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At least 217 records · Page 12

Compact Microscope Imaging System Developed

The Compact Microscope Imaging System (CMIS) is a diagnostic tool with intelligent controls for use in space, industrial, medical, and security applications. The CMIS can be used in situ with a minimum amount of user intervention. This system, which was developed at the NASA Glenn Research Center, can scan, find areas of interest, focus, and acquire images automatically. Large numbers of multiple cell experiments require microscopy for in situ observations; this is only feasible with compact microscope systems. CMIS is a miniature machine vision system that combines intelligent image processing with remote control capabilities. The software also has a user-friendly interface that can be used independently of the hardware for post-experiment analysis. CMIS has potential commercial uses in the automated online inspection of precision parts, medical imaging, security industry (examination of currency in automated teller machines and fingerprint identification in secure entry locks), environmental industry (automated examination of soil/water samples), biomedical field (automated blood/cell analysis), and microscopy community. CMIS will improve research in several ways: It will expand the capabilities of MSD experiments utilizing microscope technology. It may be used in lunar and Martian experiments (Rover Robot). Because of its reduced size, it will enable experiments that were not feasible previously. It may be incorporated into existing shuttle orbiter and space station experiments, including glove-box-sized experiments as well as ground-based experiments.

McDowell, Mark↗

Compact Video Microscope Imaging System Implemented in Colloid Studies

Long description Photographs showing fiber-optic light source, microscope and charge-coupled discharge (CCD) camera head connected to camera body, CCD camera body feeding data to image acquisition board in PC, and Cartesian robot controlled via PC board. The Compact Microscope Imaging System (CMIS) is a diagnostic tool with intelligent controls for use in space, industrial, medical, and security applications. CMIS can be used in situ with a minimum amount of user intervention. This system can scan, find areas of interest in, focus on, and acquire images automatically. Many multiple-cell experiments require microscopy for in situ observations; this is feasible only with compact microscope systems. CMIS is a miniature machine vision system that combines intelligent image processing with remote control. The software also has a user-friendly interface, which can be used independently of the hardware for further post-experiment analysis. CMIS has been successfully developed in the SML Laboratory at the NASA Glenn Research Center and adapted for use for colloid studies and is available for telescience experiments. The main innovations this year are an improved interface, optimized algorithms, and the ability to control conventional full-sized microscopes in addition to compact microscopes. The CMIS software-hardware interface is being integrated into our SML Analysis package, which will be a robust general-purpose image-processing package that can handle over 100 space and industrial applications.

McDowell, Mark↗

Development of Collaborative Research Initiatives to Advance the Aerospace Sciences-via the Communications, Electronics, Information Systems Focus Group

The primary goal of the Adaptive Vision Laboratory Research project was to develop advanced computer vision systems for automatic target recognition. The approach used in this effort combined several machine learning paradigms including evolutionary learning algorithms, neural networks, and adaptive clustering techniques to develop the E-MOR.PH system. This system is capable of generating pattern recognition systems to solve a wide variety of complex recognition tasks. A series of simulation experiments were conducted using E-MORPH to solve problems in OCR, military target recognition, industrial inspection, and medical image analysis. The bulk of the funds provided through this grant were used to purchase computer hardware and software to support these computationally intensive simulations. The payoff from this effort is the reduced need for human involvement in the design and implementation of recognition systems. We have shown that the techniques used in E-MORPH are generic and readily transition to other problem domains. Specifically, E-MORPH is multi-phase evolutionary leaming system that evolves cooperative sets of features detectors and combines their response using an adaptive classifier to form a complete pattern recognition system. The system can operate on binary or grayscale images. In our most recent experiments, we used multi-resolution images that are formed by applying a Gabor wavelet transform to a set of grayscale input images. To begin the leaming process, candidate chips are extracted from the multi-resolution images to form a training set and a test set. A population of detector sets is randomly initialized to start the evolutionary process. Using a combination of evolutionary programming and genetic algorithms, the feature detectors are enhanced to solve a recognition problem. The design of E-MORPH and recognition results for a complex problem in medical image analysis are described at the end of this report. The specific task involves the identification of vertebrae in x-ray images of human spinal columns. This problem is extremely challenging because the individual vertebra exhibit variation in shape, scale, orientation, and contrast. E-MORPH generated several accurate recognition systems to solve this task. This dual use of this ATR technology clearly demonstrates the flexibility and power of our approach.

Knasel, T. Michael↗

Optical correlator techniques applied to robotic vision

Vision processing is one of the most computationally intensive tasks required of an autonomous robot. The data flow from a single typical imaging sensor is roughly 60 Mbits/sec, which can easily overload current on-board processors. Optical correlator-based processing can be used to perform many of the functions required of a general robotic vision system, such as object recognition, tracking, and orientation determination, and can perform these functions fast enough to keep pace with the incoming sensor data. We describe a hybrid digital electronic/analog optical robotic vision processing system developed at Ames Research Center to test concepts and algorithms for autonomous construction, inspection, and maintenance of space-based habitats. We discuss the system architecture design and implementation, its performance characteristics, and our future plans. In particular, we compare the performance of the system to a more conventional all digital electronic system developed concurrently. The hybrid system consistently outperforms the digital electronic one in both speed and robustness.

Hine, Butler P., III↗

Learning for autonomous navigation : extrapolating from underfoot to the far field

Autonomous off-road navigation of robotic ground vehicles has important applications on Earth and in space exploration. Progress in this domain has been retarded by the limited lookahead range of 3-D sensors and by the difficulty of preprogramming systems to understand the traversability of the wide variety of terrain they can encounter. Enabling robots to learn from experience may alleviate both of these problems. We define two paradigms for this, learning from 3-D geometry and learning from proprioception, and describe initial instantiations of them we have developed under DARPA and NASA programs. Field test results show promise for learning traversability of vegetated terrain, learning to extend the lookahead range of the vision system, and learning how slip varies with slope.

autonomous robots↗

Intelligent manipulation technique for multi-branch robotic systems

New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system.

Chen, Alexander Y. K.↗

Image based tracking approaches to AR/C at the Johnson Space Center

Automated Rendezvous and Capture (AR&C) requires the determination of the six degrees of freedom relating two free bodies. Sensor systems that can provide such information have varying sizes, weights, power requirements, complexities and accuracies. One type of sensor system which can provide several key advantages is an image based tracking system, or better known as a machine vision system. By image based tracking we mean that the sensor is some imaging device such as one or more video cameras, from which the tracking parameters necessary to support the rendezvous and capture operations (range, attitude, etc.) can be derived. Image based tracking offers many advantages such as relative hardware simplicity and reprogrammability. These advantages must be weighed against the disadvantages of these systems, such as limited operational range, poorer accuracy at greater distances and sensitivity to lighting conditions. However, with properly designed algorithms and targets these disadvantages can be minimized for many important applications. Rigorous testing in realistic environments can further increase the robustness and reliability of these systems. This presentation describes the facilities used at JSC to support AR&C image based tracking development and the details of our binocular stereo approach to image based tracking.

Fisher, Timothy E.↗

Object tracking with stereo vision

A real-time active stereo vision system incorporating gaze control and task directed vision is described. Emphasis is placed on object tracking and object size and shape determination. Techniques include motion-centroid tracking, depth tracking, and contour tracking.

Huber, Eric↗

John F. Kennedy Space Center's Technology Development and Application 2006-2007 Report

Topics covered include: Reversible Chemochromic Hydrogen Detectors; Determining Trajectory of Triboelectrically Charged Particles, Using Discrete Element Modeling; Using Indium Tin Oxide To Mitigate Dust on Viewing Ports; High-Performance Polyimide Powder Coatings; Controlled-Release Microcapsules for Smart Coatings for Corrosion Applications; Aerocoat 7 Replacement Coatings; Photocatalytic Coatings for Exploration and Spaceport Design; New Materials for the Repair of Polyimide Electrical Wire Insulation; Commodity-Free Calibration; Novel Ice Mitigation Methods; Crack Offset Measurement With the Projected Laser Target Device; New Materials for Structural Composites and Protective Coatings; Fire Chemistry Testing of Spray-On Foam Insulation (SOFI); Using Aerogel-Based Insulation Material To Prevent Foam Loss on the Liquid-Hydrogen Intertank; Particle Ejection and Levitation Technology (PELT); Electrostatic Characterization of Lunar Dust; Numerical Analysis of Rocket Exhaust Cratering; RESOLVE Projects: Lunar Water Resource Demonstration and Regolith Volatile Characterization; Tribocharging Lunar Soil for Electrostatic Beneficiation; Numerically Modeling the Erosion of Lunar Soil by Rocket Exhaust Plumes; Trajectory Model of Lunar Dust Particles; Using Lunar Module Shadows To Scale the Effects of Rocket Exhaust Plumes; Predicting the Acoustic Environment Induced by the Launch of the Ares I Vehicle; Measuring Ultrasonic Acoustic Velocity in a Thin Sheet of Graphite Epoxy Composite; Hail Size Distribution Mapping; Launch Pad 39 Hail Monitor Array System; Autonomous Flight Safety System - Phase III; The Photogrammetry Cube; Bird Vision System; Automating Range Surveillance Through Radio Interferometry and Field Strength Mapping Techniques; Next-Generation Telemetry Workstation; GPS Metric Tracking Unit; and Space-Based Range.

Source record↗

Hybrid Terrain Database

A prototype hybrid terrain database is being developed in conjunction with other databases and with hardware and software that constitute subsystems of aerospace cockpit display systems (known in the art as synthetic vision systems) that generate images to increase pilots' situation awareness and eliminate poor visibility as a cause of aviation accidents. The basic idea is to provide a clear view of the world around an aircraft by displaying computer-generated imagery derived from an onboard database of terrain, obstacle, and airport information.

Arthur, Trey↗

Real-time Terrain Relative Navigation Test Results from a Relevant Environment for Mars Landing

Terrain Relative Navigation (TRN) is an on-board GN&C function that generates a position estimate of a spacecraft relative to a map of a planetary surface. When coupled with a divert, the position estimate enables access to more challenging landing sites through pin-point landing or large hazard avoidance. The Lander Vision System (LVS) is a smart sensor system that performs terrain relative navigation by matching descent camera imagery to a map of the landing site and then fusing this with inertial measurements to obtain high rate map relative position, velocity and attitude estimates. A prototype of the LVS was recently tested in a helicopter field test over Mars analog terrain at altitudes representative of Mars Entry Descent and Landing conditions. TRN ran in real-time on the LVS during the flights without human intervention or tuning. The system was able to compute estimates accurate to 40m (3 sigma) in 10 seconds on a flight like processing system. This paper describes the Mars operational test space definition, how the field test was designed to cover that operational envelope, the resulting TRN performance across the envelope and an assessment of test space coverage.

Pin-point Landing↗

Design and Analysis of Map Relative Localization for Access to Hazardous Landing Sites on Mars

Human and robotic planetary lander missions require accurate surface relative position knowledge to land near science targets or next to pre-deployed assets. In the absence of GPS, accurate position estimates can be obtained by automatically matching sensor data collected during descent to an on-board map. The Lander Vision System (LVS) that is being developed for Mars landing applications generates landmark matches in descent imagery and combines these with inertial data to estimate vehicle position, velocity and attitude. This paper describes recent LVS design work focused on making the map relative localization algorithms robust to challenging environmental conditions like bland terrain, appearance differences between the map and image and initial input state errors. Improved results are shown using data from a recent LVS field test campaign. This paper also fills a gap in analysis to date by assessing the performance of the LVS with data sets containing significant vertical motion including a complete data set from the Mars Science Laboratory mission, a Mars landing simulation, and field test data taken over multiple altitudes above the same scene. Accurate and robust performance is achieved for all data sets indicating that vertical motion does not play a significant role in position estimation performance.

Lander Vision System↗

Robonaut: A Robotic Astronaut Assistant

NASA's latest anthropomorphic robot, Robonaut, has reached a milestone in its capability. This highly dexterous robot, designed to assist astronauts in space, is now performing complex tasks at the Johnson Space Center that could previously only be carried out by humans. With 43 degrees of freedom, Robonaut is the first humanoid built for space and incorporates technology advances in dexterous hands, modular manipulators, lightweight materials, and telepresence control systems. Robonaut is human size, has a three degree of freedom (DOF) articulated waist, and two, seven DOF arms, giving it an impressive work space for interacting with its environment. Its two, five fingered hands allow manipulation of a wide range of tools. A pan/tilt head with multiple stereo camera systems provides data for both teleoperators and computer vision systems.

Ambrose, Robert O.↗

Clearance Analysis of CTC2 (on ELC4) to S-TRRJ HRS Radiator Rotation Envelope

In response to the planned retirement of the Space Shuttle Program, International Space Station (ISS) management began stockpiling spare parts on the ISS. Many of the larger orbital replacement units were stored on the Expedite the Processing of Experiments to Space Station (EXPRESS) Logistics Carriers (ELCs) mounted on the end of the S3 and P3 truss segments, immediately outboard of the Thermal Radiator Rotary Joints (TRRJs) and their attached radiators. In an August 2009 computer-aided design (CAD) assessment, it was determined that mounting the Cargo Transport Container (CTC) 2 on the inboard face of ELC4 as planned would create insufficient clearance between the CTC2 and the rotational envelope of the radiators when the TRRJs were rotated to a gamma angle of 35.0 degrees. The true clearance would depend on how the Unpressurized Cargo Carrier Attachment System (UCCAS) was mounted to the S3 truss and how the ELC4 was attached to it. If the plane of the UCCAS attachment points were tilted even slightly inboard, it would significantly change the clearance between CTC2 and the Starboard TRRJ (S-TRRJ) radiators. Additionally, since CTC2 would be covered in multilayer insulation (MLI), the true outer profile of CTC2 was not captured in the CAD models used for the clearance assessment. It was possible that, even if the S-TRRJ radiators cleared CTC2, they could snag the MLI covering. In the fall of 2010, the Image Science and Analysis Group (ISAG) was asked to perform an on-orbit clearance analysis to determine the location of CTC2 on ELC4 and the S-TRRJ radiators at the angle of closest approach so that a positive clearance could be assured. To provide the measurements as quickly as possible to aid in the assessment, it was decided that the clearance analysis would be broken into two phases. Phase I: The location and orientation of the UCCAS fittings, which support and hold the ELC4 in place, would be measured relative to the ISS Analytical Coordinate System (ISSACS) as defined by nine preexisting Space Vision System (SVS) targets affixed to the forward/zenith side of the S1 and S3 truss segments. The location of the outboard edge of the S-TRRJ radiator would also be measured when positioned at the angle of closest approach to CTC2 (gamma = 35.0 degrees). This data would allow the Digital Pre-Assembly Group to predict how the ELC4 would sit on the UCCAS and how that would translate into the clearance between CTC2 and the S-TRRJ radiators. Phase II: After the ELC4 was delivered and installed into the UCCAS, the position of the CTC2 mounting plate on the inboard face of ELC4, would be measured in the ISSACS coordinate system relative to the SVS control points used in Phase I. Although CTC2 would not yet be mounted on ELC4, the working envelope of CTC2 could be mathematically added to the measured position of ELC4 to produce a best estimate for CTC2's mounted location. Comparing CTC2's best estimated location to the S-TRRJ radiator (measured in Phase I); relative to the ISSACS coordinate system, would provide a direct measurement of the expected clearance. Due to the impending delivery of ELC4 (scheduled for January 2011), planning for the Phase I clearance analysis began immediately. Using the Dynamic Onboard Ubiquitous Graphics (DOUG) program, ISAG designed a way to acquire images of the SVS control points on truss segments S1 and S3, the aft facing edge of the S-TRRJ Heat Rejection Subsystem (HRS) radiator, and the three UCCAS latch mechanisms mounted on the zenith face of the S3 truss using the Space Station Remote Manipulator System (SSRMS). To minimize the number of SSRMS movements, the Special Purpose Dexterous Manipulator (SPDM) would be attached to the SSRMS. This would make it possible to park the SPDM in one position and acquire multiple images by changing the viewing orientation of the SPDM body cameras using the pan/tilt units on which they are mounted. Using this implementation concept, ISAG identified four SSRMS/SPDM positions from which the majority of the needed imagery could be acquired. Five additional images would be acquired using the CP-3 external ISS camera mounted on the S1 truss immediately inboard of ELC4. Based on a photogrammetric simulation, it was estimated that the measured location of the HRS radiator and UCCAS latch points would be accurate to about 0.3 in. in each of the three axes relative to ISSACS. Working with ROBO, ISAG collected 78 images of the ISS December 29, 2010. From this imagery, the best 40 were selected for use in the analysis process. The images were radiometrically enhanced to improve color and contrast and loaded into the FotoG analysis software along with the camera parameters and control data, which consisted of the coordinates for the nine SVS targets on the S1 and S3 trusses in the ISSACS coordinate system.

Liddle, Donn↗

A Low-Power High-Speed Smart Sensor Design for Space Exploration Missions

A low-power high-speed smart sensor system based on a large format active pixel sensor (APS) integrated with a programmable neural processor for space exploration missions is presented. The concept of building an advanced smart sensing system is demonstrated by a system-level microchip design that is composed with an APS sensor, a programmable neural processor, and an embedded microprocessor in a SOI CMOS technology. This ultra-fast smart sensor system-on-a-chip design mimics what is inherent in biological vision systems. Moreover, it is programmable and capable of performing ultra-fast machine vision processing in all levels such as image acquisition, image fusion, image analysis, scene interpretation, and control functions. The system provides about one tera-operation-per-second computing power which is a two order-of-magnitude increase over that of state-of-the-art microcomputers. Its high performance is due to massively parallel computing structures, high data throughput rates, fast learning capabilities, and advanced VLSI system-on-a-chip implementation.

Fang, Wai-Chi↗

Analysis of Wallops Flight Test Data Through an Automated COTS System

During the summer of 2004 NASA Langley Research Center flight tested a Synthetic Vision System (SVS) at the Reno/Tahoe International Airport (RNO) and the Wallops Flight Facility (WAL). The SVS included a Runway Incursion Prevention System (RIPS) to improve pilot situational awareness while operating near and on the airport surface. The flight tests consisted of air and ground operations to evaluate and validate the performance of the system. This paper describes the flight test and emphasizes how positioning data was collected, post processed and analyzed through the use of a COTS-derived software system. The system that was developed to analyze the data was constructed within the MATLAB(TM) environment. The software was modified to read the data, perform several if-then scenarios and produce the relevant graphs, figures and tables.

Blackstock, Dexter Lee↗

An investigation of air transportation technology at the Massachusetts Institute of Technology, 1991-1992

There are two completed projects and five new or continuing research activities under the sponsorship of the FAA/NASA Joint University Program as the 1991-92 period ends. A brief summary of some of the continuing research projects is provided. The active research projects are: (1) extensions for the FASA (Final Approach Spacing Advisory) System; (2) radar tracking around a turn; (3) impact of advanced technologies of single pilot IFR operations; (4) system and human limitations in millimeter wave and infrared synthetic vision systems; and (5) differences in party line information usage by operational user groups.

Simpson, Robert W.↗

Image processing for flight crew enhanced situation awareness

This presentation describes the image processing work that is being performed for the Enhanced Situational Awareness System (ESAS) application. Specifically, the presented work supports the Enhanced Vision System (EVS) component of ESAS.

Roberts, Barry↗