The Fresh-water Sponges of Wisconsin

The fresh-water sponges were observed but not recognized as animals at a very early period. The early investigators thought they were plants; later workers, colonial protozoa. In 1696, Leonard Plukenet first made public mention of them; and later, in i745, Linnaeus described them as Spongia and made mention of their, globuli . After Linnaeus the term Spongia changed many times until, in 1816, Lamarck introduced the generic name Spongilla

Guidance and Control System for a Satellite Constellation

A distributed guidance and control algorithm was developed for a constellation of satellites. The system repositions satellites as required, regulates satellites to desired orbits, and prevents collisions. 1. Optimal methods are used to compute nominal transfers from orbit to orbit. 2. Satellites are regulated to maintain the desired orbits once the transfers are complete. 3. A simulator is used to predict potential collisions or near-misses. 4. Each satellite computes perturbations to its controls so as to increase any unacceptable distances of nearest approach to other objects. a. The avoidance problem is recast in a distributed and locally-linear form to arrive at a tractable solution. b. Plant matrix values are approximated via simulation at each time step. c. The Linear Quadratic Gaussian (LQG) method is used to compute perturbations to the controls that will result in increased miss distances. 5. Once all danger is passed, the satellites return to their original orbits, all the while avoiding each other as above. 6. The delta-Vs are reasonable. The controller begins maneuvers as soon as practical to minimize delta-V. 7. Despite the inclusion of trajectory simulations within the control loop, the algorithm is sufficiently fast for available satellite computer hardware. 8. The required measurement accuracies are within the capabilities of modern inertial measurement devices and modern positioning devices

Guidance in the Use of Adaptive Critics for Control

This chapter, along with Chapter 3, provides an overview of several ADP design techniques. While Chapter 3 deals more with the theoretical foundations, Chapter 4 is more devoted to practical issues such as problem formulation and utility functions. The authors discuss issues associated with designing and training adaptive critics using the design techniques introduced in Chapter 3

Flight Test Results of Autonomous Fixed-Wing Transition to and from Stationary Hover

Copyright © 2007 by Eric N. Johnson, Allen Wu, James C. Neidhoefer, Suresh K. Kannan, and Michael A. Turbe. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.Digital Object Identifier: 10.2514/1.29261Linear 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

Real-Time Vision-Based Relative Aircraft Navigation

Received 22 February 2006; revision received 11 September 2006; accepted for publication 11 September 2006. Copyright © 2007 by Eric N. Johnson, Anthony J. Calise,YokoWatanabe, Jincheol Ha, and James C. Neidhoefer. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.Published in Journal of Aerospace Computing, Information, and Communication, Vol. 4, Issue 4, January 2004.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

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

Published in Journal of Guidance Control and Dynamics, Vol. 31, No. 2, March–April 2008.Received 11 December 2006; revision received 25 May 2007; accepted for publication 25 May 2007. Copyright © 2007 by Eric N. Johnson, Allen Wu, James C. Neidhoefer, Suresh K. Kannan, and Michael A. Turbe. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.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

Nomenclature

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, SubtendedAngle 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

Flight Results of Autonomous Fixed-Wing UAV Transitions to and from Stationary Hover

Presented at the AIAA Guidance, Navigation, and Control Conference and Exhibit 21 - 24 August 2006, Keystone, Colorado.Fixed-wing unmanned aerial vehicles (UAVs) with the ability to hover have significant potential for applications in urban or other constrained environments where the combination of fast speed, endurance, and stable hovering flight can provide strategic advantages. This paper discusses the use of dynamic inversion with neural network adaptation to provide an adaptive controller capable of transitioning a fixed-wing UAV to and from hovering flight in a nearly stationary position. This approach allows utilization of the entire low speed flight envelope even beyond stall conditions. The method is applied to the GTEdge, an 8.75 foot wing span fixed-wing aerobatic UAV which has been fully instrumented for autonomous flight. Results from actual flight test experiments of the system where the airplane transitions from high speed steady flight into a stationary hover and then back are presented