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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成 | |
| dc.contributor.author | Pei-Wen Wu | en |
| dc.contributor.author | 吳佩文 | zh_TW |
| dc.date.accessioned | 2021-06-16T10:53:12Z | - |
| dc.date.available | 2016-08-16 | |
| dc.date.copyright | 2013-08-16 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2013-08-09 | |
| dc.identifier.citation | [1] James J. Kuffner Jr and S. LaValle, “RRT-connect: An Efficient Approach to Single-Query Path Planning,” in Proc. IEEE Int. Conf. on Robotics and Automation, Vol. 2. 2000.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/61208 | - |
| dc.description.abstract | Nowadays, techniques in robotics are getting mature and people expect robots to handle more complex tasks in our daily life. In high-level services, the ability of the robot to grasp objects is essential. In this thesis, to increase e the robot’s ability of interaction with human and environments, we proposed a novel manipulator grasping planner which emphasize the cooperation between two RGB-D cameras and can adapt to the dynamic environments.
We assume that the object to grasp and obstacles are both dynamic. Our motion planner doesn’t need whole environmental model previously, and only requires the object information that the robot is demanded to grasp. The concept of our motion planner is based on the potential field and composed of the attractive and repulsive vectors which are generated by the distances from the manipulator to the target and obstacles respectively. Then the motion planner determines the potential vector according to the attractive and repulsive vectors in a variety of situations. Furthermore, an approach to deal with local minima is contained in the algorithm. For robot control, we take multiple control points into account and apply the potential vector with joint-level control. The mobility and the kinematic constraints of the robot are evaluated in the controller to modify the joint velocities. The experiment platform is a wheeled mobile robot with a 5-DOF manipulator which possesses a close range and a normal range RGB-D cameras as our sensors. Through several experiments, the results show that our framework is valid to accommodate the environmental changes and to grasp the object under various situations. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T10:53:12Z (GMT). No. of bitstreams: 1 ntu-101-R00921006-1.pdf: 3424535 bytes, checksum: d243ab97ede29d470b5a06a98626cf62 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | CONTENTS
口試委員會審定書 # 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv TABLE OF NOTATIONS vii LIST OF FIGURES ix LIST OF TABLES xi Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Background and Related Works 3 1.3 Objectives 5 1.4 Contributions 6 1.5 Thesis Organization 7 Chapter 2 Preliminaries 8 2.1 System Overview 8 2.1.1 Manipulator Motion Planner with RGB-D cameras 10 2.1.2 Robot Motion Controller 10 2.2 Kinematics 12 2.2.1 Forward Kinematics 12 2.2.2 Velocity Kinematics 13 2.3 Pseudo Inverse Matrix by Singular Value Decomposition (SVD) 15 2.4 Camera Perspective Model of RGB-D Camera 18 2.5 Artificial Potential Field 19 Chapter 3 Mobile Manipulator Grasping in Dynamic Environment 22 3.1 Attractive Action 24 3.1.1 Attractive Vector 25 3.1.2 Target Velocity Estimation 29 3.2 Repulsive Action 31 3.2.1 Repulsive Vector 32 3.2.2 Exclusion of the Target/Robot Body from the Collision-free Space 34 3.2.3 Obstacle Velocity Estimation 37 3.3 Potential Action 38 3.3.1 Escape from the Local Minimum 38 3.3.2 Potential Vector 42 3.3.3 Motion Decision 44 3.4 Motion Control of the Wheeled Mobile Manipulator 47 3.4.1 Agile Robot In Office (ARIO) 47 3.4.1.1 Kinematic Model of the Mobile Platform 48 3.4.1.2 Kinematic Model of the Manipulator 49 3.4.2 Motion Controller 51 Chapter 4 Experimental Setting and Results 54 4.1 Experimental Settings 54 4.1.1 Hardware 54 4.1.2 Set up 55 4.2 Experiment I (Grasping the Target on the Desk) 56 4.3 Experiment II (Obstacles Avoidance with Keeping Pose) 59 4.4 Experiment III (Local Minima Escape Strategy) 62 4.4.1 Gap existence 62 4.4.2 Wait 65 4.4.3 Others 68 4.5 Experiment IV (External Command) 70 4.6 Experiment V (Overall Test) 72 Chapter 5 Conclusions and Future Work 76 REFERENCES 78 | |
| dc.language.iso | en | |
| dc.subject | 移動平台 | zh_TW |
| dc.subject | 抓取 | zh_TW |
| dc.subject | 動態環境 | zh_TW |
| dc.subject | 彩色深度相機 | zh_TW |
| dc.subject | mobile manipulator grasping | en |
| dc.subject | RGB-D camera | en |
| dc.subject | dynamic environments | en |
| dc.title | 利用彩色深度相機建立移動型機械手臂於動態環境中抓取物體系統 | zh_TW |
| dc.title | Manipulator Grasping on a Mobile Platform with Help from RGB-D Cameras in Dynamic Environments | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 范欽雄,羅仁權,周瑞仁,黃正民 | |
| dc.subject.keyword | 抓取,移動平台,彩色深度相機,動態環境, | zh_TW |
| dc.subject.keyword | mobile manipulator grasping,RGB-D camera,dynamic environments, | en |
| dc.relation.page | 81 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-08-09 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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