Person:

Johnson, Eric N.

Permanent Link

https://hdl.handle.net/1853/72197

Associated Organization(s)

Organizational Unit

Daniel Guggenheim School of Aerospace Engineering

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Publication Search Results

Now showing 1 - 10 of 83

The Semi-Coaxial Multirotor

( 2018-05) Bershadsky, Dmitry ; Haviland, Stephen ; Johnson, Eric N.

The ”semi-coaxial” multirotor configuration is presented including its advantages over the conventional coaxial rotor configuration. The semi-coaxial configuration retains the benefits of the coaxial configuration, and additionally alleviates the loss of efficiency encountered when rotors are stacked coaxially. In addition to being more power-efficient than the standard coaxial configuration, the described configuration allows for nearly- or fully-actuated control of a multirotor when used in configurations such as the three-armed Y6 hexarotor. Using this configuration, a new Direct Force Control (DFC) multirotor is presented: the Y6sC, a specific example of the semi-coaxial multirotor. The configuration orients six rotors in a way which allows the vehicle to hover in non-zero attitudes and translate without rotating with higher efficiency than the corresponding coaxial design.
State 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.
Vision-Based Optimal Landing On a Moving Platform

(Georgia Institute of Technology, 2016-05) Nakamura, Takuma ; Haviland, Stephen ; Bershadsky, Dmitry ; Johnson, Eric N.

This paper describes a vision-based control architecture designed to enable autonomous landing on a moving platform. The landing trajectory is generated by using the receding-horizon differential dynamic programming (DDP), an optimal control method. The trajectory generation is aided by the output of a vision-based target tracking system. The vision system uses multiple extended Kalman filters which allows us to estimate the position and heading of the moving target via the observed locations. The combination of vision-based target tracking system and the receding-horizon DDP gives an unmanned aerial vehicle the capability to adaptively generate a landing trajectory against tracking errors and disturbances. Additionally, by adding the exterior penalty function to the cost of the DDP we can easily constrain the trajectory from collisions and physically infeasible solutions. We provide key mathematics needed for the implementation and share the results of the image-in-the-loop simulation and flight tests to validate the suggested methodology.
Vision-Based Closed-Loop Tracking Using Micro Air Vehicles

(Georgia Institute of Technology, 2016) Nakamura, Takuma ; Haviland, Stephen ; Bershadsky, Dmitry ; NodeIn, Daniel Magree ; Johnson, Eric N.

This paper describes the target detection and tracking architecture used by the Georgia Tech Aerial Robotics team for the American Helicopter Society (AHS) Micro Aerial Vehicle (MAV) challenge. The vision system described enables vision-aided navigation with additional abilities such as target detection and tracking all performed onboard the vehicles computer. The author suggests a robust target tracking method that does not solely depend on the image obtained from a camera, but also utilizes the other sensor outputs and runs a target location estimator. The machine learning based target identification method uses Haar-like classifiers to extract the target candidate points. The raw measurements are plugged into multiple Extended Kalman Filters (EKFs). The statistical test (Z-test) is used to bound the measurement, and solve the corresponding problem. Using Multiple EKFs allows us not only to optimally estimate the target location, but also to use the information as one of the criteria to evaluate the tracking performance. The MAV utilizes performance-based criteria that determine whether or not to initiate a maneuver such as hover or land over/on the target. The performance criteria are closed in the loop which allows the system to determine at any time whether or not to continue with the maneuver. For Vision-aided Inertial Navigation System (VINS), a corner Harris algorithm finds the feature points, and we track them using the statistical knowledge. The feature point locations are integrated in Bierman Thornton extended Kalman Filter (BTEKF) with Inertial Measurement Unit (IMU) and sonar sensor outputs to generate vehicle states: position, velocity, attitude, accelerometer and gyroscope biases. A 6- degrees-of-freedom quadrotor flight simulator is developed to test the suggested method. This paper provides the simulation results of the vision-based maneuvers: hovering over the target, and landing on the target. In addition to the simulation results, flight tests have been conducted to show and validate the system performance. The 500 gram Georgia Tech Quadrotor (GTQ)- Mini, was used for the flight tests. All processing is done onboard the vehicle and it is able to operate without human interaction. Both of the simulation and flight test results show the effectiveness of the suggested method. This system and vehicle were used for the AHS 2015 MAV Student Challenge where the GPS-denied closed-loop target search is required. The vehicle successfully found the ground target, and landed on the desired location. This paper shares the data obtained from the competition.
Efficient Approximation of Optimal High-Order Kinematic Trajectories

(Georgia Institute of Technology, 2016-01) Mooney, John ; Johnson, Eric N.

A method for efficiently planning one-dimensional pop-limited trajectories is presented, along with a direct method for synchronizing trajectories across multiple dimensions. This heuristic is designed for a double integrator utilizing acceleration commands passed through a 4th-order cascaded filter, the model for which is presented along with the system solution for an arbitrary time step and derivative limits. Examples for trajectories generated in both one and two dimensions are shown, with comparison to an iterative solver which searches for the exact optimal solution. The presented algorithm shows drastically lower computational requirements than the iterative solver, with very little cost in accuracy. Benefits and limitations of this approach are discussed.
Single-operator Multi-vehicle Human-Automation Interface Study dataset

(Georgia Institute of Technology, 2015-05) Feigh, Karen M. ; Johnson, Eric N. ; Christmann, Hans Claus

With the achievement of autonomous flight for small unmanned aircraft, currently ongoing research is expanding the capabilities of systems utilizing such vehicles for various tasks. This allows shifting the research focus from the individual systems to task execution benefits resulting from interaction and collaboration of several aircraft. Given that some available high-fidelity simulations do not yet support multi-vehicle scenarios, a multi-vehicle framework has been introduced which allows several individual single-vehicle systems to be combined into a larger multi-vehicle scenario with little to no special requirements towards the single-vehicle systems. The created multi-vehicle system offers real-time software-in-the-loop simulations of vehicle teams across multiple hosts and enables a single operator to command and control a several unmanned aircraft beyond line-of-sight in geometrically correct two-dimensional cluttered environments through a multi-hop network of data relaying intermediaries. The related dissertation by Christmann presents the main aspects of the developed system: the underlying software framework and application programming interface, the utilized inter- and intrasystem communication architecture, the graphical user interface, and implemented algorithms and operator aid heuristics to support the management and placement of the vehicles.The effectiveness of the aid heuristics is validated through a human subject study which showed that the provided operator support systems significantly improve the operators' performance in a simulated first responder scenario. This dataset contains the collected data of that human subject study.
Development of a 500 gram Vision-based Autonomous Quadrotor Vehicle Capable of Indoor Navigation

(Georgia Institute of Technology, 2015-05) Haviland, Stephen ; Bershadsky, Dmitry ; Magree, Daniel ; Johnson, Eric N.

This paper presents the work and related research done in preparation for the American Helicopter Society (AHS) Micro Aerial Vehicle (MAV) Student Challenge. The described MAV operates without human interaction in search of a ground target in an open indoor environment. The Georgia Tech Quadrotor-Mini (GTQ-Mini) weighs under 500 grams and was specifically sized to carry a high processing computer. The system platform also consists of a monocular camera, sonar, and an inertial measurement unit (IMU). All processing is done onboard the vehicle using a lightweight powerful computer. A vision navigation system generates vehicle state data and image feature estimates in a vision SLAM formation using a Bierman Thornton extended Kalman Filter (BTEKF). Simulation and flight tests have been performed to show and validate the systems performance.
A Monocular Vision-aided Inertial Navigation System with Improved Numerical Stability

(Georgia Institute of Technology, 2015-01) Magree, Daniel ; Johnson, Eric N.

This paper develops a monocular vision-aided inertial navigation system based on the factored extended Kalman filter (EKF) proposed by Bierman and Thornton. The simultaneous localization and mapping (SLAM) algorithm measurement update and propagation steps are formulated in terms of the factored covariance matrix P = UDUT, and a novel method for efficiently adding and removing features from the covariance factors is presented. The system is compared to the standard EKF formulation in navigation performance and computational requirements. The proposed method is shown to improve numerical stability with minimal impact on computational requirements. Flight test results are presented which demonstrate navigation performance with a controller in the loop.
Longitudinal Motion Planning for Slung-Loads Using Simplified Models and Rapidly-Exploring Random Trees

(Georgia Institute of Technology, 2015-01) Johnson, Eric N. ; Mooney, John G.

A randomized motion-planning approach to providing guidance for helicopters with under-slung loads is presented. Rapidly-exploring Random Trees are adapted to plan trajectories for simplified helicopter-load models. Four different planning models are tested against four different representative tasks. The poor performance of the baseline planner, and subsequent efforts to improve that performance shows the sensitivity of the RRT to proper sizing of the sampling area and amount of computation available. Further lines of potential research into optimizing planner performance and reducing computational cost are identified.
Feasibility Study to Determine the Economic and Operational Benefits of Utilizing Unmanned Aerial Vehicles (UAVs)

(Georgia Institute of Technology, 2014-05-06) Irizarry, Javier ; Johnson, Eric N.

This project explored the feasibility of using Unmanned Aerial Systems (UASs) in Georgia Department of Transportation (GDOT) operations. The research team conducted 24 interviews with personnel in four GDOT divisions. Interviews focused on (1) the basic goals of the operators in each division, (2) their major decisions for accomplishing those goals, and (3) the information requirements for each decision. Following an interview validation process, a set of UASs design characteristics that fulfill user requirements of each previously identified division was developed. A “House of Quality” viewgraph was chosen to capture the relationships between GDOT tasks and potential UAS aiding those operations. As a result, five reference systems are proposed. The UAS was broken into three components: vehicle, control station, and system. This study introduces a variety of UAS applications in traffic management, transportation and construction disciplines related to DOTs, such as the ability to get real time, digital photographs/videos of traffic scenes, providing a "bird’s eye view" that was previously only available with the assistance of a manned aircraft, integrating aerial data into GDOT drawing software programs, and dealing with restricted or complicated access issues when terrain, area, or the investigated object make it difficult for GDOT personnel to conduct a task. The results of this study could lead to further research on design, development, and field-testing of UAVs for applications identified as beneficial to the Department.