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Aerospace Design Group

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End date: 2006 × Start date: 2003 × search.filters.applied.f.isOrgUnitOfPublicationTitle: Aerospace Design Group × search.filters.applied.f.isOrgUnitOfPublicationTitle: Unmanned Aerial Vehicle Research Facility ×

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Now showing 1 - 9 of 9
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Flight Results of Autonomous Fixed-Wing UAV Transitions to and from Stationary Hover

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.

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Estimation and Guidance Strategies for Vision-based Target Tracking

2005-06 , Calise, Anthony J. , Johnson, Eric N. , Sattigeri, Ramachandra J. , Watanabe, Yoko , Madyastha, Venkatesh

This paper discusses estimation and guidance strategies for vision-based target tracking. Specific applications include formation control of multiple unmanned aerial vehicles (UAVs) and air-to-air refueling. We assume that no information is communicated between the aircraft, and only passive 2-D vision information is available to maintain formation. To improve the robustness of the estimation process with respect to unknown target aircraft acceleration, the nonlinear estimator (EKF) is augmented with an adaptive neural network (NN). The guidance strategy involves augmenting the inverting solution of nonlinear line-of-sight (LOS) range kinematics with the output of an adaptive NN to compensate for target aircraft LOS velocity. Simulation results are presented that illustrate the various approaches.

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Small Adaptive Flight Control Systems for UAVs Using FPGA/DSP Technology

2004-09 , Christophersen, Henrik B. , Pickell, Wayne J. , Koller, Adrian A. , Kannan, Suresh K. , Johnson, Eric N.

Future small UAVs will require enhanced capabilities like seeing and avoiding obstacles, tolerating unpredicted flight conditions, interfacing with payload sensors, tracking moving targets, and cooperating with other manned and unmanned systems. Cross-platform commonality to simplify system integration and training of personnel is also desired. A small guidance, navigation, and control system has been developed and tested. It employs Field Programmable Gate Array (FPGA) and Digital Signal Processor (DSP) technology to satisfy the requirements for more advanced vehicle behavior in a small package. Having these two processors in the system enables custom vehicle interfacing and fast sequential processing of high-level control algorithms. This paper focuses first on the design aspects of the hardware and the low-level software. Discussion of flight test experience with the system controlling both an unmanned helicopter and an 11-inch ducted fan follow.

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Modeling, Control, and Flight Testing of a Small Ducted Fan Aircraft

2006-07 , Johnson, Eric N. , Turbe, Michael A.

Small ducted fan autonomous vehicles have potential for several applications, especially for missions in urban environments. This paper discusses the use of dynamic inversion with neural network adaptation to provide an adaptive controller for the GTSpy, a small ducted fan autonomous vehicle based on the Micro Autonomous Systems' Helispy. This approach allows utilization of the entire low speed flight envelope with a relatively poorly understood vehicle. A simulator model is constructed from a force and moment analysis of the vehicle, allowing for a validation of the controller in preparation for flight testing. Data from flight testing of the system is provided.

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Adaptive Trajectory Control for Autonomous Helicopters

2005 , Johnson, Eric N. , Kannan, Suresh K.

For autonomous helicopter flight, it is common to separate the flight control problem into an inner loop that controls attitude and an outer loop that controls the translational trajectory of the helicopter. In previous work, dynamic inversion and neural-network-based adaptation was used to increase performance of the attitude control system and the method of pseudocontrol hedging (PCH) was used to protect the adaptation process from actuator limits and dynamics. Adaptation to uncertainty in the attitude, as well as the translational dynamics, is introduced, thus, minimizing the effects of model error in all six degrees of freedom and leading to more accurate position tracking. The PCH method is used in a novel way that enables adaptation to occur in the outer loop without interacting with the attitude dynamics. A pole-placement approach is used that alleviates timescale separation requirements, allowing the outer-loop bandwidth to be closer to that of the inner loop, thus, increasing position tracking performance. A poor model of the attitude dynamics and a basic kinematics model is shown to be sufficient for accurate position tracking. The theory and implementation of such an approach, with a summary of flight-test results, are described.

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Vision-Only Aircraft Flight Control

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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A Compact Guidance, Navigation, and Control System for Unmanned Aerial Vehicles

2006-05 , Christophersen, Henrik B. , Pickell, R. Wayne , Neidhoefer, James C. , Koller, Adrian A. , Kannan, Suresh K. , Johnson, Eric N.

The Flight Control System 20 (FCS20) is a compact, self-contained Guidance, Navigation, and Control system that has recently been developed to enable advanced autonomous behavior in a wide range of Unmanned Aerial Vehicles (UAVs). The FCS20 uses a floating point Digital Signal Processor (DSP) for high level serial processing, a Field Programmable Gate Array (FPGA) for low level parallel processing, and GPS and Micro Electro Mechanical Systems (MEMS) sensors. In addition to guidance, navigation, and control functions, the FCS20 is capable of supporting advanced algorithms such as automated reasoning, artificial vision, and multi-vehicle interaction. The unique contribution of this paper is that it gives a complete overview of the FCS20 GN&C system, including computing, communications, and information aspects. Computing aspects of the FCS20 include details about the design process, hardware components, and board configurations, and specifications. Communications aspects of the FCS20 include descriptions of internal and external data flow. The information section describes the FCS20 Operating System (OS), the Support Vehicle Interface Library (SVIL) software, the navigation Extended Kalman Filter, and the neural network based adaptive controller. Finally, simulation-based results as well as actual flight test results that demonstrate the operation of the guidance, navigation, and control algorithms on a real Unmanned Aerial Vehicle (UAV) are presented.

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Approaches to Vision-Based Formation Control

2004-12 , Johnson, Eric N. , Calise, Anthony J. , Sattigeri, Ramachandra J. , Watanabe, Yoko , Madyastha, Venkatesh

This paper implements several methods for performing vision-based formation flight control of multiple aircraft in the presence of obstacles. No information is communicated between aircraft, and only passive 2-D vision information is available to maintain formation. The methods for formation control rely either on estimating the range from 2-D vision information by using Extended Kalman Filters or directly regulating the size of the image subtended by a leader aircraft on the image plane. When the image size is not a reliable measurement, especially at large ranges, we consider the use of bearing-only information. In this case, observability with respect to the relative distance between vehicles is accomplished by the design of a time-dependent formation geometry. To improve the robustness of the estimation process with respect to unknown leader aircraft acceleration, we augment the EKF with an adaptive neural network. 2-D and 3-D simulation results are presented that illustrate the various approaches.

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Development of an Autonomous Aerial Reconnaissance System at Georgia Tech

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.

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