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
Recommended Citation
Zhao, Qian and Hughes, Jason, "DRONE PROXIMITY DETECTION VIA AIR DISTURBANCE ANALYSIS" (2020). Faculty Publications. 69.
https://research.library.fordham.edu/frcv_facultypubs/69
Version
Published
Subjects
Robotics, Data Mining
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
