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Function-based design process for an intelligent ground vehicle vision system

[+] Author Affiliations
Robert L. Nagel

James Madison University, School of Engineering, MSC 4113, HHS 3224, Harrisonburg, Virginia 22807

Kenneth L. Perry

Missouri University of Science and Technology, Department of Computer Science, 300 West 12th Street, 203 Centennial Hall, Rolla, Missouri 65409

Robert B. Stone

Oregon State University, School of Mechanical, Industrial and Manufacturing Engineering, 406 Rogers Hall, Corvallis, Oregon 97331

Daniel A. McAdams

Texas A&M University, Mechanical Engineering Department, Engineering/Physics Building 108, MS 3123, College Station, Texas 77843

J. Electron. Imaging. 19(4), 043024 (December 28, 2010). doi:10.1117/1.3528476
History: Received November 10, 2009; Revised October 14, 2010; Accepted October 29, 2010; Published December 28, 2010; Online December 28, 2010
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An engineering design framework for an autonomous ground vehicle vision system is discussed. We present both the conceptual and physical design by following the design process, development and testing of an intelligent ground vehicle vision system constructed for the 2008 Intelligent Ground Vehicle Competition. During conceptual design, the requirements for the vision system are explored via functional and process analysis considering the flows into the vehicle and the transformations of those flows. The conceptual design phase concludes with a vision system design that is modular in both hardware and software and is based on a laser range finder and camera for visual perception. During physical design, prototypes are developed and tested independently, following the modular interfaces identified during conceptual design. Prototype models, once functional, are implemented into the final design. The final vision system design uses a ray-casting algorithm to process camera and laser range finder data and identify potential paths. The ray-casting algorithm is a single thread of the robot's multithreaded application. Other threads control motion, provide feedback, and process sensory data. Once integrated, both hardware and software testing are performed on the robot. We discuss the robot's performance and the lessons learned.

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© 2010 SPIE and IS&T

Citation

Robert L. Nagel ; Kenneth L. Perry ; Robert B. Stone and Daniel A. McAdams
"Function-based design process for an intelligent ground vehicle vision system", J. Electron. Imaging. 19(4), 043024 (December 28, 2010). ; http://dx.doi.org/10.1117/1.3528476


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