Embedded Visual System and its Applications on Robots


by

De Xu

DOI: 10.2174/97816080516631100101
eISBN: 978-1-60805-166-3, 2010
ISBN: 978-1-60805-310-0



Recommend this eBook to your Library

Indexed in: Scopus

Embedded vision systems such as smart cameras have been rapidly developed recently. Vision systems have become smaller and lighter, bu...[view complete introduction]

Table of Contents

Foreword

- Pp. i

Qinglin Wang Beijing

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Preface

- Pp. ii

De Xu

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Contributors

- Pp. iii

De Xu

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Introduction of Robot Vision on the Aspects from Configuration to Measurement and Control Methods

- Pp. 1-14 (14)

De Xu

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Hardware and Software Design of an Embedded Vision System

- Pp. 15-29 (15)

Jia Liu

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Embedded Vision Positioning System Based on ARM Processor

- Pp. 30-46 (17)

Wei Zou, De Xu and Junzhi Yu

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Collaboration Based Self-localization Algorithm for Humanoid Robot with Embedded Vision System

- Pp. 47-55 (9)

Wei Zou, De Xu and Junzhi Yu

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Application of Vision Sensor to Seam Tracking of Butt Joint in Container Manufacture

- Pp. 56-82 (27)

Zao Jun Fang and De Xu

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Vision System Design and Motion Planning for Table Tennis Robot

- Pp. 83-102 (20)

Zheng Tao Zhang, Ping Yang and De Xu

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Object Recognition Using Local Context Information

- Pp. 103-118 (16)

Nong Sang and Changxin Gao

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The Structured Light Vision System and Application in Reverse Engineering and Rapid Prototyping

- Pp. 119-131 (13)

Bingwei He and Shengyong Chen

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Subject Index

- Pp. 132-134 (3)

De Xu

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Foreword

It has been known for a very long time that vision systems are essential for autonomous robots to recognize the environments where they are and to detect and measure the objects that they are interested in to track or avoid. Vision system for a robot is just like the eyes for a person. Up to now, almost all robots are equipped with vision system.

Traditionally, a vision system consists of cameras and a computer. An image grabber card inserted in the computer is employed to capture images from the cameras to the computer. The large size and high energy cost prevent the traditional vision system from micro robots or some autonomous robots that require small and light vision sensing system. Indeed, a vision system works as a kind of sensing system to provide special information what the robots need. The ideal vision system should like other sensors, such as distance sensors, position sensors, velocity sensors etc., which are of compact structure and can present the specified sensing information. Thanks to the developments of electronics and optical engineering, the compact version system, that is, the embedded vision system integrating the camera and processing unit together, merges in recent years. Of course, the computing power of an embedded vision system is not as strong as that of the computer in a traditional vision system. Therefore, how to sufficiently utilize the limited computing capability in an embedded vision system is necessary to investigated.

The e-book edited by Prof. De Xu provides a broad overview of the embedded vision system and addresses the aforementioned questions. Chapters written by experts in their respective fields will make the reader have a variety of topics ranging from the configuration to algorithm design and applications. I believe that this e-book should be very useful to basic investigators and engineers interested in the latest advances in this exciting field. ".

Professor Qinglin Wang
Beijing Institute of Technology
Beijing 100190
China


Preface

Vision system is very important for robots to sense the environments where they work and to detect the objects what they will operate. Effective vision system can greatly improve robot's flexibility, adaptability and intelligence. Up to now, vision system has been widely applied on various robots such as mobile robots, industrial robots, under water robots, and flying robots. However, most of the vision systems currently used by robots consists of traditional cameras and image capture devices, and the image processing algorithms are executed on PC-based processors. The separated components make the traditional vision system be large and heavy, which prevents it from many applications requiring small and light vision system.

Recently, embedded vision system such as smart camera has been rapidly developed. Vision system becomes smaller and lighter, but its performance is stronger and stronger. The algorithms in embedded vision system have their specified characteristics because of resource limitations such as main frequency of CPU, memory size, and architecture. The motivation of this e-book is to provide a platform for the engineers, researchers and scholars in the robotics, machine vision, and automation communities to exchange their ideas, experiences and views on embedded vision system. The topics or chapters include the configuration and algorithm designs for embedded vision systems, and the applications of smart cameras on different autonomous robots, and etc.

We prepare to invite the eminent scientists or engineers in the field of visual measurement and control for robotics and automation to contribute their currently works to this e-book. The actual effectiveness in practice will be emphasized for all methods or systems presented in this e-book. Our goal is to provide an excellent e-book about embedded visual system, which can be used as guidance book and advanced reference, for the readers from the postgraduates in university to the engineers in factory.

I would like to thank all my colleagues and friends who have contributed to this e-book.

De Xu
Institute of Automation,
Chinese Academy of Sciences
Beijing 100190, China

List of Contributors

Editor(s):
De Xu
Institute of Automation, Chinese Academy of Sciences
P.R China




Contributor(s):
Shengyong Chen
College of Information Engineering
Zhejiang University of Technology
Hangzhou , 310014
P.R. China


Shouxian Chen
State Key Laboratory of Industrial Control Technology
Institute of Cyber-Systems and Control, Zhejiang University
Hangzhou , 310027
P. R. China


Jian Chu
State Key Laboratory of Industrial Control Technology
Institute of Cyber-Systems and Control, Zhejiang University
Hangzhou , 310027
P. R. China


Zao Jun Fang
Key Laboratory of Complex Systems and Intelligence Science
Institute of Automation, Chinese Academy of Sciences
Beijing , 100190
P. R. China


Changxin Gao
Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology
Wuhan , 430074
P. R. China


Bingwei He
Fuzhou University



Jia Liu
Robotics Institute
Beihang University
Beijing , 100083
P. R. China


Yong Liu
State Key Laboratory of Industrial Control Technology
Institute of Cyber-Systems and Control, Zhejiang University
Hangzhou , 310027
P. R. China


Nong Sang
Institute for Pattern Recognition and Artificial Intelligence
Huazhong University of Science and Technology
Wuhan , 430074
P. R. China


Rong Xiong
State Key Laboratory of Industrial Control Technology
Institute of Cyber-Systems and Control, Zhejiang University
Hangzhou , 310027
P. R. China


De Xu
Key Laboratory of Complex Systems and Intelligence Science
Institute of Automation, Chinese Academy of Sciences
Beijing , 100190
P. R. China


Junzhi Yu
Key Laboratory of Complex Systems and Intelligence Science
Institute of Automation, Chinese Academy of Sciences
Beijing , 100190
P. R. China


Zheng Tao Zhang
Key Laboratory of Complex Systems and Intelligence Science
Institute of Automation, Chinese Academy of Sciences
Beijing , 100190
P. R. China


Wei Zou
Key Laboratory of Complex Systems and Intelligence Science
Institute of Automation, Chinese Academy of Sciences
Beijing , 100190
P. R. China




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