Degree of Contribution

Lead

Document Type

Conference Proceeding

Keywords

Robotics, Machine Learning, Drones

Disciplines

Computer Engineering | Robotics

Abstract

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 by using data mining algorithms to recognize the air disturbance caused by the other drone.

Keywords: Drone, obstacle detection, inexpensive, data mining.

Publication Title

SPIE Defense & Secure Symposium, Unmanned Systems Technology Conference

Article Number

1078

Publication Date

Spring 4-2020

Publisher

SPIE

Language

English

Peer Reviewed

1

Version

Published

Subjects

Robotics, Data Mining

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Included in

Robotics Commons

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