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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Now showing 1 - 10 of 24

A 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.
Collaborative Mapping and Search for Autonomous Helicopters

(Georgia Institute of Technology, 2013-05) Johnson, Eric N. ; Magree, Daniel P. ; Mooney, John G. ; White, Matthew ; Hartman, Jonathan ; Sahasrabudhe, Vineet

This paper describes recent results from a partnership between the Sikorsky Aircraft Corporation and the Georgia Institute of Technology to develop, improve, and flight test a multi-aircraft collaborative architecture, focused on decentralized autonomous decision-making. The architecture includes a finite-state machine, Voronoi mapping strategy, and real-time information sharing system designed to solve a challenge problem. The architecture was implemented on a pair of Yamaha RMax helicopters outfitted with modular avionics, as well as an associated set of simulation tools. Simulation results for single- and multiple-aircraft scenarios are presented, along with a quadratic relationship between mapping speed and task completion time. Further work suggested includes validation of simulation results in flight test with two real aircraft, as well as further exploration between search problem parameters and theoretical optimal performance.
Autonomous Flight in GPS-Denied Environments Using Monocular Vision and Inertial Sensors

(Georgia Institute of Technology, 2013-04) Wu, Allen D. ; Johnson, Eric N. ; Kaess, Michael ; Dellaert, Frank ; Chowdhary, Girish

A vision-aided inertial navigation system that enables autonomous flight of an aerial vehicle in GPS-denied environments is presented. Particularly, feature point information from a monocular vision sensor are used to bound the drift resulting from integrating accelerations and angular rate measurements from an Inertial Measurement Unit (IMU) forward in time. An Extended Kalman filter framework is proposed for performing the tasks of vision-based mapping and navigation separately. When GPS is available, multiple observations of a single landmark point from the vision sensor are used to estimate the point’s location in inertial space. When GPS is not available, points that have been sufficiently mapped out can be used for estimating vehicle position and attitude. Simulation and flight test results of a vehicle operating autonomously in a simplified loss-of-GPS scenario verify the presented method.
Terrain Height Evidence Sharing for Collaborative Autonomous Rotorcraft Operation

(Georgia Institute of Technology, 2013-01) Johnson, Eric N. ; Mooney, John G. ; White, Matthew ; Hartman, Jonathan ; Sahasrabudhe, Vineet

This paper describes recent 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 information sharing system to support collaborative autonomy and high performance nap-of-the-Earth helicopter flight. The emphasis here is on smart and selective sharing of terrain data which (1) minimizes the bandwidth consumed by obstacle/terrain-information-sharing between aircraft, (2) assigns an appropriate level of confidence to the data received from other heterogeneous aircraft, (3) is robust to sensor error and failures, and (4) is robust to entry and exit of vehicles from the network. Results from simulation and flight testing are provided.
Self-Contained Autonomous Indoor Flight with Ranging Sensor Navigation

(Georgia Institute of Technology, 2012-11) Chowdhary, Girish ; Sobers, D. Michael, Jr. ; Pravitra, Chintasid ; Christmann, Hans Claus ; Wu, Allen ; Hashimoto, Hiroyuki ; Ong, Chester ; Kalghatgi, Roshan ; Johnson, Eric N.

This paper describes the design and flight test of a completely self-contained autonomous indoor Miniature Unmanned Aerial System (M-UAS). Guidance, navigation, and control algorithms are presented, enabling the M-UAS to autonomously explore cluttered indoor areas without relying on any off-board computation or external navigation aids such as GPS. The system uses a scanning laser rangefinder and a streamlined Simultaneous Localization and Mapping (SLAM) algorithm to provide a position and heading estimate, which is combined with other sensor data to form a six degree-of-freedom inertial navigation solution. This enables an accurate estimate of the vehicle attitude, relative position, and velocity. The state information, with a self-generated map, is used to implement a frontier-based exhaustive search of an indoor environment. Improvements to existing guidance algorithms balance exploration with the need to remain within sensor range of indoor structures such that the SLAM algorithm has sufficient information to form a reliable position estimate. A dilution of precision metric is developed to quantify the effect of environment geometry on the SLAM pose covariance, which is then used to update the 2-D position and heading in the navigation filter. Simulation and flight test results validate the presented algorithms.
Flight Testing of Nap of-the-Earth Unmanned Helicopter Systems

(Georgia Institute of Technology, 2011-05) Johnson, Eric N. ; Mooney, John G. ; Ong, Chester ; Sahasrabudhe, Vineet ; Hartman, Jonathan

This paper describes recent 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 helicopter flight. The emphasis here is on optimization for a combination of low height above terrain/obstacles and high speeds. Multiple methods for generating the desired flight path were evaluated, including (1) a simple processing of each laser scan; and (2) a potential field based method. Simulation and flight test results have been obtained utilizing an onboard laser scanner to detect terrain and obstacles while flying at low altitude, and have successfully demonstrated obstacle avoidance in a realistic semi-urban environment at speeds up to 40 ft/s while maintaining a miss distance of 50 ft horizontally and vertically. These results indicate that the technical approach is sound, paving the way for testing of even lower altitudes, higher speeds, and more aggressive maneuvering in future work.
High Performance Nap-of-the-Earth Unmanned Helicopter Flight

(Georgia Institute of Technology, 2011) Johnson, Eric N. ; Mooney, John G. ; Ong, Chester ; Sahasrabudhe, Vineet ; Hartman, Jonathan

This paper describes recent 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 helicopter flight. The emphasis here is on optimization for a combination of low height above terrain/obstacles and high speeds. Multiple methods for generating the desired flight path were evaluated, including (1) a simple processing of each laser scan; and (2) a potential field based method. Simulation and flight test results have been obtained utilizing an onboard laser scanner to detect terrain and obstacles while flying at low altitude, and have successfully demonstrated obstacle avoidance at speeds up to 40 ft/s while maintaining a miss distance of 50 ft horizontally and vertically. These results indicate that the technical approach is sound, paving the way for testing of even lower altitudes, higher speeds, and more aggressive maneuvering in future work.
Indoor Navigation for Unmanned Aerial Vehicles

(Georgia Institute of Technology, 2009-08) Sobers, D. Michael Jr. ; Chowdhary, Girish ; Johnson, Eric N.

The ability for vehicles to navigate unknown environments is critical for autonomous operation. Mapping of a vehicle's environment and self-localization within that environment are especially difficult for an Unmanned Aerial Vehicle (UAV) due to the complexity of UAV attitude and motion dynamics, as well as interference from external influences such as wind. By using a stable vehicle platform and taking advantage of the geometric structure typical of most indoor environments, the complexity of the localization and mapping problem can be reduced. Interior wall and obstacle location can be measured using low-cost range sensors. Relative vehicle location within the mapped environment can then be determined. By alternating between mapping and localization, a vehicle can explore its environment autonomously. This paper examines available low-cost range sensors for suitability in solving the mapping and localization problem. A control system and navigation algorithm are developed to perform mapping of indoor environments and localization. Simulation and experimental results are provided to determine feasibility of the proposed approach to indoor navigation.
A Process to Obtain Robustness Metrics for Adaptive Flight Controllers

(Georgia Institute of Technology, 2009-08) Kimbrell, Scott ; Johnson, Eric N. ; Chowdhary, Girish ; Calise, Anthony J. ; Chandramohan, Rajeev

This research effort seeks a process to draw parallels between the classical stability metrics of gain and phase margins for classical linear control systems with stability margins for adaptive controllers. The method uses a Monte Carlo simulation to yield stability threshold results for the adaptive controller based on problem-specific performance metrics. By fitting a linear controller's analytical robustness results to the adaptive stability data, the gain and phase margin for the performance-fitting linear system are considered to be the worst case equivalent gain and phase margin for the adaptive controller. This paper also discusses some experiences successfully obtaining time delay margin in a flight test setting.
Flight Test Results of Autonomous Fixed-Wing Transition to and from Stationary Hover

(Georgia Institute of Technology, 2008-03) Johnson, Eric N. ; Turbe, Michael A. ; Wu, Allen D. ; Kannan, Suresh K. ; Neidhoefer, James C.

Linear systems can be used to adequately model and control an aircraft in either ideal steady-level flight or in ideal hovering flight. However, constructing a single unified system capable of adequately modeling or controlling an airplane in steady-level flight and in hovering flight, as well as during the highly nonlinear transitions between the two, requires the use of more complex systems, such as scheduled-linear, nonlinear, or stable adaptive systems. This paper discusses the use of dynamic inversion with real-time neural network adaptation as a means to provide a single adaptive controller capable of controlling a fixed-wing unmanned aircraft system in all three flight phases: steadylevel flight, hovering flight, and the transitions between them. Having a single controller that can achieve and transition between steady-level and hovering flight allows utilization of the entire low-speed flight envelope, even beyond stall conditions. This method is applied to the GTEdge, an eight-foot wingspan, fixed-wing unmanned aircraft system that has been fully instrumented for autonomous flight. This paper presents data from actual flighttest experiments in which the airplane transitions from high-speed, steady-level flight into a hovering condition and then back again.