SBIR Phase I: Mission Planning Methods and Simulated Crisis Management Framework to teach STEM to Underserved Youth

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The broader/ commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in addressing the unemployment of youth, primarily in underserved communities, across the United States by providing an online, collaborative gaming environment to educate, train, and provide Science, Technology, Engineering and Mathematics (STEM)-based career opportunities. It is estimated that more than 5.5 million youth in the U.S. ages 16 to 24 years, are out-of-school and out-of- work. The unemployment rate in this age group is close to 20 percent. This project aims to improve young people’s skills and overall knowledge, and help them secure certifications in drone design, function, and operations related to industry and first responder communities. Implicit to the core learning are knowledge in areas such as critical thinking and planning, rapid prototyping, mission planning, data post-processing and analytics, and co-authoring of effective robotics designs to assist in industry and first responder areas of operation. The project empowers students to develop strong STEM skills that target career opportunities in advanced manufacturing, inspire budding entrepreneurs, and directly benefit industry and first responder communities with a relevantly trained workforce.This Small Business Innovation Research Phase I project will develop a scalable template of a realistic environment for a multi-player game for education and training of youth, integrating autonomous robots using scenarios driven by industry and first responders. This technology will be accomplished through a collaborative mission readiness workflow application with a multi-player gaming engine, Computer Aided Design (CAD)-generated prototype drones, and geo-accurate areas of terrain for realistic and relevant environments. Intelligent improvements of the system will be accelerated as Machine Learning (ML) and Artificial Intelligence (AI) algorithms are added to the workflow application and gaming environment from data that is collected and analyzed from training, exercise, and lessons learned from live response events. The greatest technical obstacle is modeling the data in a way that preserves integrity and that can be adapted and “trained” for machine learning. Teams of youth, first responders, and engineers in this e-sports gaming league will compete for the best time, procedures, and systems to win each mission. Drone design, physics reality, and machine learning algorithms for each vehicle will be outputs for the drone blueprints that will be actualized via 3D printing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.