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Web-Based Environment for Maintaining Legacy Software

“Advanced Tool Integration Environment” (“ATIE”) is the name of both a software system and a Web-based environment created by the system for maintaining an archive of legacy software and expertise involved in developing the legacy software. ATIE can also be used in modifying legacy software and developing new software. The information that can be encapsulated in ATIE includes experts’ documentation, input and output data of tests cases, source code, and compilation scripts. All of this information is available within a common environment and retained in a database for ease of access and recovery by use of powerful search engines. ATIE also accommodates the embedment of supporting software that users require for their work, and even enables access to supporting commercial-off-the-shelf (COTS) software within the flow of the experts’ work.

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Parallel-Processing Software for Correlating Stereo Images

A computer program implements parallel-processing algorithms for correlating images of terrain acquired by stereoscopic pairs of digital stereo cameras on an exploratory robotic vehicle (e.g., a Mars rover). Such correlations are used to create three-dimensional computational models of the terrain for navigation. In this program, the scene viewed by the cameras is segmented into subimages. Each subimage is assigned to one of a number of central processing units (CPUs) operating simultaneously. Because each subimage is smaller than a full image, the correlation process takes less time than it would if full images were processed on one CPU. Segmentation and parallelization also make the process more robust in that the smaller subimages present fewer opportunities for a correlation algorithm to “get lost” and thereby fail to converge on a solution. The effectiveness of this program has been demonstrated on several parallel- processing computer systems. Whereas correlation processing of a typical stereoscopic pair of test images on a single CPU was found to take on the order of one hour, parallel processing of the same images on a 16-CPU cluster was found to take about 3 minutes. This program was written by Gerhard Klimeck, Robert Deen, Michael Mcauley, and Eric De Jong of Caltech for NASA’s Jet Propulsion Laboratory. This software is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (626) 395-2322. Refer to NPO-30631.

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Parallel-Processing Software for Correlating Stereo Images

A computer program implements parallel-processing algorithms for correlating images of terrain acquired by stereoscopic pairs of digital stereo cameras on an exploratory robotic vehicle (e.g., a Mars rover). Such correlations are used to create three-dimensional computational models of the terrain for navigation. In this program, the scene viewed by the cameras is segmented into subimages. Each subimage is assigned to one of a number of central processing units (CPUs) operating simultaneously. Because each subimage is smaller than a full image, the correlation process takes less time than it would if full images were processed on one CPU. Segmentation and parallelization also make the process more robust in that the smaller subimages present fewer opportunities for a correlation algorithm to “get lost” and thereby fail to converge on a solution. The effectiveness of this program has been demonstrated on several parallel- processing computer systems. Whereas correlation processing of a typical stereoscopic pair of test images on a single CPU was found to take on the order of one hour, parallel processing of the same images on a 16-CPU cluster was found to take about 3 minutes. This program was written by Gerhard Klimeck, Robert Deen, Michael Mcauley, and Eric De Jong of Caltech for NASA’s Jet Propulsion Laboratory. This software is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (626) 395-2322. Refer to NPO-30631.

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Model Checker for Java Programs

Java Pathfinder (JPF) is a verification and testing environment for Java that integrates model checking, program analysis, and testing. JPF consists of a custom-made Java Virtual Machine (JVM) that interprets bytecode, combined with a search interface to allow the complete behavior of a Java program to be analyzed, including interleavings of concurrent programs. JPF is implemented in Java, and its architecture is highly modular to support rapid prototyping of new features.

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Mission Simulation Toolkit

The Mission Simulation Toolkit (MST) is a flexible software system for autonomy research. It was developed as part of the Mission Simulation Facility (MSF) project that was started in 2001 to facilitate the development of autonomous planetary robotic missions. Autonomy is a key enabling factor for robotic exploration. There has been a large gap between autonomy software (at the research level), and software that is ready for insertion into near-term space missions. The MST bridges this gap by providing a simulation framework and a suite of tools for supporting research and maturation of autonomy.

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Automatic Command Sequence Generation

Automatic Sequence Generator (Autogen) Version 3.0 software automatically generates command sequences for the Mars Reconnaissance Orbiter (MRO) and several other JPL spacecraft operated by the multi-mission support team. Autogen uses standard JPL sequencing tools like APGEN, ASP, SEQGEN, and the DOM database to automate the generation of uplink command products, Spacecraft Command Message Format (SCMF) files, and the corresponding ground command products, DSN Keywords Files (DKF). Autogen supports all the major multimission mission phases including the cruise, aerobraking, mapping/science, and relay mission phases.

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Generating Scenarios When Data Are Missing

A computer program implements the algorithm described in “Hypothetical Scenario Generator for Fault-Tolerant Diagnosis” (NPO-42516), NASA Tech Briefs, Vol. 31, No. 6 (June 2007), page 71. To recapitulate: the Hypothetical Scenario Generator (HSG) is being developed in conjunction with other components of artificial-intelligence systems for automated diagnosis and prognosis of faults in spacecraft, aircraft, and other complex engineering systems. The HSG accepts, as input, possibly incomplete data on the current state of a system (see figure).

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