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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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Publication Search Results

Now showing 1 - 10 of 15

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.
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.
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.
Flight-Test Results of Autonomous Airplane Transitions Between Steady-Level and Hovering Flight

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

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: steady-level 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 flight-test experiments in which the airplane transitions from high-speed, steady-level flight into a hovering condition and then back again.
Real-Time Vision-Based Relative Aircraft Navigation

(Georgia Institute of Technology, 2007-03) Johnson, Eric N. ; Calise, Anthony J. ; Watanabe, Yoko ; Ha, Jin-Cheol ; Neidhoefer, James C.

This paper describes two vision-based techniques for the navigation of an aircraft relative to an airborne target using only information from a single camera fixed to the aircraft. These techniques are motivated by problems such as "see and avoid", pursuit, formation flying, and in-air refueling. By applying an Extended Kalman Filter for relative state estimation, both the velocity and position of the aircraft relative to the target can be estimated. While relative states such as bearing can be estimated fairly easily, estimating the range to the target is more difficult because it requires achieving valid depth perception with a single camera. The two techniques presented here offer distinct solutions to this problem. The first technique, Center Only Relative State Estimation, uses optimal control to generate an optimal (sinusoidal) trajectory to a desired location relative to the target that results in accurate range-to-target estimates while making minimal demands on the image processing system.The second technique, Subtended Angle Relative State Estimation, uses more rigorous image processing to arrive at a valid range estimate without requiring the aircraft to follow a prescribed path. Simulation results indicate that both methods yield range estimates of comparable accuracy while placing different demands on the aircraft and its systems.
Modeling, Control, and Flight Testing of a Small Ducted Fan Aircraft

(Georgia Institute of Technology, 2006-07) Johnson, Eric N. ; Turbe, Michael A.

Small ducted fan autonomous vehicles have potential for several applications, especially for missions in urban environments. This paper discusses the use of dynamic inversion with neural network adaptation to provide an adaptive controller for the GTSpy, a small ducted fan autonomous vehicle based on the Micro Autonomous Systems' Helispy. This approach allows utilization of the entire low speed flight envelope with a relatively poorly understood vehicle. A simulator model is constructed from a force and moment analysis of the vehicle, allowing for a validation of the controller in preparation for flight testing. Data from flight testing of the system is provided.
Adaptive Guidance and Control for Hypersonic Vehicles

(Georgia Institute of Technology, 2006-05) Johnson, Eric N. ; Calise, Anthony J. ; Curry, Michael D.

Guidance and control technology is recognized as an important aspect of the military, civil, and commercial goal of reliable, low-cost, aircraft-type operations into space. Here, several guidance and control methods are extended to enable integration into a single fully adaptive guidance and control system that offers a high degree of mission flexibility, fault tolerance, and autonomy. This paper summarizes the guidance and control system and several research issues related to use of adaptive guidance and control in reusable launch vehicles. Results that demonstrate the ability of the integrated system to plan and fly abort trajectories are also presented.
A Compact Guidance, Navigation, and Control System for Unmanned Aerial Vehicles

(Georgia Institute of Technology, 2006-05) Christophersen, Henrik B. ; Pickell, R. Wayne ; Neidhoefer, James C. ; Koller, Adrian A. ; Kannan, Suresh K. ; Johnson, Eric N.

The Flight Control System 20 (FCS20) is a compact, self-contained Guidance, Navigation, and Control system that has recently been developed to enable advanced autonomous behavior in a wide range of Unmanned Aerial Vehicles (UAVs). The FCS20 uses a floating point Digital Signal Processor (DSP) for high level serial processing, a Field Programmable Gate Array (FPGA) for low level parallel processing, and GPS and Micro Electro Mechanical Systems (MEMS) sensors. In addition to guidance, navigation, and control functions, the FCS20 is capable of supporting advanced algorithms such as automated reasoning, artificial vision, and multi-vehicle interaction. The unique contribution of this paper is that it gives a complete overview of the FCS20 GN&C system, including computing, communications, and information aspects. Computing aspects of the FCS20 include details about the design process, hardware components, and board configurations, and specifications. Communications aspects of the FCS20 include descriptions of internal and external data flow. The information section describes the FCS20 Operating System (OS), the Support Vehicle Interface Library (SVIL) software, the navigation Extended Kalman Filter, and the neural network based adaptive controller. Finally, simulation-based results as well as actual flight test results that demonstrate the operation of the guidance, navigation, and control algorithms on a real Unmanned Aerial Vehicle (UAV) are presented.
Vision-Aided Inertial Navigation for Flight Control

(Georgia Institute of Technology, 2005-09) Wu, Allen D. ; Johnson, Eric N. ; Proctor, Alison A.

Many onboard navigation systems use the Global Positioning System to bound the errors that result from integrating inertial sensors over time. Global Positioning System information, however, is not always accessible since it relies on external satellite signals. To this end, a vision sensor is explored as an alternative for inertial navigation in the context of an Extended Kalman Filter used in the closed-loop control of an unmanned aerial vehicle. The filter employs an onboard image processor that uses camera images to provide information about the size and position of a known target, thereby allowing the flight computer to derive the target's pose. Assuming that the position and orientation of the target are known a priori, vehicle position and attitude can be determined from the fusion of this information with inertial and heading measurements. Simulation and flight test results verify filter performance in the closed-loop control of an unmanned rotorcraft.