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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Comparison of central axis and jet ring coolant supply for turbine disk cooling on a SSME-HPOTP model

The test facility, test methods and results are presented for an experimental study modeling the cooling of turbine disks in the blade attachment regions with multiple impinging jets, in a configuration simulating the disk cooling method employed on the Space Shuttle Main Engine oxygen turbopump. The study's objective was to provide a comparison of detailed local convection heat transfer rates obtained for a single center-supply of disk coolant with those obtained with the present flight configuration where disk coolant is supplied through an array of 19 jets located near the disk outer radius. Specially constructed disk models were used in a program designed to evaluate possible benefits and identify any possible detrimental effects involved in employing an alternate disk cooling scheme. The study involved the design, construction and testing of two full scale rotating model disks, one plane and smooth for baseline testing and the second contoured to the present flight configuration, together with the corresponding plane and contoured stator disks. Local heat transfer rates are determined from the color display of encapsulated liquid crystals coated on the disk in conjunction with use of a computer vision system. The test program was composed of a wide variety of disk speeds, flowrates, and geometrical configurations, including testing for the effects of disk boltheads and gas ingestion from the gas path region radially outboard of the disk-cavity.

Kim, Y. W.↗
NASA NTRS1992

The fusion of information via fuzzy integration

Multisensor fusion is becoming increasingly important in intelligent computer vision systems. In this paper we present the generalized fuzzy integral with respect to an S-decomposable measure as a tool for fusing information from multiple sensors in an object recognition problem. Results from an experiment with automatic target recognition imagery are provided.

Keller, James M.↗
NASA NTRS1994

Unsupervised texture image segmentation by improved neural network ART2

We here propose a segmentation algorithm of texture image for a computer vision system on a space robot. An improved adaptive resonance theory (ART2) for analog input patterns is adapted to classify the image based on a set of texture image features extracted by a fast spatial gray level dependence method (SGLDM). The nonlinear thresholding functions in input layer of the neural network have been constructed by two parts: firstly, to reduce the effects of image noises on the features, a set of sigmoid functions is chosen depending on the types of the feature; secondly, to enhance the contrast of the features, we adopt fuzzy mapping functions. The cluster number in output layer can be increased by an autogrowing mechanism constantly when a new pattern happens. Experimental results and original or segmented pictures are shown, including the comparison between this approach and K-means algorithm. The system written in C language is performed on a SUN-4/330 sparc-station with an image board IT-150 and a CCD camera.

Wang, Zhiling↗
DOE OSTI2025

Integrating Machine-learning-assisted Computer Vision with RICH System

Developments in artificial intelligence have vastly expanded the capabilities of robots. Currently, the Spallation Neutron Source (SNS) beamlines at Oak Ridge National Lab (ORNL) have robotic sample loaders to increase the efficiency of running experiments. However, they require retraining if anything about the situation changes, e.g., where the samples are, and cannot notice if errors occur. So, the viability of using computer vision and machine learning to enhance these sample loaders’ functionality was investigated. In this project, the RICH system with a Dobot CR3 6-axis robot present at the VULCAN beamline assisted by an Intel Realsense D435i camera, a unique camera that enables convenient translation of 2D pixel coordinates to 3D world points, was programmed to load ceramic crucibles into a thermogravimetric analyzer (TGA) furnace. An algorithm was constructed in Python with three major phases planned: (1) obtaining a sample, (2) moving it to the target location, and then (3) bringing the sample back to its original location once the experiment finished. In the first phase, the algorithm would dynamically detect sample locations using ArUco markers to recognize the samples’ general location and a custom-trained yolov5 object detection model to locate the crucibles’ centers. Afterward, the robot would be directed to pick up samples based on the crucibles’ calculated positions. In the second phase, the robot would move the sample to a secondary point, reorient its grip, and place the sample at the target location. In the final phase, the robot would determine whether the sample was intact and would bring it back to its original place if it was or raise an alarm. Using this algorithm, the robot was able to pick up different types of crucibles at varying positions. These results indicate that integrating machine-learning-assisted computer vision with robotic sample loaders can result in effective autonomous detection of samples.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

NETRA: A parallel architecture for integrated vision systems. 1: Architecture and organization

Computer vision is regarded as one of the most complex and computationally intensive problems. An integrated vision system (IVS) is considered to be a system that uses vision algorithms from all levels of processing for a high level application (such as object recognition). A model of computation is presented for parallel processing for an IVS. Using the model, desired features and capabilities of a parallel architecture suitable for IVSs are derived. Then a multiprocessor architecture (called NETRA) is presented. This architecture is highly flexible without the use of complex interconnection schemes. The topology of NETRA is recursively defined and hence is easily scalable from small to large systems. Homogeneity of NETRA permits fault tolerance and graceful degradation under faults. It is a recursively defined tree-type hierarchical architecture where each of the leaf nodes consists of a cluster of processors connected with a programmable crossbar with selective broadcast capability to provide for desired flexibility. A qualitative evaluation of NETRA is presented. Then general schemes are described to map parallel algorithms onto NETRA. Algorithms are classified according to their communication requirements for parallel processing. An extensive analysis of inter-cluster communication strategies in NETRA is presented, and parameters affecting performance of parallel algorithms when mapped on NETRA are discussed. Finally, a methodology to evaluate performance of algorithms on NETRA is described.

Choudhary, Alok N.↗
DOE OSTI2024

Hydra: Computer Vision for Online Data Quality Monitoring

Hydra is a system utilizing computer vision for near real-time data quality monitoring. Currently operational across all of Jefferson Lab’s experimental halls, it reduces the workload of shift takers by autonomously monitoring diagnostic plots during experiments. Hydra uses "off-the-shelf" supervised learning technologies and is supported by a comprehensive MySQL database. To simplify access, web apps have been developed to facilitate both labeling and monitoring of Hydra’s inferences. Hydra can connect with the alarm system and incorporates complete historical tracking, enabling it to identify issues that shift takers could miss. When issues are detected, a natural first question is: "Why does Hydra think there is a problem?" To answer, Hydra employs Gradient-weighted Class Activation Maps (GradCAM) to identify regions of the image that are important for the specific classification. This interpretive layer enhances transparency and trustworthiness, which is essential for integration with experiment workflows and operation. The Hydra system, results, and sociological considerations for deployment will be discussed.

Jeske, Torri↗
NASA NTRS1983

Sensor Control of Robot Arc Welding

The potential for using computer vision as sensory feedback for robot gas-tungsten arc welding is investigated. The basic parameters that must be controlled while directing the movement of an arc welding torch are defined. The actions of a human welder are examined to aid in determining the sensory information that would permit a robot to make reproducible high strength welds. Special constraints imposed by both robot hardware and software are considered. Several sensory modalities that would potentially improve weld quality are examined. Special emphasis is directed to the use of computer vision for controlling gas-tungsten arc welding. Vendors of available automated seam tracking arc welding systems and of computer vision systems are surveyed. An assessment is made of the state of the art and the problems that must be solved in order to apply computer vision to robot controlled arc welding on the Space Shuttle Main Engine.

Sias, F. R., Jr.↗
NASA NTRS2012

Real-time Accurate Surface Reconstruction Pipeline for Vision Guided Planetary Exploration Using Unmanned Ground and Aerial Vehicles

This report discusses work completed over the summer at the Jet Propulsion Laboratory (JPL), California Institute of Technology. A system is presented to guide ground or aerial unmanned robots using computer vision. The system performs accurate camera calibration, camera pose refinement and surface extraction from images collected by a camera mounted on the vehicle. The application motivating the research is planetary exploration and the vehicles are typically rovers or unmanned aerial vehicles. The information extracted from imagery is used primarily for navigation, as robot location is the same as the camera location and the surfaces represent the terrain that rovers traverse. The processed information must be very accurate and acquired very fast in order to be useful in practice. The main challenge being addressed by this project is to achieve high estimation accuracy and high computation speed simultaneously, a difficult task due to many technical reasons.

computer vision↗
NASA NTRS2005

MER-DIMES : a planetary landing application of computer vision

During the Mars Exploration Rovers (MER) landings, the Descent Image Motion Estimation System (DIMES) was used for horizontal velocity estimation. The DIMES algorithm combines measurements from a descent camera, a radar altimeter and an inertial measurement unit. To deal with large changes in scale and orientation between descent images, the algorithm uses altitude and attitude measurements to rectify image data to level ground plane. Feature selection and tracking is employed in the rectified data to compute the horizontal motion between images. Differences of motion estimates are then compared to inertial measurements to verify correct feature tracking. DIMES combines sensor data from multiple sources in a novel way to create a low-cost, robust and computationally efficient velocity estimation solution, and DIMES is the first use of computer vision to control a spacecraft during planetary landing. In this paper, the detailed implementation of the DIMES algorithm and the results from the two landings on Mars are presented.

landing systems↗
NASA NTRS1989

Knowledge-based machine vision systems for space station automation

Computer vision techniques which have the potential for use on the space station and related applications are assessed. A knowledge-based vision system (expert vision system) and the development of a demonstration system for it are described. This system implements some of the capabilities that would be necessary in a machine vision system for the robot arm of the laboratory module in the space station. A Perceptics 9200e image processor, on a host VAXstation, was used to develop the demonstration system. In order to use realistic test images, photographs of actual space shuttle simulator panels were used. The system's capabilities of scene identification and scene matching are discussed.

Ranganath, Heggere S.↗
NASA NTRS1972

Development of a model of machine hand eye coordination and program specifications for a topological machine vision system

A unified approach to computer vision and manipulation is developed which is called choreographic vision. In the model, objects to be viewed by a projected robot in the Viking missions to Mars are seen as objects to be manipulated within choreographic contexts controlled by a multimoded remote, supervisory control system on Earth. A new theory of context relations is introduced as a basis for choreographic programming languages. A topological vision model is developed for recognizing objects by shape and contour. This model is integrated with a projected vision system consisting of a multiaperture image dissector TV camera and a ranging laser system. System program specifications integrate eye-hand coordination and topological vision functions and an aerospace multiprocessor implementation is described.

Source record↗
NASA NTRS1988

Understanding of and applications for robot vision guidance at KSC

The primary thrust of robotics at KSC is for the servicing of Space Shuttle remote umbilical docking functions. In order for this to occur, robots performing servicing operations must be capable of tracking a swaying Orbiter in Six Degrees of Freedom (6-DOF). Currently, in NASA KSC's Robotic Applications Development Laboratory (RADL), an ASEA IRB-90 industrial robot is being equipped with a real-time computer vision (hardware and software) system to allow it to track a simulated Orbiter interface (target) in 6-DOF. The real-time computer vision system effectively becomes the eyes for the lab robot, guiding it through a closed loop visual feedback system to move with the simulated Orbiter interface. This paper will address an understanding of this vision guidance system and how it will be applied to remote umbilical servicing at KSC. In addition, other current and future applications will be addressed.

Shawaga, Lawrence M.↗
NASA NTRS2024

From Blood Platelets Classification to Earth System Digital Twins: A Career in Computer Vision

IEEE SIgnal Processing Society (SPS) and Women in Signal Processing (WiSP) Webinar (https://signalprocessingsociety.org/blog/webinar-wisp-blood-platelets-classification-earth-system-digital-twins-career-computer-vision): Join us for an interactive session with Dr. Jacqueline Le Moigne, Manager of the NASA Science Mission Directorate Earth Science Technology Office’s Advanced Information Systems Technology Program. In this webinar, Dr. Le Moigne will share her journey through academia, the private sector, and her pivotal roles at NASA, emphasizing her work in signal processing, computer vision, and related technologies. The webinar will conclude with an interactive Q&A session, providing attendees the opportunity to engage directly with Dr. Le Moigne about her experiences and insights. Dr. Jacqueline Le Moigne manages NASA's Advanced Information Systems Technology Program, focusing on novel technologies that support Earth Science missions. She previously held roles at NASA Goddard, including Assistant Chief for Technology, and has worked on NASA's Space Technology Research Grants Program and Technology Roadmaps. Before NASA, she was a Research Scientist at the University of Maryland's Computer Vision Laboratory and also gained experience in the private sector. Jacqueline earned her Ph.D. in Computer Science from the University Pierre and Marie Curie in Paris. Her research interests include Image Registration, Computer Vision, Artificial Intelligence, Autonomous Systems, Distributed Spacecraft Missions (DSM), and Earth System Digital Twins (ESDT), as well as high-performance and onboard computing. She has authored over 180 publications, including books and patents, and has received multiple awards, including the NASA Exceptional Service Medal and the Goddard Information Science and Technology Award in 2012.

Computer Vision; Image Processing; Earth Science R↗
NASA NTRS1995

Algorithms for detection of objects in image sequences captured from an airborne imaging system

This research was initiated as a part of the effort at the NASA Ames Research Center to design a computer vision based system that can enhance the safety of navigation by aiding the pilots in detecting various obstacles on the runway during critical section of the flight such as a landing maneuver. The primary goal is the development of algorithms for detection of moving objects from a sequence of images obtained from an on-board video camera. Image regions corresponding to the independently moving objects are segmented from the background by applying constraint filtering on the optical flow computed from the initial few frames of the sequence. These detected regions are tracked over subsequent frames using a model based tracking algorithm. Position and velocity of the moving objects in the world coordinate is estimated using an extended Kalman filter. The algorithms are tested using the NASA line image sequence with six static trucks and a simulated moving truck and experimental results are described. Various limitations of the currently implemented version of the above algorithm are identified and possible solutions to build a practical working system are investigated.

Kasturi, Rangachar↗
NASA NTRS1991

Computer graphics testbed to simulate and test vision systems for space applications

A system was developed for displaying computer graphics images of space objects and the use of the system was demonstrated as a testbed for evaluating vision systems for space applications. In order to evaluate vision systems, it is desirable to be able to control all factors involved in creating the images used for processing by the vision system. Considerable time and expense is involved in building accurate physical models of space objects. Also, precise location of the model relative to the viewer and accurate location of the light source require additional effort. As part of this project, graphics models of space objects such as the Solarmax satellite are created that the user can control the light direction and the relative position of the object and the viewer. The work is also aimed at providing control of hue, shading, noise and shadows for use in demonstrating and testing imaging processing techniques. The simulated camera data can provide XYZ coordinates, pitch, yaw, and roll for the models. A physical model is also being used to provide comparison of camera images with the graphics images.

Cheatham, John B.↗