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Problem Based Learning Through Modeling and Simulation of Unmanned Vehicles

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Conference

2013 ASEE Annual Conference & Exposition

Location

Atlanta, Georgia

Publication Date

June 23, 2013

Start Date

June 23, 2013

End Date

June 26, 2013

ISSN

2153-5965

Conference Session

Ocean and Marine Tech Session

Tagged Division

Ocean and Marine

Page Count

10

Page Numbers

23.983.1 - 23.983.10

Permanent URL

https://peer.asee.org/22368

Download Count

41

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Paper Authors

biography

Lifford McLauchlan Texas A&M University, Kingsville

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Dr. Lifford McLauchlan completed his Ph.D. at Texas A&M University, College Station. After spending time in industry, he has returned to academia. He is an associate professor at Texas A&M University - Kingsville in the Electrical Engineering and Computer Science Department.
His main research interests include controls, robotics, education, adaptive systems, intelligent systems, signal and image processing, biometrics and watermarking. He is the current chair of the ASEE Ocean and Marine Engineering Division and is a senior member of IEEE.

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Mehrube Mehrubeoglu Texas A&M University, Corpus Christi

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Jayson Durham SPAWAR Systems Center Pacific (SSC Pacific)

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Abstract

Problem Based Learning Through Modeling and Simulation of Unmanned VehiclesProblem based learning has been shown to increase student excitement and attention which will increasestudent understanding of course material and concepts. With the high cost of large scale underwater,land and air vehicles, the use of modeling and simulation capabilities becomes more important foruniversity programs. Autonomous Unmanned Vehicle (AUV) Workbench was developed as a modelingand simulation environment to enable physics based real time simulation of autonomous vehicles, suchas surface, underwater, land and air. Vehicle missions can also be replayed for further study. Labexercises have been created for the students to illustrate waypoint navigation and control for underwatervehicles. By enabling the student to see the effects of the control algorithm in the simulated actions,Freshman students gain a larger scale understanding of more advanced concepts that they will learn thetheory for during their junior and senior years; thereby allowing the students to gain insights into how thetheory in various undergraduate classes may be used in applications. AUV Workbench simulationenvironment enhances the student’s understanding of modeling systems.

McLauchlan, L., & Mehrubeoglu, M., & Durham, J. (2013, June), Problem Based Learning Through Modeling and Simulation of Unmanned Vehicles Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. https://peer.asee.org/22368

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