ZMP,Whole Robot Motion Planning,Inverse Kinematics, | en |
| dc.relation.page | 87 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2012-08-13 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
| 顯示於系所單位： | 機械工程學系 | |
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