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Johnson,
Eric N.
Johnson,
Eric N.
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ItemState Estimation using Gaussian Process Regression for Colored Noise Systems(Georgia Institute of Technology, 2017-06) Lee, Kyuman ; Johnson, Eric N.The goal of this study is to use Gaussian process (GP) regression models to estimate the state of colored noise systems. The derivation of a Kalman filter assumes that the process noise and measurement noise are uncorrelated and both white. In relaxing those assumptions, the Kalman filter equations were modified to deal with the non-whiteness of each noise source. The standard Kalman filter ran on an augmented system that had white noises and other approaches were also introduced depending on the forms of the noises. Those existing methods can only work when the characteristics of the colored noise are perfectly known. However, it is usually difficult to model a noise without additional knowledge of the noise statistics. When the parameters of colored noise models are totally unknown and the functions of each underlying model (nonlinear dynamic and measurement functions) are uncertain or partially known, filtering using GP-Color models can perform regardless of whatever forms of colored noise. The GPs can learn the residual outputs between the GP models and the approximate parametric models (or between actual sensor readings and predicted measurement readings), as a member of a distribution over functions, typically with a mean and covariance function. Lastly, a series of simulations, including Monte Carlo results, will be run to compare the GP based filtering techniques with the existing methods to handle the sequentially correlated noise.
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ItemA Comparison of Automatic Nap-of-the-Earth Guidance Strategies for Helicopters(Georgia Institute of Technology, 2014-05) Johnson, Eric N. ; Mooney, John G.This paper describes updated results from a partnership between the Sikorsky Aircraft Corporation and the Georgia Institute of Technology to develop, improve, and flight test a sensor, guidance, navigation, control, and real-time flight path optimization system to support high performance Nap-of-the-Earth (NOE) helicopter flight.
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ItemMonocular Visual Mapping for Obstacle Avoidance on UAVs(Georgia Institute of Technology, 2014-01) Magree, Daniel ; Mooney, John G. ; Johnson, Eric N.An unmanned aerial vehicle requires adequate knowledge of its surroundings in order to operate in close proximity to obstacles. UAVs also have strict payload and power constraints which limit the number and variety of sensors available to gather this information. It is desirable, therefore, to enable a UAV to gather information about potential obstacles or interesting landmarks using common and lightweight sensor systems. This paper presents a method of fast terrain mapping with a monocular camera. Features are extracted from camera images and used to update a sequential extended Kalman filter. The features locations are parameterized in inverse depth to enable fast depth convergence. Converged features are added to a persistent terrain map which can be used for obstacle avoidance and additional vehicle guidance. Simulation results, results from recorded flight test data, and flight test results are presented to validate the algorithm.
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ItemGPS-denied Indoor and Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraft(Georgia Institute of Technology, 2013-05) Chowdhary, Girish ; Johnson, Eric N. ; Magree, Daniel ; Wu, Allen ; Shein, AndyGPS-denied closed-loop autonomous control of unstable Unmanned Aerial Vehicles (UAVs) such as rotorcraft using information from a monocular camera has been an open problem. Most proposed Vision aided Inertial Navigation Systems (V-INSs) have been too computationally intensive or do not have sufficient integrity for closed-loop flight. We provide an affirmative answer to the question of whether V-INSs can be used to sustain prolonged real-world GPS-denied flight by presenting a V-INS that is validated through autonomous flight-tests over prolonged closed-loop dynamic operation in both indoor and outdoor GPS-denied environments with two rotorcraft unmanned aircraft systems (UASs). The architecture efficiently combines visual feature information from a monocular camera with measurements from inertial sensors. Inertial measurements are used to predict frame-to-frame transition of online selected feature locations, and the difference between predicted and observed feature locations is used to bind in real-time the inertial measurement unit drift, estimate its bias, and account for initial misalignment errors. A novel algorithm to manage a library of features online is presented that can add or remove features based on a measure of relative confidence in each feature location. The resulting V-INS is sufficiently efficient and reliable to enable real-time implementation on resource-constrained aerial vehicles. The presented algorithms are validated on multiple platforms in real-world conditions: through a 16-min flight test, including an autonomous landing, of a 66 kg rotorcraft UAV operating in an unconctrolled outdoor environment without using GPS and through a Micro-UAV operating in a cluttered, unmapped, and gusty indoor environment.
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ItemDevelopment and Evaluation of an Automated Path Planning Aid(Georgia Institute of Technology., 2012-11) Watts, Robert ; Christmann, Hans Claus ; Johnson, Eric N. ; Feigh, Karen M. ; Tsiotras, PanagiotisHandling en route emergencies in modern transport aircraft through adequate teamwork between the pilot, the crew and the aircraft’s automation systems is an ongoing and active field of research. An automated path planning aid tool can assist pilots with the tasks of selecting a convenient landing site and developing a safe path to land at this site in the event of an onboard emergency. This paper highlights the pilot evaluation results of a human factors study as part of such a proposed automated planning aid. Focusing on the interactions between the pilot and the automated planning aid, the presented results suggest that a particular implementation of the pilot aid interface, which uses a simple dial to sort the most promising landing sites, was effective. This selectable sorting capability, motivated by the anticipated cognitive mode of the pilot crew, improved the quality of the selected site for the majority of the cases tested. Although the presented approach increased the average time required for the selection of an alternate landing site, it decreased the time to complete the task in the case of emergencies unfamiliar to the pilot crew.
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ItemNetwork Discovery: An Estimation Based Approach(Georgia Institute of Technology, 2011-06) Chowdhary, Girish ; Egerstedt, Magnus B. ; Johnson, Eric N.We consider the unaddressed problem of network discovery, in which, an agent attempts to formulate an estimate of the global network topology using only locally available information. We show that under two key assumptions, the network discovery problem can be cast as a parameter estimation problem. Furthermore, we show that some form of excitation must be present in the network to be able to converge to a solution. The performance of two methods for solving the network discovery problem is evaluated in simulation.
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ItemSystem Integration and Operation of a Research Unmanned Aerial Vehicle(Georgia Institute of Technology, 2004-01) Johnson, Eric N. ; Schrage, Daniel P.The use of flight simulation tools to reduce the schedule, risk, and required amount of flight-testing for complex aerospace systems is a well-recognized benefit of these approaches. However, some special challenges arise when one attempts to obtain these benefits for the development and operation of a research Uninhabited Aerial Vehicle (UAV) system. Research UAV systems are characterized by the need for continual checkout of experimental software and hardware. Also, flight-testing can be further leveraged by complementing experimental results with flight-test validated simulation results for the same vehicle system. In this paper, flight simulation architectures for system design, integration, and operation of an experimental helicopter-based UAV, are described. The chosen helicopter-based UAV platform (a Yamaha RMax) is well instrumented: differential GPS, an inertial measurement unit, sonar altimetry, and a 3-axis magnetometer. One or two general-purpose flight processors can be utilized. Research flight test results obtained to date, including those completed in conjunction with the DARPA Software Enabled Control program, are summarized.