Electrical/Electronics

Obstacle Avoidance Methods

NASA’s Jet Propulsion Laboratory, Pasadena, California Obstacle avoidance is a difficult problem due to the non-convex state constraints. Therefore, the feasible state space needs to be convexified, or split, into convex regions at which point the search for an optimal solution among those convex regions is done. Methods for obstacle avoidance include two mixed integer linear programming (MILP) methods (obstacle related method and path-related method) and a state-constraint convexification method.

Posted in: Briefs, TSP, Electronics & Computers, Information Sciences, Software

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Bar Code Scanner to Supplement Property Inventory Process

There is no longer a need for any paper check-off sheets. Goddard Space Flight Center, Greenbelt, Maryland Within NASA Goddard, property inventories outside of the formal inventories had no automated method to perform quick checks of equipment location, especially during periods leading up to a formal inventory. One method that existed simply involved printing out an end-user’s equipment in a hard-copy form, and having each employee search for and annotate the results on the hard copy. These hard copies would then have to be returned to the property custodian. Information on the new whereabouts of equipment may or may not have been updated. This approach also relied on an honor system and the work styles of different individuals. Sometimes, multiple reminders needed to be sent to some employees to complete their inventory. In other cases, inventory was made more difficult because of the location of the equipment. A lot of property consists of test equipment in a lab environment. In this case, equipment is sometimes hard to access or grouped with numerous other pieces. These cases make it difficult to read off barcode numbers.

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Semantic Language and Tools for Reporting Human Factors Incidents

Lyndon B. Johnson Space Center, Houston, Texas This human factors incident reporting tool for authoring and using human factors incident data combines semantic Web technologies with automated assistive technologies to aid users in finding relationships among incidents. The semantic indexing provided by the use of incident reporting language permits more sophisticated search of archives. During Phase I of this project, a semantic language for incident reporting in XML was defined, and a technology approach was designed for authoring and using incident reports represented in this language. In Phase II, this software was implemented and its effectiveness evaluated for the space human factors community at JSC.

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Control Software for Integrated CW Radar Module

NASA’s Jet Propulsion Laboratory, Pasadena, California This software controls the behavior of a miniaturized microwave radar module. It controls the hardware, digitizes raw samples from the analog output of the module, and applies DSP (digital signal processing) algorithms to the data stream to reduce the bandwidth and data rate. It also implements an automatic calibration algorithm to adjust the I/Q (in-phase and quadrature) values in the cancellation path to remove a large unchanging signal. The software implements a variety of commands to control the behavior of the system, and provides for synchronization of multiple modules. It encodes the digital data in a format suitable for serial ports.

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Furuno Radar/SureTrak Interface Software

Goddard Space Flight Center, Greenbelt, Maryland The Wallops Flight Facility Launch Range has a need to interface data from ship surveillance Furuno radar to an existing surveillance display system (SureTrak). SureTrak is a multi-sensor waterway and air surveillance system. The display of Furuno radar data by the SureTrak system will be used for risk analysis purposes prior to rocket launches. The capability did not exist within the SureTrak system to ingest data from the Furuno radar. This software application was developed to provide the needed data interface capability within the SureTrak system. In addition to providing a data interface to SureTrak, the software application will also provide a data interface to another software application that performs probability of impact calculations on the ships reported by the Furuno radar.

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An Empirical Metric of Individual Datapoint Utility Given Ample Metadata as Applied to the OCO-2 Flight System

This method constructs new warn levels for metadata-rich data sources. NASA’s Jet Propulsion Laboratory, Pasadena, California Traditionally, quality flags provided a binary yes/no estimation of a datapoint’s utility. However, in modern instrumentation, significant auxiliary information for each datapoint can be obtained. This permits prediction of more than a binary estimate of good or bad data. Further, the physical confounding forces that obscure an observation’s utility are themselves rarely binary, such as the example of clouds with varying thickness from insignificant to entirely opaque. In this method, many different increasingly stringent filters are created allowing less and less data through, while attempting to minimize an error metric. This metric can be compared with select “truth” systems such as ground observations or regions of the Earth where the truth is believed to be predictable and known. For each sounding, the number of these filters that reject the observation in question becomes an estimate of its data quality: larger values mean most filters reject the sounding, while smaller values mean most filters accept the sounding. This integer, ranging from 0 to 19, is called the Warn Level. Instead of a binary yes/no data quality flag, this instead provides a data ordering paradigm with “better” and “worse” data. Warn Levels can be developed for any metadata-rich datasource with a functional error metric to help guide researchers to superior, tunable data filtration.

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Gravitational Compensation Onboard a Comsat

NASA’s Jet Propulsion Laboratory, Pasadena, California This technique for compensating the gravitational attraction experienced by a test-mass freely floating onboard a satellite is new, and solves an important problem that all gravitational wave missions face. Its application to the geostationary Laser Interferometer Space Antenna (gLISA) mission concept addresses and completely solves an important noise source: the gravity-gradient noise.

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