Target Tracking

The January 2019 edition of Aerospace America’s annual Year in Review section for Information Systems featured the flight-demonstration of a machine learning algorithm developed by a team of VSCL students and faculty.  The article discussed the progression of the project from its first demonstration in December 2017 to more recent demonstrations.  The algorithm is based on Q-Learning and provides a control policy for the vehicle’s orientation in order to keep the target fixed in the image frame autonomously. The algorithm was tested against stationary and randomly moving targets in both a structured and unstructured environment.

The Aerospace America article can be found here.

VSCL Undergraduate Research Assistant Chase Noren ’18 presented flight test results of an autonomous tracking intelligent agent at the AIAA Infotech@Aerospace Conference on 11 January at the 2018 AIAA SciTech Forum. The goal of this continuing project is to autonomously track fixed and moving user-selected targets with a non-gimbaled image capturing device mounted on a Small/micro fixed-wing UAS. The autonomous intelligent agent acts independently of human operators and the developed algorithm learns and operates without the need for prior information of road networks or terrain features.  The paper documenting this work is “Flight Testing of Intelligent Motion Video Guidance for Unmanned Air System Ground Target Surveillance,” AIAA-2018-1632.