Mr. Qian Zhao Profile

Qian Zhao

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Proceedings Article | 23 April 2020 Presentation + Paper

Drone proximity detection via air disturbance analysis

Q. Zhao, J. Hughes, D. Lyons

Proceedings Volume 11425, 114250L (2020) https://doi.org/10.1117/12.2556385

KEYWORDS: Gyroscopes, Sensors, Data modeling, Data mining, Curium, Ultrasonics, Aerodynamics, Machine learning, Robotics, Unmanned aerial vehicles

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The use of unmanned aerial vehicles (drones) is expanding to commercial, scientific, and agriculture applications, including surveillance, product deliveries and aerial photography. One challenge for applications of drones is detecting obstacles and avoiding collisions. A typical solution to this issue is the use of camera sensors or ultrasonic sensors for obstacle detection or sometimes just manual control (teleoperation). However, these solutions have costs in battery lifetime, payload, operator skill. We note that there will be an air disturbance in the vicinity of the drone when it’s moving close to obstacles or other drones. Our objective is to detect obstacles from monitoring the aforementioned air disturbance, by analyzing the data from the drone’s gyroscope and accelerometer. Results from three experiments using the Crazyflie 2 micro drone are reported here. We show that it is possible to reliably detect when a drone is passing under another using by using data mining algorithms to recognize the air disturbance caused by the other drone.

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