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Proceedings Paper

Stabilization and control of quad-rotor helicopter using a smartphone device
Author(s): Alok Desai; Dah-Jye Lee; Jason Moore; Yung-Ping Chang
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Paper Abstract

In recent years, autonomous, micro-unmanned aerial vehicles (micro-UAVs), or more specifically hovering micro- UAVs, have proven suitable for many promising applications such as unknown environment exploration and search and rescue operations. The early versions of UAVs had no on-board control capabilities, and were difficult for manual control from a ground station. Many UAVs now are equipped with on-board control systems that reduce the amount of control required from the ground-station operator. However, the limitations on payload, power consumption and control without human interference remain the biggest challenges. This paper proposes to use a smartphone as the sole computational device to stabilize and control a quad-rotor. The goal is to use the readily available sensors in a smartphone such as the GPS, the accelerometer, the rate-gyros, and the camera to support vision-related tasks such as flight stabilization, estimation of the height above ground, target tracking, obstacle detection, and surveillance. We use a quad-rotor platform that has been built in the Robotic Vision Lab at Brigham Young University for our development and experiments. An Android smartphone is connected through the USB port to an external hardware that has a microprocessor and circuitries to generate pulse-width modulation signals to control the brushless servomotors on the quad-rotor. The high-resolution camera on the smartphone is used to detect and track features to maintain a desired altitude level. The vision algorithms implemented include template matching, Harris feature detector, RANSAC similarity-constrained homography, and color segmentation. Other sensors are used to control yaw, pitch, and roll of the quad-rotor. This smartphone-based system is able to stabilize and control micro-UAVs and is ideal for micro-UAVs that have size, weight, and power limitations.

Paper Details

Date Published: 4 February 2013
PDF: 9 pages
Proc. SPIE 8662, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques, 866208 (4 February 2013); doi: 10.1117/12.2013703
Show Author Affiliations
Alok Desai, Brigham Young Univ. (United States)
Dah-Jye Lee, Brigham Young Univ. (United States)
Jason Moore, Brigham Young Univ. (United States)
Yung-Ping Chang, Brigham Young Univ. (United States)


Published in SPIE Proceedings Vol. 8662:
Intelligent Robots and Computer Vision XXX: Algorithms and Techniques
Juha Röning; David Casasent, Editor(s)

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