Document Type



Southwestern Oklahoma State University

Conference Title

SWOSU Research and Scholarly Activity Fair

City and State

Weatherford, Oklahoma

Conference Date

April 15, 2022

Publication Date



This material is based upon work supported by the National Aeronautics and Space Administration under Grant Agreement No. 80NSSC20M0114 issued through Oklahoma Space Grant Consortium. This research is in support of the Fire Dawgs competition team for this year’s SpeedFest competition at Oklahoma State University. This NASA OK Space Grant Consortium funded competition team will compete in the Charlie Class, where an autonomous vehicle will navigate a course and put out a fire.

Robots and self-driving vehicles are useful, especially for hazardous jobs, such as firefighting. The use of high-tech sensing technology is a small part of how self-driving vehicles and robots can sense the world around it. Artificial intelligence or machine learning allows robotic machines to interact with the environment. More powerful sensors and computing allow robotic machines to perform more advanced tasks, allowing developers the ability to imprint human features and capabilities in them. Two examples of this include autos and manufacturing. Autonomous cars use this application for object avoidance and industrial robots use to stop motion when a person gets too close for safety. Researching and programming sensors to make a remote-controlled vehicle drive autonomously, activate object avoidance, navigate environments, and detect distance from a fire. Industrial robots are collaborative robots that uses sensors to share a workspace with humans.

The goal of this NASA mission is to support the pipeline related to research done at NASA and in the Aerospace Industry. At SWOSU, we are currently gathering data for use in machine learning applications. The data comes from the robotic vehicle used for the firefighting competition. We will use this data to examine machine learning tools. This will grow our understanding of how to make this process work and prepare our students for careers using machine learning in the aerospace industry.



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