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ItemVision-Aided Inertial Navigation for Flight Control(Georgia Institute of Technology, 2005-09) Wu, Allen D. ; Johnson, Eric N. ; Proctor, Alison A.Many onboard navigation systems use the Global Positioning System to bound the errors that result from integrating inertial sensors over time. Global Positioning System information, however, is not always accessible since it relies on external satellite signals. To this end, a vision sensor is explored as an alternative for inertial navigation in the context of an Extended Kalman Filter used in the closed-loop control of an unmanned aerial vehicle. The filter employs an onboard image processor that uses camera images to provide information about the size and position of a known target, thereby allowing the flight computer to derive the target's pose. Assuming that the position and orientation of the target are known a priori, vehicle position and attitude can be determined from the fusion of this information with inertial and heading measurements. Simulation and flight test results verify filter performance in the closed-loop control of an unmanned rotorcraft.
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ItemVisual Search Automation for Unmanned Aerial Vehicles(Georgia Institute of Technology, 2005-01) Johnson, Eric N. ; Proctor, Alison A. ; Ha, Jin-Cheol ; Tannenbaum, Allen R.This paper describes the design, development, and testing of an Unmanned Aerial Vehicle (UAV) with automated capabilities: searching a prescribed area, identifying a specific building within that area based on a small sign located on one wall, and then identifying an opening into that building. This includes a description of the automated search system along with simulation and flight test results. Results include successful evaluation at the McKenna Military Operations in Urban Terrain flight test site.
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ItemDevelopment and Test of Highly Autonomous Unmanned Aerial Vehicles(Georgia Institute of Technology, 2004-12) Johnson, Eric N. ; Proctor, Alison A. ; Ha, Jin-Cheol ; Tannenbaum, Allen R.This paper describes the design, development, and testing of Unmanned Aerial Vehicles (UAV) with highly automated search capabilities. Here, systems are able to respond on their own in the presence of considerable uncertainty utilizing an image processor, tracker/mapper, mission manager, and trajectory generation; and are used to complete a realistic benchmark reconnaissance mission. Subsequent to the selection of the search area, all functions are automated and human operator assistance is not required. The applications of these capabilities include reduction of operator workload in operational UAV systems, new UAV or guided-munition missions conducted without the assistance or availability of human operators, or the enhancement/augmentation of human search capabilities. The resulting system was able to search the 15-building village automatically with speed comparable to a human operator searching on foot or with a conventional remotely piloted vehicle. It was successful in 6 of 7 actual flights over the McKenna Military Operations in Urban Terrain test site over two different days and a variety of lighting conditions and choice of desired building.
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ItemVision-Only Aircraft Flight Control(Georgia Institute of Technology, 2003-10) De Wagter, Christophe ; Proctor, Alison A. ; Johnson, Eric N.Building aircraft with navigation and control systems that can complete flight tasks is complex, and often involves integrating information from multiple sensors to estimate the state of the vehicle. This paper describes a method, in which a glider can fly from a starting point to a predetermined end location (target) precisely using vision only. Using vision to control an aircraft represents a unique challenge, partially due to the high rate of images required in order to maintain tracking and to keep the glider on target in a moving air mass. Second, absolute distance and angle measurements to the target are not readily available when the glider does not have independent measurements of its own position. The method presented here uses an integral image representation of the video input for the analysis. The integral image, which is obtained by integrating the pixel intensities across the image, is reduced to a probable target location by performing a cascade of feature matching functions. The cascade is designed to eliminate the majority of the potential targets in a first pruning using computationally inexpensive process. Then, the more exact and computationally expensive processes are used on the few remaining candidates; thereby, dramatically decreasing the processing required per image. The navigation algorithms presented in this paper use a Kalman filter to estimate attitude and glideslope required based on measurements of the target in the image. The effectiveness of the algorithms is demonstrated through simulation of a small glider instrumented with only a simulated camera.
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ItemDevelopment of an Autonomous Aerial Reconnaissance System at Georgia Tech(Georgia Institute of Technology, 2003-07) Proctor, Alison A. ; Kannan, Suresh K. ; Raabe, Chris ; Christophersen, Henrik B. ; Johnson, Eric N.The Georgia Tech aerial robotics team has developed a system to compete in the International Aerial Robotics Competition, organized by the Association for Unmanned Vehicle Systems, International. The team is a multi-disciplinary group of students who have developed a multi-year strategy to complete all levels and the overall mission. The approach taken to achieve the objectives of the required missions has evolved to incorporate new ideas and lessons learned. This document summarizes the approach taken, the current status of the project, and the design of the components and subsystems.