Embedded Robot Vision System Based on DSP

Xing Ze Li; Ling Zhu; Yi Hua

doi:10.4028/www.scientific.net/AMM.734.168

Paper Titles

Adaptive Tracking Control without Image Velocity Measurement
p.147

Automation Control System of the Pressure Pulse Testing Rig
p.153

Design of Automatically Picking-Up Table Tennis Based on OpenCV
p.157

Development of Automatic CAN Gateway Test System
p.161

Embedded Robot Vision System Based on DSP
p.168

Research and Design Based on Automated Warehouse of Box-Type Silk Storage
p.172

Research of the Indoor Service Robot Based on ZigBee Positioning
p.179

Research on Fan Warning Model for Curved Roads on Highways Based on Millimeter Wave Radar
p.183

The Application of Aerospace Robot Based on Human-Machine Interactive Control Technology
p.189

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 734Embedded Robot Vision System Based on DSP

Embedded Robot Vision System Based on DSP

Abstract:

Aim at the real-time problem of industrial robot vision system, design a embedded robot vision system based on DSP microprocessor. This system can use CCD camera and the ultrasonic sensor to collect the target environment information. It also can use the processor DSP to process the images and recognize target. And then through the communication module, send results in the form of wireless to the upper computer, providing target object information for robot control layer. This system completes the software and hardware system design, image collection & processing and robot control, as well as meet the real-time requirements of machine vision system.

You might also be interested in these eBooks

Electronics, Automation and Engineering of Power Systems

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 734)

Pages:

168-171

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.734.168

Citation:

Cite this paper

Online since:

February 2015

Authors:

Xing Ze Li*, Ling Zhu, Yi Hua

Keywords:

Embedded System, Machine Vision, Pattern Recognition

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Wenting Technology Development Co., Ltd . DAM6416PImage Processing Platform Software Development Guide [K]. 2003 (In Chinese).

Google Scholar

[2] Philips Semiconductors. SAA7115 Video Processor Datasheet [G]. (2003).

Google Scholar

[3] Micron Company. MT48LC4M32B2 Datasheet [G]. (2002).

Google Scholar

[4] Russo F. An image enhancement technique combining sharpening and noise reduction [J]. IEEE Transactions on Instrumentation and Measurement, 2002, 51 (4): 824-828.

DOI: 10.1109/tim.2002.803394

Google Scholar

[5] Nikoletta Bassiou, Constantine Kotropoulos. Color image histogram equalization by absolute discounting back-off [J]. Computer Vision and Image Understanding, 2007, 107(1-2): 108-122.

DOI: 10.1016/j.cviu.2006.11.012

Google Scholar

[6] Ety Navon , Ofer Miller, Amir Averbuch. Color image segmentation based on adaptive local thresholds[J]. Image and Vision Computing, 2005, 23(1): 69-85.

DOI: 10.1016/j.imavis.2004.05.011

Google Scholar

[7] H.D. Cheng C.H. Chen H.H. Chiu. Image segmentation using fuzzy homogeneity criterion [J]. Information Sciences, 1997, 98(1): 237-262.

DOI: 10.1016/s0020-0255(96)00217-4

Google Scholar

[8] T. Chaira A.K. Ray. Segmentation using fuzzy divergence[J]. Pattern Recognition Letters, 2003, 12(24): 1837-1844.

DOI: 10.1016/s0167-8655(03)00007-2

Google Scholar