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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Measuring Positions of Objects using Two or More Cameras

An improved method of computing positions of objects from digitized images acquired by two or more cameras (see figure) has been developed for use in tracking debris shed by a spacecraft during and shortly after launch. The method is also readily adaptable to such applications as (1) tracking moving and possibly interacting objects in other settings in order to determine causes of accidents and (2) measuring positions of stationary objects, as in surveying. Images acquired by cameras fixed to the ground and/or cameras mounted on tracking telescopes can be used in this method. In this method, processing of image data starts with creation of detailed computer- aided design (CAD) models of the objects to be tracked. By rotating, translating, resizing, and overlaying the models with digitized camera images, parameters that characterize the position and orientation of the camera can be determined. The final position error depends on how well the centroids of the objects in the images are measured; how accurately the centroids are interpolated for synchronization of cameras; and how effectively matches are made to determine rotation, scaling, and translation parameters. The method involves use of the perspective camera model (also denoted the point camera model), which is one of several mathematical models developed over the years to represent the relationships between external coordinates of objects and the coordinates of the objects as they appear on the image plane in a camera. The method also involves extensive use of the affine camera model, in which the distance from the camera to an object (or to a small feature on an object) is assumed to be much greater than the size of the object (or feature), resulting in a truly two-dimensional image. The affine camera model does not require advance knowledge of the positions and orientations of the cameras. This is because ultimately, positions and orientations of the cameras and of all objects are computed in a coordinate system attached to one object as defined in its CAD model.

Klinko, Steve↗
NASA NTRS1996

Estimating Distances To Objects From Motion In Images

In developmental method of estimating distances between moving platform and nearby stationary objects, positions of objects relative to moving platform deduced from apparent motions of those objects in images generated by video camera affixed to platform. Estimates enhanced by use of extended Kalman filter. Developed for use in controlling military helicopters in low-altitude evasive maneuvers, also potentially applicable in robotics and in autonomous navigation of vehicles in general.

Sridhar, B.↗
NASA NTRS2015

An Upper Bound on Orbital Debris Collision Probability When Only One Object has Position Uncertainty Information

Upper bounds on high speed satellite collision probability, P (sub c), have been investigated. Previous methods assume an individual position error covariance matrix is available for each object. The two matrices being combined into a single, relative position error covariance matrix. Components of the combined error covariance are then varied to obtain a maximum P (sub c). If error covariance information for only one of the two objects was available, either some default shape has been used or nothing could be done. An alternative is presented that uses the known covariance information along with a critical value of the missing covariance to obtain an approximate but useful P (sub c) upper bound. There are various avenues along which an upper bound on the high speed satellite collision probability has been pursued. Typically, for the collision plane representation of the high speed collision probability problem, the predicted miss position in the collision plane is assumed fixed. Then the shape (aspect ratio of ellipse), the size (scaling of standard deviations) or the orientation (rotation of ellipse principal axes) of the combined position error ellipse is varied to obtain a maximum P (sub c). Regardless as to the exact details of the approach, previously presented methods all assume that an individual position error covariance matrix is available for each object and the two are combined into a single, relative position error covariance matrix. This combined position error covariance matrix is then modified according to the chosen scheme to arrive at a maximum P (sub c). But what if error covariance information for one of the two objects is not available? When error covariance information for one of the objects is not available the analyst has commonly defaulted to the situation in which only the relative miss position and velocity are known without any corresponding state error covariance information. The various usual methods of finding a maximum P (sub c) do no good because the analyst defaults to no knowledge of the combined, relative position error covariance matrix. It is reasonable to think, given an assumption of no covariance information, an analyst might still attempt to determine the error covariance matrix that results in an upper bound on the P (sub c). Without some guidance on limits to the shape, size and orientation of the unknown covariance matrix, the limiting case is a degenerate ellipse lying along the relative miss vector in the collision plane. Unless the miss position is exceptionally large or the at-risk object is exceptionally small, this method results in a maximum P (sub c) too large to be of practical use. For example, assuming that the miss distance is equal to the current ISS alert volume along-track (+ or -) distance of 25 kilometers and that the at-risk area has a 70 meter radius. The maximum (degenerate ellipse) P (sub c) is about 0.00136. At 40 kilometers, the maximum P (sub c) would be 0.00085 which is still almost an order of magnitude larger than the ISS maneuver threshold of 0.0001. In fact, a miss distance of almost 340 kilometers is necessary to reduce the maximum P (sub c) associated with this degenerate ellipse to the ISS maneuver threshold value. Such a result is frequently of no practical value to the analyst. Some improvement may be made with respect to this problem by realizing that while the position error covariance matrix of one of the objects (usually the debris object) may not be known the position error covariance matrix of the other object (usually the asset) is almost always available. Making use of the position error covariance information for the one object provides an improvement in finding a maximum P (sub c) which, in some cases, may offer real utility. The equations to be used are presented and their use discussed.

Frisbee, Joseph H., Jr.↗
NASA NTRS1985

Optical observations of HEAO-B objects

Positions and error circles of optically unidentified X-ray sources near the galactic plane were used to carry out searches within and around the error circles for optical counterparts for these sources. Forty-nine X-ray fields were searched in the course of 42 nights of observation and 62 individual stellar objects were studied. No optical identifications of the X-ray sources were made.

Mook, D. E.↗
NASA NTRS1992

Large-gap magnetic positioning system having advantageous configuration

A magnetic configuration was devised in which the positioned object is maintained in a stable orientation and position on one side of an opaque plane surface entirely by means of magnetic components on the other side of the plane. The system is effective with or without gravity, and can operate in any orientation. In this system, the positioned object need only contain a simple dipole magnet. The positioning components consist of a group of permanent magnets creating a magnetic field configuration which stabilizes the levitated dipole in all but one degree of freedom, and a magnetic position sensing and force feedback system to actively stabilize the object in the one unstable direction. The system utilizes very low power at equilibrium and can maintain gaps of 50 mm.

Chong, Paul↗
NASA NTRS2015

An Upper Bound on High Speed Satellite Collision Probability When Only One Object has Position Uncertainty Information

Upper bounds on high speed satellite collision probability, PC †, have been investigated. Previous methods assume an individual position error covariance matrix is available for each object. The two matrices being combined into a single, relative position error covariance matrix. Components of the combined error covariance are then varied to obtain a maximum PC. If error covariance information for only one of the two objects was available, either some default shape has been used or nothing could be done. An alternative is presented that uses the known covariance information along with a critical value of the missing covariance to obtain an approximate but potentially useful Pc upper bound.

Frisbee, Joseph H., Jr.↗
NASA NTRS1989

Force/torque and tactile sensors for sensor-based manipulator control

The autonomy of manipulators, in space and in industrial environments, can be dramatically enhanced by the use of force/torque and tactile sensors. The development and future use of a six-component force/torque sensor for the Hermes Robot Arm (HERA) Basic End-Effector (BEE) is discussed. Then a multifunctional gripper system based on tactile sensors is described. The basic transducing element of the sensor is a sheet of pressure-sensitive polymer. Tactile image processing algorithms for slip detection, object position estimation, and object recognition are described.

Vanbrussel, H.↗
NASA NTRS1994

FAST User Guide

The Flow Analysis Software Toolkit, FAST, is a software environment for visualizing data. FAST is a collection of separate programs (modules) that run simultaneously and allow the user to examine the results of numerical and experimental simulations. The user can load data files, perform calculations on the data, visualize the results of these calculations, construct scenes of 3D graphical objects, and plot, animate and record the scenes. Computational Fluid Dynamics (CFD) visualization is the primary intended use of FAST, but FAST can also assist in the analysis of other types of data. FAST combines the capabilities of such programs as PLOT3D, RIP, SURF, and GAS into one environment with modules that share data. Sharing data between modules eliminates the drudgery of transferring data between programs. All the modules in the FAST environment have a consistent, highly interactive graphical user interface. Most commands are entered by pointing and'clicking. The modular construction of FAST makes it flexible and extensible. The environment can be custom configured and new modules can be developed and added as needed. The following modules have been developed for FAST: VIEWER, FILE IO, CALCULATOR, SURFER, TOPOLOGY, PLOTTER, TITLER, TRACER, ARCGRAPH, GQ, SURFERU, SHOTET, and ISOLEVU. A utility is also included to make the inclusion of user defined modules in the FAST environment easy. The VIEWER module is the central control for the FAST environment. From VIEWER, the user can-change object attributes, interactively position objects in three-dimensional space, define and save scenes, create animations, spawn new FAST modules, add additional view windows, and save and execute command scripts. The FAST User Guide uses text and FAST MAPS (graphical representations of the entire user interface) to guide the user through the use of FAST. Chapters include: Maps, Overview, Tips, Getting Started Tutorial, a separate chapter for each module, file formats, and system administration.

Walatka, Pamela P.↗
NASA NTRS1993

Design of an air traffic computer simulation system to support investigation of civil tiltrotor aircraft operations

The TATSS Project's goal was to develop a design for computer software that would support the attainment of the following objectives for the air traffic simulation model: (1) Full freedom of movement for each aircraft object in the simulation model. Each aircraft object may follow any designated flight plan or flight path necessary as required by the experiment under consideration. (2) Object position precision up to +/- 3 meters vertically and +/- 15 meters horizontally. (3) Aircraft maneuvering in three space with the object position precision identified above. (4) Air traffic control operations and procedures. (5) Radar, communication, navaid, and landing aid performance. (6) Weather. (7) Ground obstructions and terrain. (8) Detection and recording of separation violations. (9) Measures of performance including deviations from flight plans, air space violations, air traffic control messages per aircraft, and traditional temporal based measures.

Rogers, Ralph V.↗
NASA NTRS1964

Apparatus alters position of objects to facilitate demagnetization

An apparatus consisting of pulleys, a drive shaft and an inner compartment, in which components to be demagnetized are mounted, is constructed. Due to the speed ratio of the three frames, every point on a component in the inner compartment is cycled through an optimum locus in the demagnetization field.

Rinard, G.↗
NASA NTRS1967

Electronic skewing circuit monitors exact position of object underwater

Linear Variable Differential Transformer /LVDT/ electronic skewing circuit guides a long cylindrical capsule underwater into a larger tube so that it does not contact the tube wall. This device detects movement of the capsule from a reference point and provides a continuous signal that is monitored on an oscilloscope.

Roller, R.↗
NASA NTRS1990

HII regions in dwarf irregular galaxies of the local group

Deep, narrowband H alpha Charge Coupled Device (CCD) surveys of HII regions were carried out in several dwarf irregular galaxies in and near the local group. Data are now complete for these galaxies: NGC 6822, GR 8, IC 10, IC 1613, Sextans A, Sextans B, and Sag Irr. Observations are complete for DDO 47, 53, 167, 168 and 187. Details of some of the results for the surveys completed so far are discussed. For NGC 6822, CCD survey at H alpha resulted in the detection of 145 HII regions in the local group irregular galaxy NGC 6822. Most of them are newly detected, faint surface-brightness objects. Positions, maps and dimensions are being published elsewhere. For GR 8, a deep narrowband H alpha imaging of the nearby dwarf irregular galaxy GR 8 revealed a total of 32 HII regions. Positions, H alpha luminosities, and sizes of these objects were determined. The H alpha luminosity function has the same shape as that for more luminous galaxies, except for size of sample effects. Most HII regions detected are at the very low luminosity end of the general luminosity function. For IC 10, a deep CCD narrowband H alpha imaging of the local group dwarf irregular galaxy IC 10 revealed a total of 144 HII regions. Positions, H alpha luminosities, and sizes of these objects were determined. The H alpha luminosity function has the same shape as that for more luminous galaxies.

Hodge, Paul↗
NASA NTRSDate not supplied

Spectral Characterization of Spacecraft Materials used in Hypervelocity Impact Testing

The increasing number of successfully deployed space missions have resulted in an increased density of man-made objects positioned in orbital domains near Earth. With this steady accumulation of objects in space, it is becoming more imperative to characterize spacecraft materials, which may ultimately be contributors to the orbital debris population. In order to ascertain the potential damage from orbital debris, a laboratory hypervelocity impact test was conducted using a 56-kg modern spacecraft representative satellite (DebriSat) to simulate a catastrophic fragmentation event in low Earth orbit. In an effort to identify unique, material-specific spectroscopic markers, a select number of the spacecraft materials used to construct DebriSat were analyzed using reflectance spectroscopy as a characterization technique for assessment on material type according to optical features. Spectral measurements of DebriSat materials analyzed prior to the laboratory impact are presented in this paper. These data provide a spectral characterization baseline for modern-day spacecraft materials in their pristine conditions and are compared to each other to distinguish spectra of materials belonging to different classifications with an effort of grouping them using color index. The ongoing efforts to classify materials utilizing their reflectance spectroscopic fingerprint are discussed in this study

Jacqueline A. Reyes↗
NASA NTRSDate not supplied

Analysis of Spacecraft Materials Discrimination Using Color Indices for Remote Sensing for Space Situational Awareness

The increasing number of space missions have resulted in an augmented density of artificial objects positioned in orbital domains near Earth. Knowing the material composition of the resident space object can be of value in object identification and risk assessment using remote sensing techniques. To perform this task, it is increasingly imperative to optically characterize spacecraft materials to identify unique material-specific spectroscopic markers. In this work, a variety of materials frequently utilized by the aerospace industry in spacecraft design and construction were analyzed using reflectance spectroscopy. The collected data provide a spectral characterization baseline for modern-day and historical spacecraft materials. The color index was computed for standard astronomical and suggested theoretical filter passbands and compared to each other in their capability to discriminate materials belonging to different classes. The color index was calculated from reflectance spectra of common spacecraft materials in their pristine, as-received conditions that fell under different family groupings. Different color-index combinations were studied using color-color diagrams. Visual and quantitative analysis of the color-color diagrams were used to evaluate the possibility of discriminating materials from one another by means of optical measurements. Results of the analysis show that polyimide and photovoltaic materials are most easy to discriminate from all other materials via color indices using different filter passbands evaluated in this study.

spectral reflectance↗
NASA NTRS2004

Optical Tweezer Assembly and Calibration

An Optical Tweezer, as the name implies, is a useful tool for precision manipulation of micro and nano scale objects. Using the principle of electromagnetic radiation pressure, an optical tweezer employs a tightly focused laser beam to trap and position objects of various shapes and sizes. These devices can trap micrometer and nanometer sized objects. An exciting possibility for optical tweezers is its future potential to manipulate and assemble micro and nano sized sensors. A typical optical tweezer makes use of the following components: laser, mirrors, lenses, a high quality microscope, stage, Charge Coupled Device (CCD) camera, TV monitor and Position Sensitive Detectors (PSDs). The laser wavelength employed is typically in the visible or infrared spectrum. The laser beam is directed via mirrors and lenses into the microscope. It is then tightly focused by a high magnification, high numerical aperture microscope objective into the sample slide, which is mounted on a translating stage. The sample slide contains a sealed, small volume of fluid that the objects are suspended in. The most common objects trapped by optical tweezers are dielectric spheres. When trapped, a sphere will literally snap into and center itself in the laser beam. The PSD s are mounted in such a way to receive the backscatter after the beam has passed through the trap. PSD s used with the Differential Interference Contrast (DIC) technique provide highly precise data. Most optical tweezers employ lasers with power levels ranging from 10 to 100 miliwatts. Typical forces exerted on trapped objects are in the pico-newton range. When PSDs are employed, object movement can be resolved on a nanometer scale in a time range of milliseconds. Such accuracy, however, can only by utilized by calibrating the optical tweezer. Fortunately, an optical tweezer can be modeled accurately as a simple spring. This allows Hook s Law to be used. My goal this summer at NASA Glenn Research Center is the assembly and calibration of an optical tweezer setup in the Instrumentation and Controls Division (5520). I am utilizing a custom LabVIEW Virtual Instrument program for data collection and microscope stage control. Helping me in my assignment are the following people: Mentor Susan Wrbanek (5520), Dr. Baha Jassemnejad (UCO) and Technicians Ken Weiland (7650) and James Williams (7650). Without their help, my task would not be possible.

Collins, Timothy M.↗
NASA NTRS1993

Three-dimensional object recognition using similar triangles and decision trees

A system, TRIDEC, that is capable of distinguishing between a set of objects despite changes in the objects' positions in the input field, their size, or their rotational orientation in 3D space is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 x 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94 percent recognition accuracy in the 2D invariant object recognition domain and 98 percent recognition accuracy in the 3D invariant object recognition domain after training on only a small sample of transformed views of the objects.

Spirkovska, Lilly↗