Title

Quadcopter Proximity Detection by Air Disturbance Analysis

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Presentation for SPIE Defense and Security Symposium

Umanned Systems Technology Conference April 2020

Disciplines

Artificial Intelligence and Robotics

Abstract

The use of unmanned aerial vehicles (drones) is expanding to commercial, scientific, and agriculture applications, such as surveillance, product deliveries and aerial photography etc. One challenge for applications of drones is detecting obstacles and avoiding collisions. A typical solution to this issue is the use of camera sensor, ultrasonic sensor 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 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 the aforementioned air disturbance by analyzing the data from the 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 one or more other drones using by using machine learning algorithms to recognize the air disturbance caused by the other drones. Keywords: Drone, obstacle detection, inexpensive, data mining.

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