group

Authors' Class Standing

Joshua Gates, Senior Faustina Adeline, Senior Christian Mueller, Sophomore Paulina De La Torre, Junior Olusola Olojede, Senior

Lead Presenter's Name

Joshua Gates

Faculty Mentor Name

Dr. Hever Moncayo

Abstract

Since cost of unmanned aircraft vehicles have decreased recently due to technological advancement, there has been a growing interest in developing and implementing systems for close formation missions. Our research objective is to investigate and implement low-cost vision-based tracking algorithms for such a flight formation. For the first technical objective (TO), we are developing an algorithm for vision-based tracking using a Raspberry-Pi hardware. For the second TO, we assembled a quadcopter to be equipped with a camera module and a calibrated flight control computer. In addition, the research team has performed flight testing to obtain video data of a flying marked quadcopter as a reference for developing the tracking algorithm. The final TO is to test-fly two quadcopters in close formation using vision-based tracking algorithm. Ultimately, this research will provide a reliable platform to further investigate formation flight capabilities, and to extrapolate the technology to a wide range of applications.

Did this research project receive funding support (Spark or Ignite Grants) from the Office of Undergraduate Research?

Yes

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Vision-Based Close Formation Flight of Unmanned Aerial Vehicles (UAVs)

Since cost of unmanned aircraft vehicles have decreased recently due to technological advancement, there has been a growing interest in developing and implementing systems for close formation missions. Our research objective is to investigate and implement low-cost vision-based tracking algorithms for such a flight formation. For the first technical objective (TO), we are developing an algorithm for vision-based tracking using a Raspberry-Pi hardware. For the second TO, we assembled a quadcopter to be equipped with a camera module and a calibrated flight control computer. In addition, the research team has performed flight testing to obtain video data of a flying marked quadcopter as a reference for developing the tracking algorithm. The final TO is to test-fly two quadcopters in close formation using vision-based tracking algorithm. Ultimately, this research will provide a reliable platform to further investigate formation flight capabilities, and to extrapolate the technology to a wide range of applications.

 

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