Organizational Unit:

Aerospace Design Group

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https://hdl.handle.net/1853/70743

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Organizational Unit

Daniel Guggenheim School of Aerospace Engineering

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https://finding-aids.library.gatech.edu/agents/corporate_entities/1134

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Publication Search Results

Now showing 1 - 10 of 20

Design and Implementation of a Self-configuring Ad-hoc Network for Unmanned Aerial Systems

(Georgia Institute of Technology, 2007-10) Christmann, Hans Claus ; Johnson, Eric N.

Unmanned aerial vehicles (UAVs), and unmanned aerial systems (UAS) as such in general, need wireless networks in order to communicate. UAS are very flexible and hence allow for a wide range of missions by means of utilizing different UAVs according to the mission requirements. Each of these missions also poses special needs and requirements on the communication network. Especially, mission scenarios calling for UAV swarms increase the complexity and call for specialized communication solutions. This work focuses on these specialties and needs and describes the selection process, adaptation and implementation of an ad-hoc routing protocol tailored to an UAV surrounding and a correspondingly adapted communication method.
Adaptive Neural Network Flight Control Using both Current and Recorded Data

(Georgia Institute of Technology, 2007-08) Chowdhary, Girish ; Johnson, Eric N.

Modern aerospace vehicles are expected to perform beyond their conventional flight envelopes and exhibit the robustness and adaptability to operate in uncertain environments. Augmenting proven lower level control algorithms with adaptive elements that exhibit long term learning could help in achieving better adaptation performance while performing aggressive maneuvers. The current adaptive methodologies which use Neural Network based control methods use only the instantaneous states to tune the adaptive gains. This results in a rank one limitation on the adaptive law. In this paper we propose a novel approach to adaptive control, which uses the current or the online information as well as stored or background information for adaptation. We show that using a combined online and background learning approach it is possible to overcome the rank one limitation on the adaptive law resulting in faster adaptation to the unknown dynamics. Furthermore, we show that using combined online and background learning methods it is possible to guarantee long term learning in the adaptive flight controller, which enhances performance of the controller when it encounters a maneuver that has been performed in the past. We use Lyapunov based methods for showing boundedness of all signals for a proposed method. The performance of the proposed method is evaluated in the high fidelity simulation environment for the GTMAX UAS maintained by the Georgia Tech UAV lab. The simulation results show that the proposed method exhibits long term learning and faster adaptation leading to better performance of the UAS flight controller.
Vision-based Target Tracking with Adaptive Target State Estimator

(Georgia Institute of Technology, 2007-08) Sattigeri, Ramachandra J. ; Johnson, Eric N. ; Calise, Anthony J. ; Ha, Jin-Cheol

This paper presents an approach to vision-based target tracking with a neural network (NN) augmented Kalman filter as the adaptive target state estimator. The vision sensor onboard the follower (tracker) aircraft is a single camera. Real-time image processing implemented in the onboard flight computer is used to derive measurements of relative bearing (azimuth and elevation angles) and the maximum angle subtended by the target aircraft on the image plane. These measurements are used to update the NN augmented Kalman filter. This filter generates estimates of the target aircraft position, velocity and acceleration in inertial 3D space that are used in the guidance and flight control law to guide the follower aircraft relative to the target aircraft. Applications of the presented approach include vision-based autonomous formation flight, pursuit and autonomous aerial refueling. The NN augmenting the Kalman filter estimates the target acceleration and hence provides for robust state estimation in the presence of unmodeled target maneuvers. Vision-in-the-loop simulation results obtained in a 6DOF real-time simulation of vision-based autonomous formation flight are presented to illustrate the efficacy of the adaptive target state estimator design.
Real-Time Vision-Based Relative Aircraft Navigation

(Georgia Institute of Technology, 2007-03) Johnson, Eric N. ; Calise, Anthony J. ; Watanabe, Yoko ; Ha, Jin-Cheol ; Neidhoefer, James C.

This paper describes two vision-based techniques for the navigation of an aircraft relative to an airborne target using only information from a single camera fixed to the aircraft. These techniques are motivated by problems such as "see and avoid", pursuit, formation flying, and in-air refueling. By applying an Extended Kalman Filter for relative state estimation, both the velocity and position of the aircraft relative to the target can be estimated. While relative states such as bearing can be estimated fairly easily, estimating the range to the target is more difficult because it requires achieving valid depth perception with a single camera. The two techniques presented here offer distinct solutions to this problem. The first technique, Center Only Relative State Estimation, uses optimal control to generate an optimal (sinusoidal) trajectory to a desired location relative to the target that results in accurate range-to-target estimates while making minimal demands on the image processing system.The second technique, Subtended Angle Relative State Estimation, uses more rigorous image processing to arrive at a valid range estimate without requiring the aircraft to follow a prescribed path. Simulation results indicate that both methods yield range estimates of comparable accuracy while placing different demands on the aircraft and its systems.
Flight 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.
Modeling, Control, and Flight Testing of a Small Ducted Fan Aircraft

(Georgia Institute of Technology, 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.
Adaptive Guidance and Control for Hypersonic Vehicles

(Georgia Institute of Technology, 2006-05) Johnson, Eric N. ; Calise, Anthony J. ; Curry, Michael D.

Guidance and control technology is recognized as an important aspect of the military, civil, and commercial goal of reliable, low-cost, aircraft-type operations into space. Here, several guidance and control methods are extended to enable integration into a single fully adaptive guidance and control system that offers a high degree of mission flexibility, fault tolerance, and autonomy. This paper summarizes the guidance and control system and several research issues related to use of adaptive guidance and control in reusable launch vehicles. Results that demonstrate the ability of the integrated system to plan and fly abort trajectories are also presented.
A Compact Guidance, Navigation, and Control System for Unmanned Aerial Vehicles

(Georgia Institute of Technology, 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.
Vision-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.
Estimation and Guidance Strategies for Vision-based Target Tracking

(Georgia Institute of Technology, 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.