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 11

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.
Monocular 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.
Georgia Tech Team Entry for the 2013 AUVSI International Aerial Robotics Competition

(Georgia Institute of Technology, 2013-08) Magree, Daniel ; Bershadsky, Dmitry ; Costes, Chris ; Haviland, Stephen ; Sanz, David ; Kim, Eric ; Valdez, Pierre ; Dyer, Timothy ; Johnson, Eric N.
Improving Uniform Ultimate Bounded Response of Neuroadaptive Control Approaches Using Command Governors

(Georgia Institute of Technology, 2013-08) Magree, Daniel ; Yucelen, Tansel ; Johnson, Eric N.

In this paper, we develop a command governor-based architecture in order to improve the response of neuroadaptive control approaches. Specifically, a command governor is a linear dynamical system that modifies a given desired command to improve transient and steady-state performance of uncertain dynamical systems. It is shown that as the command governor gain is increased, the neuroadaptive system converges to the linear reference system. Simulation results are used to validate the effectiveness of the proposed framework.
Performance of a Monocular Vision-aided Inertial Navigation System for a Small UAV

(Georgia Institute of Technology, 2013-08) Magree, Daniel ; Johnson, Eric N.

The use of optical sensors for navigation on aircraft has receive much attention recently. Optical sensors provide a wealth of information about the environment and are standard payloads for many unmanned aerial vehicles (UAVs). Simultaneous localization and map- ping (SLAM) algorithms using optical sensors have become computationally feasible in real time in the last ten years. However, implementations of visual SLAM navigation systems on aerial vehicles are still new and consequently are often limited to restrictive environ- ments or idealized conditions. One example of a ight condition which can dramatically a ect navigation performance is altitude. This paper seeks to examine the performance of monocular extended Kalman lter based SLAM (EKF-SLAM) navigation over a large altitude change. Simulation data is collected which illustrates the behavior of the naviga- tion system over the altitude range. Navigation and control system parameters values are speci ed which improve vehicle performance across the ight conditions. Additionally, a detailed presentation of the monocular EKF-SLAM navigation system is given. Flight test results are presented on a quadrotor.
GPS-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, Andy

GPS-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.
Monocular Visual Mapping for Obstacle Avoidance on UAVs

(Georgia Institute of Technology, 2013-05) 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 and results from recorded flight test data are presented to validate the algorithm.
Georgia Tech Team Entry for the 2012 AUVSI International Aerial Robotics Competition

(Georgia Institute of Technology, 2012-08) Magree, Daniel ; Bershadsky, Dmitry ; Wang, Xo ; Valdez, Pierre ; Antico, Jason ; Coder, Ryan ; Dyer, Timothy ; George, Eohan ; Johnson, Eric N.

This paper describes the details of a Quadrotor Unmanned Aerial Vehicle capable of exploring cluttered indoor areas without relying on any external navigational aids. A Simultaneous Localization and Mapping (SLAM) algorithm is used to fuse information from a laser range sensor, an inertial measurement unit, and an altitude sonar to provide relative position, velocity, and attitude information. A wall avoidance and guidance system is implemented to ensure that the vehicle explores maximum indoor area. A model reference adaptive control architecture is used to ensure stability and mitigation of uncertainties. Finally, an object detection system is implemented to identify target objects for retrieval.
Command Governor-Based Adaptive Control of an Autonomous Helicopter

(Georgia Institute of Technology, 2012-08) Magree, Daniel ; Yucelen, Tansel ; Johnson, Eric N.

This paper presents an application of a recently developed command governor-based adaptive control framework to a high-fidelity autonomous helicopter model. This framework is based on an adaptive controller, but the proposed command governor adjusts the trajectories of a given command in order to follow an ideal reference system (capturing a desired closed-loop system behavior) both in transient-time and steady-state without resorting to high-gain learning rates in the adaptation (update) law. The high-fidelity autonomous helicopter is a six rigid body degree of freedom model, with additional engine, fuel and rotor dynamics. Non-ideal attributes of physical systems such as model uncertainty, sensor noise, and actuator dynamics are modeled to evaluate the command governor controller in realistic conditions. The proposed command governor adaptive control framework is shown to reduce attitude error with respect to a standard adaptive control scheme during vehicle maneuvers.