Organizational Unit:

Daniel Guggenheim School of Aerospace Engineering

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https://hdl.handle.net/1853/70742

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College of Engineering

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Aerospace Design Group

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Aerospace Systems Design Laboratory (ASDL)

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Air Transportation Laboratory

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Cognitive Engineering Center

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Space Systems Design Laboratory (SSDL)

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https://finding-aids.library.gatech.edu/agents/corporate_entities/106

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Publication Search Results

Now showing 1 - 10 of 38

Development 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, Panagiotis

Handling 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.
Advanced methods for intelligent flight guidance and planning in support of pilot decision making

(Georgia Institute of Technology, 2012-03-01) Tsiotras, Panagiotis ; Johnson, Eric N.
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.
Methodology to support dynamic function allocation polices between humans and flight deck automation

(Georgia Institute of Technology, 2010-09-30) Johnson, Eric N.
Modeling Urban Environments for Communication-Aware UAV Swarm Path Planning

(Georgia Institute of Technology, 2010-08) Christmann, Hans Claus ; Johnson, Eric N.

The presented work introduces a graph based approach to model urban (or otherwise cluttered) environments for UAS utilization beyond line-of-sight as well as out of direct R/F range of the operator's control station. Making the assumption that some a priori data of the environment is available, the proposed method uses a classification of obstacles with respect to their impact on UAV motion and R/F communication and generates continuously updateable graphs usable to compute traverseable paths for UAVs while maintaining R/F communication. Using a simulated urban scenario this work shows that the proposed modeling method allows to find reachable loiter or hover areas for UAVs in order to establish a multi-hop R/F communication link between a primary UAV and its remote operator by utilizing an overlay of motion (Voronoi based) and R/F (visibility based) specific mapping methods.
Visual Marker Detection In The Presence Of Colored Noise for Unmanned Aerial Vehicles

(Georgia Institute of Technology, 2010-04) Shah, Syed Irtiza Ali ; Wu, Allen D. ; Johnson, Eric N.

This paper develops a vision-based algorithm to detect a visual marker in real time and in the presence of excessive colored noise for Unmanned Aerial Vehicles. After using various image analysis techniques, including color histograms, filtering techniques and color space analyses, typical pixel-based characteristics of the visual marker were established. It was found that not only various color space based characteristics were significant, but also relationships between various channels across different color spaces were of great consequence. A block based search algorithm was then used to search for those established characteristics in real-time image data stream from a colored camera. A low cost noise and interference filter was also devised to handle excessive noise that was encountered during flight tests. The specific implementation scenario is that of detection of a Blue LED for GeorgiaTech's participating aircraft into the International Aerial Robotics competition. The final algorithm that was implemented on GTAR lama aircraft, used both multiple thresholding and linear confidence level calculations and was successfully used in the competition in 2009.
Flight Test Validation of a Neural Network based Long Term Learning Adaptive Flight Controller

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

The purpose of this paper is to present and analyze flight test results of a Long Term Learning Adaptive Flight Controller implemented on a rotorcraft and a fixed wing Unmanned Aerial Vehicle. The adaptive control architecture used is based on a proven Model Reference Adaptive Control (MRAC) architecture employing a Neural Network as the adaptive element. The method employed for training the Neural Network for these flight tests is unique since it uses current (online) as well as stored (background) information concurrently for adaptation. This ability allows the adaptive element to simulate long term memory by retaining specifically stored input output data pairs and using them for concurrent adaptation. Furthermore, the structure of the adaptive law ensures that concurrent training on past data does not affect the responsiveness of the adaptive element to current data. The results show that the concurrent use of current and background data does not affect the practical stability properties of the MRAC control architecture. The results also confirm expected improvements in performance.
Real-Time System Identification of a Small Multi-Engine Aircraft

(Georgia Institute of Technology, 2009-08) DeBusk, Wesley M. ; Chowdhary, Girish ; Johnson, Eric N.

In-flight identification of an aircraft's dynamic model can benefit adaptive control schemes by providing estimates of aerodynamic stability derivatives in real time. This information is useful when the dynamic model changes severely in flight such as when faults and failures occur. Moreover a continuously updating model of the aircraft dynamics can be used to monitor the performance of onboard controllers. Flight test data was collected using a sum of sines input implemented in closed loop on a twin engine, fixed wing, Unmanned Aerial Vehicle. This data has been used to estimate a complete six degree of freedom aircraft linear model using the recursive Fourier Transform Regression method in frequency domain. The methods presented in this paper have been successfully validated using computer simulation and real flight data. This paper shows the feasibility of using the frequency domain Fourier Transform Regression method for real time parameter identification.
3D Obstacle Detection Using a Single Camera

(Georgia Institute of Technology, 2009-08) Shah, Syed Irtiza Ali ; Johnson, Eric N.

This paper aims at detecting obstacles using a single camera in an unknown three dimensional world, for 3D motion of an unmanned air vehicle. Obstacle detection is a pre-requisite for collision-free motion of a UAV through 3D space. Most research towards vision based obstacle detection and avoidance has been done for 2D planar motion of ground robots and using active sensors like laser range finders, sonar, radar etc. Passive camera based research has mostly been done, either using stereo vision (multiple cameras) or, by developing a prior expectation map of the world and its comparison with the new image data. In this paper, an attempt has been made to find a 3D solution of the obstacle detection problem using a single camera in an unknown world. The equations developed and the simulations results presented here, show that a 3D model of the scene can be generated from 2D image information from a single camera flying through a very small arc of lateral flight around the object, without the need of capturing images from all sides as in a typical 'structures from motion' problem. The forward flight simulation results show that the depth extracted from forward motion is in fact usable for large part of the image, which is a significant contribution of this work.