SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Robotic vision system measures small gaps between nearly parallel tubes. Robot-held video camera examines closely spaced tubes while computer determines gaps between tubes. Video monitor simultaneously displays data on gaps.
This paper investigates concepts for visual operator aids required for effective telerobotic control. Operator visual aids, as defined here, mean any operational enhancement that improves man-machine control through the visual system. These concepts were derived as part of a study of vision issues for space teleoperation. Extensive literature on teleoperation, robotics, and human factors was surveyed to definitively specify appropriate requirements. This paper presents these visual aids in three general categories of camera/lighting functions, display enhancements, and operator cues. In the area of camera/lighting functions concepts are discussed for: (1) automatic end effector or task tracking; (2) novel camera designs; (3) computer-generated virtual camera views; (4) computer assisted camera/lighting placement; and (5) voice control. In the technology area of display aids, concepts are presented for: (1) zone displays, such as imminent collision or indexing limits; (2) predictive displays for temporal and spatial location; (3) stimulus-response reconciliation displays; (4) graphical display of depth cues such as 2-D symbolic depth, virtual views, and perspective depth; and (5) view enhancements through image processing and symbolic representations. Finally, operator visual cues (e.g., targets) that help identify size, distance, shape, orientation and location are discussed.
The Extravehicular Activity Retriever (EVAR) is a robotic device currently being developed by the Automation and Robotics Division at the NASA Johnson Space Center to support activities in the neighborhood of the Space Shuttle or Space Station Freedom. As the name implies, the Retriever's primary function will be to provide the capability to retrieve tools, equipment or other objects which have become detached from the spacecraft, but it will also be able to rescue a crew member who may have become inadvertently de-tethered. Later goals will include cooperative operations between a crew member and the Retriever such as fetching a tool that is required for servicing or maintenance operations. This report documents a preliminary design for a Vision System Planner (VSP) for the EVAR that is capable of achieving visual objectives provided to it by a high level task planner. Typical commands which the task planner might issue to the VSP relate to object recognition, object location determination, and obstacle detection. Upon receiving a command from the task planner, the VSP then plans a sequence of actions to achieve the specified objective using a model-based reasoning approach. This sequence may involve choosing an appropriate sensor, selecting an algorithm to process the data, reorienting the sensor, adjusting the effective resolution of the image using lens zooming capability, and/or requesting the task planner to reposition the EVAR to obtain a different view of the object. An initial version of the Vision System Planner which realizes the above capabilities using simulated images has been implemented and tested. The remaining sections describe the architecture and capabilities of the VSP and its relationship to the high level task planner. In addition, typical plans that are generated to achieve visual goals for various scenarios will be discussed. Specific topics to be addressed will include object search strategies, repositioning of the EVAR to improve the quality of information obtained from the sensors, complementary usage of the sensors and redundant capabilities.
The Extravehicular Activity Retriever (EVAR) is a robotic device currently being developed by the Automation and Robotics Division at the NASA Johnson Space Center to support activities in the neighborhood of the Space Shuttle or Space Station Freedom. As the name implies, the Retriever's primary function will be to provide the capability to retrieve tools and equipment or other objects which have become detached from the spacecraft, but it will also be able to rescue a crew member who may have become inadvertently de-tethered. Later goals will include cooperative operations between a crew member and the Retriever such as fetching a tool that is required for servicing or maintenance operations. This paper documents a preliminary design for a Vision System Planner (VSP) for the EVAR that is capable of achieving visual objectives provided to it by a high level task planner. Typical commands which the task planner might issue to the VSP relate to object recognition, object location determination, and obstacle detection. Upon receiving a command from the task planner, the VSP then plans a sequence of actions to achieve the specified objective using a model-based reasoning approach. This sequence may involve choosing an appropriate sensor, selecting an algorithm to process the data, reorienting the sensor, adjusting the effective resolution of the image using lens zooming capability, and/or requesting the task planner to reposition the EVAR to obtain a different view of the object. An initial version of the Vision System Planner which realizes the above capabilities using simulated images has been implemented and tested. The remaining sections describe the architecture and capabilities of the VSP and its relationship to the high level task planner. In addition, typical plans that are generated to achieve visual goals for various scenarios are discussed. Specific topics to be addressed will include object search strategies, repositioning of the EVAR to improve the quality of information obtained from the sensors, and complementary usage of the sensors and redundant capabilities.
With the maturation of various developing robot control schemes, it is becoming extremely important that the technical community evaluate the performance of these various control technologies against an established baseline to determine which technology provides the most reliable robust, and safe on-orbit robot control. The Supervisory Telerobotics Laboratory (STELER) at JPL has developed a unique robot control capability which has been evaluated by the NASA technical community and found useful for augmenting both the operator interface and control of intended robotic systems on-board the Space Station. As part of the technology development and prototyping effort, the STELER team has been evaluating the performance of different control modes; namely, teleoperation under position, or rate, control, teleoperation with force reflection and shared control. Nine trained subjects were employed in the performance evaluation involving several high fidelity servicing tasks. Four types of operator performance data were collected; task completion time, average force, peak force, and number of operator successes and errors. This paper summarizes the results of this performance evaluation.
The present conference discusses such neural networks (NN) related topics as their current development status, NN architectures, NN learning rules, NN optimization methods, NN temporal models, NN control methods, NN pattern recognition systems and applications, biological and biomedical applications of NNs, VLSI design techniques for NNs, NN systems simulation, fuzzy logic, and genetic algorithms. Attention is given to missileborne integrated NNs, adaptive-mixture NNs, implementable learning rules, an NN simulator for travelling salesman problem solutions, similarity-based forecasting, NN control of hypersonic aircraft takeoff, NN control of the Space Shuttle Arm, an adaptive NN robot manipulator controller, a synthetic approach to digital filtering, NNs for speech analysis, adaptive spline networks, an anticipatory fuzzy logic controller, and encoding operations for fuzzy associative memories.
This video (24:43) shows how the Remapper Project helps with many problems including vision problems. It shows the Remapper in action as it tracks several objects around the moon. The video is narrated by Dr. Richard Juday, Robotic Vision Manager at the Johnson Space Center.
On this third day of the STS-74 mission, the flight crew, Cmdr. Kenneth Cameron, Pilot James Halsell, and Mission Specialists William McArthur, Jerry Ross, and Chris Hadfield successfully connect the Russian-made docking module to the Space Shuttle using the shuttle's robot arm. There is a live, in-orbit press interview with the astronauts from inside the Russian docking module regarding the status of the mission thus far. The docking module will remain with Mir after the two spacecraft have undocked.
On this third day of the STS-72 mission, the flight crew, Cmdr. Brian Duffy, Pilot Brent W. Jett, and Mission Specialists Leroy Chiao, Daniel T. Barry, Winston E. Scott, and Koichi Wakata (NASDA), awakened to a traditional Japanese song, 'Sea in Springtime.' Wakata, using the shuttle's robot arm, successfully retrieved the Japanese Space Flyer Unit (SFU) satellite and berthed it in the shuttle's cargo bay. Duffy and Wakata were interviewed, via satellite, by Japanese journalists and reporters in Houston, Texas. Earth views include cloud cover, storm systems, Africa and several other land masses.
External shots of the International Space Station (ISS) show the ISS' robotic arm in grappling position in preparation for lifting an oxygen tank out of the payload bay of Atlantis. Mission Specialists Michael Gernhardt and Jim Reilly are seen as they perform their spacewalks, installing the oxygen tank in place on the ISS.
The NASA Strategic Plan identifies the long-term goal to provide safe and affordable space access, orbital transfer, and interplanetary transportation capabilities to enable research, human exploration, and the commercial development of space; and to conduct human and robotic missions to planets and other bodies in our solar system. Numerous scientific and engineering breakthroughs will be required to develop the technology necessary to achieve this goal. Critical technologies include advanced vehicle primary and secondary structure, radiation protection, propulsion and power systems, fuel storage, electronics and devices, sensors and science instruments, and medical diagnostics and treatment. Advanced materials with revolutionary new capabilities are an essential element of each of these technologies. This paper discusses those materials best suited for aerospace vehicle structure and highlights the enormous potential of one revolutionary new material, carbon nanotubes.
On the sixth day of the STS-110 mission, Mission Specialists Jerry Ross and Lee Morin are seen in the second extravehicular activity (EVA) of this mission to the International Space Station (ISS). The video opens with the two astronauts preparing for the EVA, which will be Ross' eighth, thereby making him the most experienced space-walker in history. This pair of astronauts has been dubbed the Silver Team, largely because they both have grandchildren. As Ross and Morin prepare for the EVA by breathing pure oxygen and experiencing reduced pressure, Mission Specialist Ellen Ochoa maneuvers the robotic arm towards the hatch of the Space Shuttle Atlantis. There is video footage of the astronauts exiting the hatch and preparing their tools and work areas. During this EVA the astronauts will install two tripod-like struts, which will serve as structural attachments between the S-0 truss segment and the Destiny Laboratory Module. While the construction takes place, helmet mounted cameras on the spacesuits provide live video of the astronauts at work, most often using a pistol grip tool used as an electric ratcheting-type drill to loosen and tighten bolts. Following the installation of the struts, Ross and Morin remove the draglinks and keelpins that had functioned to hold the S0 truss structure securely in Atlantis' payload bay during the launch sequence of the mission. They move these to temporary storage positions, bolted on the outside of the ISS. They then install an umbilical system that will provide power to the Mobile Transporter (MT).
We have developed a model-based, distributed architecture that integrates diverse components in a system designed for lunar and planetary surface operations: an astronaut's space suit, cameras, rover/All-Terrain Vehicle (ATV), robotic assistant, other personnel in a local habitat, and a remote mission support team (with time delay). Software processes, called agents, implemented in the Brahms language, run on multiple, mobile platforms. These mobile agents interpret and transform available data to help people and robotic systems coordinate their actions to make operations more safe and efficient. The Brahms-based mobile agent architecture (MAA) uses a novel combination of agent types so the software agents may understand and facilitate communications between people and between system components. A state-of-the-art spoken dialogue interface is integrated with Brahms models, supporting a speech-driven field observation record and rover command system (e.g., return here later and bring this back to the habitat ). This combination of agents, rover, and model-based spoken dialogue interface constitutes a personal assistant. An important aspect of the methodology involves first simulating the entire system in Brahms, then configuring the agents into a run-time system.
Joint position sensors are necessary for most robot control systems. A single position sensor failure in a normal robot system can greatly degrade performance. This paper presents a method to obtain position information from Cartesian accelerometers without integration. Depending on the number and location of the accelerometers. the proposed system can tolerate the loss of multiple position sensors. A solution technique suitable for real-time implementation is presented. Simulations were conducted using 5 triaxial accelerometers to recover from the loss of up to 4 joint position sensors on a 7 degree of freedom robot moving in general three dimensional space. The simulations show good estimation performance using non-ideal accelerometer measurements.
The NASA Glenn Research Center at Lewis Field, in partnership with U.S. industries, universities, and other Government institutions, is responsible for developing critical technologies that address national priorities in aeropropulsion and space applications. Our work is focused on research for new aeropropulsion technologies, aerospace power, microgravity science (fluids and combustion), electric propulsion, and communications technologies for aeronautics, space, and aerospace applications. As NASA s premier center for aeropropulsion, aerospace power, and turbomachinery, our role is to conduct world-class research and to develop key technologies. We contribute to economic growth and national security through safe, superior, and environmentally compatible U.S. civil and military aircraft propulsion systems. Our Aerospace Power Program supports all NASA Enterprises and major programs, including the International Space Station, Advanced Space Transportation, and new initiatives in human and robotic exploration. Glenn Research Center leads NASA s research in the microgravity science disciplines of fluid physics, combustion science, and acceleration measurement. Almost every space shuttle science mission has had an experiment managed by NASA Glenn, and we have conducted a wide array of similar experiments on the International Space Station. The Glenn staff consists of over 3200 civil service employees and support service contractor personnel. Scientists and engineers comprise more than half of our workforce, with technical specialists, skilled workers, and an administrative staff supporting them. We aggressively strive for technical excellence through continuing education, increased diversity in our workforce, and continuous improvement in our management and business practices so that we can expand the boundaries of aeronautics, space, and aerospace technology. Glenn Research Center is a unique facility located in northeast Ohio. Situated on 350 acres of land adjacent to the Cleveland Hopkins International Airport, Glenn comprises more than 140 buildings, including 24 major facilities and over 500 specialized research and test facilities. Additional facilities are located at Plum Brook Station, which is about 50 miles west of Cleveland. Plum Brook Station has four large, major, world-class facilities for space research available for Government and industry programs. Knowledge is the end product of our activities. The R&T reports help make this knowledge fully available to potential users the aircraft engine industry, the space industry, the energy industry, the automotive industry, the aerospace industry, and others. It is organized so that a broad cross section of the community can readily use it. Each article begins with a short introductory paragraph that should prove valuable for the layperson. These articles summarize the progress made during the year in various technical areas and portray the technical and administrative support associated with Glenn s technology programs. We hope that this information is useful to all. If additional information is desired, readers are encouraged to contact the researchers identified at the end of each article and to visit Glenn on the World Wide Web at http://www.grc.nasa.gov.
High energy power and propulsion capability roadmap - general background and introduction. Advanced telescopes and observatories and scientific instruments and sensors capability roadmaps - general background and introduction. Space communications capability roadmap interim review. Robotic access to planetary surface capability roadmap. Human health and support systems capability roadmap progress review.
To enable long-duration, more distant human and robotic missions for the Vision for Space Exploration, as well as safer, lighter, quieter, and more fuel efficient vehicles for aeronautics and space transportation, NASA is developing instrumentation and material technologies. The high-temperature capabilities of thin-film ceramic thermocouples are being explored at the NASA Glenn Research Center by the Sensors and Electronics Branch and the Ceramics Branch in partnership with Case Western Reserve University (CWRU). Glenn s Sensors and Electronics Branch is developing thin-film sensors for surface measurement of strain, temperature, heat flux, and surface flow in propulsion system research. Glenn s Ceramics Branch, in conjunction with CWRU, is developing structural and functional ceramic technology for aeropropulsion and space propulsion.
The Lunar Reconnaissance Orbiter (LRO) is the first mission of the Robotic Lunar Exploration Program (RLEP), a space exploration venture to the Moon, Mars and beyond. The LRO mission includes spacecraft developed by NASA Goddard Space Flight Center (GSFC) and seven instruments built by GSFC, Russia, and contractors across the nation. LRO is defined as a measurement mission, not a science mission. It emphasizes the overall objectives of obtaining data to facilitate returning mankind safely to the Moon in preparation for an eventual manned mission to Mars. As the first mission in response to the President's commitment of the journey of exploring the solar system and beyond: returning to the Moon in the next decade, then venturing further into the solar system, ultimately sending humans to Mars and beyond, LRO has high-visibility to the public but limited resources and a tight schedule. This paper demonstrates how NASA's Lunar Reconnaissance Orbiter Mission project office incorporated reliability analyses in assessing risks and performing design tradeoffs to ensure mission success. Risk assessment is performed using NASA Procedural Requirements (NPR) 8705.5 - Probabilistic Risk Assessment (PRA) Procedures for NASA Programs and Projects to formulate probabilistic risk assessment (PRA). As required, a limited scope PRA is being performed for the LRO project. The PRA is used to optimize the mission design within mandated budget, manpower, and schedule constraints. The technique that LRO project office uses to perform PRA relies on the application of a component failure database to quantify the potential mission success risks. To ensure mission success in an efficient manner, low cost and tight schedule, the traditional reliability analyses, such as reliability predictions, Failure Modes and Effects Analysis (FMEA), and Fault Tree Analysis (FTA), are used to perform PRA for the large system of LRO with more than 14,000 piece parts and over 120 purchased or contractor built components.