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Aerospace Design Group
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ItemVisual Marker Detection In The Presence Of Colored Noise for Unmanned Aerial Vehicles(Georgia Institute of Technology, 2010-04) Shah, Syed Irtiza Ali ; Wu, Allen D. ; Johnson, Eric N.This paper develops a vision-based algorithm to detect a visual marker in real time and in the presence of excessive colored noise for Unmanned Aerial Vehicles. After using various image analysis techniques, including color histograms, filtering techniques and color space analyses, typical pixel-based characteristics of the visual marker were established. It was found that not only various color space based characteristics were significant, but also relationships between various channels across different color spaces were of great consequence. A block based search algorithm was then used to search for those established characteristics in real-time image data stream from a colored camera. A low cost noise and interference filter was also devised to handle excessive noise that was encountered during flight tests. The specific implementation scenario is that of detection of a Blue LED for GeorgiaTech's participating aircraft into the International Aerial Robotics competition. The final algorithm that was implemented on GTAR lama aircraft, used both multiple thresholding and linear confidence level calculations and was successfully used in the competition in 2009.
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ItemMethods for Localization and Mapping Using Vision and Inertial Sensors(Georgia Institute of Technology, 2008-08) Wu, Allen D. ; Johnson, Eric N.The problems of vision-based localization and mapping are currently highly active areas of research for aerial systems. With a wealth of information available in each image, vision sensors allow vehicles to gather data about their surrounding environment in addition to inferring own-ship information. However, algorithms for processing camera images are often cumbersome for the limited computational power available onboard many unmanned aerial systems. This paper therefore investigates a method for incorporating an inertial measurement unit together with a monocular vision sensor to aid in the extraction of information from camera images, and hence reduce the computational burden for this class of platforms. Feature points are detected in each image using a Harris corner detector, and these feature measurements are statistically corresponded across each captured image using knowledge of the vehicle's pose. The investigated methods employ an Extended Kalman Filter framework for estimation. Real-time hardware results are presented using a baseline configuration in which a manufactured target is used for generating salient feature points, and vehicle pose information is provided by a high precision motion capture system for comparison purposes.
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ItemGuidance, Navigation, Control, and Operator Interfaces for Small Rapid Response Unmanned Helicopters(Georgia Institute of Technology, 2008-04) Christmann, Hans Claus ; Christophersen, Henrik B. ; Wu, Allen D. ; Johnson, Eric N.This paper focuses on the development of small rapid response reconnaissance unmanned helicopters (1 to 3 kg, electric), for use by the military in urban areas and by civilian first responders, in terms of system architecture, automation (including navigation, flight control, and guidance), and operator interface designs. Design objectives include an effective user interface, a vehicle capable of smooth and precise motion control, an ability to display clear images to an operator, and a vehicle that is capable of safe and stable flight.
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ItemFlight Results of Autonomous Fixed-Wing UAV Transitions to and from Stationary Hover(Georgia Institute of Technology, 2006-08) Johnson, Eric N. ; Turbe, Michael A. ; Wu, Allen D. ; Kannan, Suresh K. ; Neidhoefer, James C.Fixed-wing unmanned aerial vehicles (UAVs) with the ability to hover have significant potential for applications in urban or other constrained environments where the combination of fast speed, endurance, and stable hovering flight can provide strategic advantages. This paper discusses the use of dynamic inversion with neural network adaptation to provide an adaptive controller capable of transitioning a fixed-wing UAV to and from hovering flight in a nearly stationary position. This approach allows utilization of the entire low speed flight envelope even beyond stall conditions. The method is applied to the GTEdge, an 8.75 foot wing span fixed-wing aerobatic UAV which has been fully instrumented for autonomous flight. Results from actual flight test experiments of the system where the airplane transitions from high speed steady flight into a stationary hover and then back are presented.