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     Vision algorithms were implemented on an FPGA to provide additional information to supplement the insufficient data of a standard IMU in order to create a previously unrealized completely-on-board vision system for micro-UAVs. The on-board vision system is composed of an FPGA board, and a custom interface daughter-board which allow it to provide data regarding drifting movements of the micro-UAV not detected by IMUs. The algorithms implemented for the vision system include a Harris feature detector, template matching feature correlator, similarity-constrained homography by random sample consensus (RANSAC), color segmentation, radial distortion correction, and an extended Kalman filter with a standard-deviation outlier rejection technique (SORT). This vision system was designed specifically for use as an on-board vision solution for determining movement of micro-UAVs that have severe size, weight, and power limitations. Results show that the vision-system is capable of real-time on-board image processing with sufficient accuracy to allow a micro-UAV to control itself without power or data tethers to a ground station.

    Our latest work on this project 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 our Robotic Vision Lab 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.

 Graduate Students:

  Alok, Desai, Aaron Dennis, Spencer Fowers, Kirt Lillywhite, and Beau Tippetts

Publications:
  1. A. Desai, D.J. Lee, J.A. Moore, and Y.P. Chang, “Stabilization and Control of a Quad-Rotor Helicopter Using Smartphone Device," SPIE Electronic Imaging, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques, San Francisco, CA, USA, February 3-7, 2013.

  2. B.J. Tippetts, D. J. Lee, S.G. Fowers, and J.K Archibald, “Real-Time Vision Sensor for an Autonomous Hovering Micro Unmanned Aerial Vehicle,” AIAA Journal of Aerospace Computing, Information, and Communication, vol. 6, p. 570-584, October 2009.

  3. S.G. Fowers, B.J. Tippetts, D.J. Lee, and J.K. Archibald, “Vision-guided Autonomous Quad-rotor Helicopter Flight Stabilization and Control,” AUVSI's Unmanned Systems North America 2008, San Diego, CA, USA, June 10 -12, 2008.

  4. S.G. Fowers, D.J. Lee, B.J. Tippetts, K.D. Lillywhite, A.W. Dennis, and J.K. Archibald, “Vision Aided Stabilization and the Development of a Quad-Rotor Micro UAV,” The 7th IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), p. 143-148, Jacksonville, FL, USA, June 20-23, 2007.
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