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At least 361 records · Page 20

REE radiation fault model: a tool for organizing and communication radiation test data and construction COTS based spacebourne computing systems

The growth in data rates of instruments on future NASA spacecraft continues to outstrip the improvement in communications bandwidth and processing capabilities of radiation-hardened computers. Sophisticated autonomous operations strategies will further increase the processing workload. Given the reductions in spacecraft size and available power, standard radiation hardened computing systems alone will not be able to address the requirements of future missions. The REE project was intended to overcome this obstacle by developing a COTS- based supercomputer suitable for use as a science and autonomy data processor in most space environments. This development required a detailed knowledge of system behavior in the presence of Single Event Effect (SEE) induced faults so that mitigation strategies could be designed to recover system level reliability while maintaining the COTS throughput advantage. The REE project has developed a suite of tools and a methodology for predicting SEU induced transient fault rates in a range of natural space environments from ground-based radiation testing of component parts. In this paper we provide an overview of this methodology and tool set with a concentration on the radiation fault model and its use in the REE system development methodology. Using test data reported elsewhere in this and other conferences, we predict upset rates for a particular COTS single board computer configuration in several space environments.

Radiation Effects Modeling COTS computers REE SEU ↗

The Cassini spacecraft: Object oriented flight control software

The Cassini Attitude and Articulation Control Subsystem (AACS) is responsible for determining and controlling the spacecraft attitude including instrument pointing, antenna pointing, and thrust vector pointing during velocity change maneuvers. The 12 year mission life, long round-trip light time, and extended periods of coast without continuous ground control drive the AACS flight software design in the directions of autonomy, fault tolerance, and modularity to accommodate planned upgrades in flight. The Cassini AACS Flight Software is depicted in increasing levels of detail using a Context Diagram, Architecture Diagrams (i.e., Dependency Diagrams), an Object Diagram for each object, and a Statechart (i.e., State Transition Diagram) for each object. The detail contained in the diagrams is enhanced and refined during the Requirements and Design Phases of both Subsystem and Software Development. Examples of all the diagrams as well as the criteria for object selection, the advantages of statecharts, and the ease of modifying the design to accommodate changes in scope are described.

Hackney, John C.↗

Model-Based Data Integration and Process Standardization Techniques for Fault Management: A Feasibility Study

This paper describes the theory and considerations in the application of model-based techniques to assimilate information from disjoint knowledge sources for performing NASA's Fault Management (FM)-related activities using the TEAMS® toolset. FM consists of the operational mitigation of existing and impending spacecraft failures. NASA's FM directives have both design-phase and operational-phase goals. This paper highlights recent studies by QSI and DST of the capabilities required in the TEAMS® toolset for conducting FM activities with the aim of reducing operating costs, increasing autonomy, and conforming to time schedules. These studies use and extend the analytic capabilities of QSI's TEAMS® toolset to conduct a range of FM activities within a centralized platform.

Haste, Deepak↗

Monitoring Human Performance on Future Deep SpaceMissions

NASA and the commercial spacecraft community are working diligently to put the first woman on the moon in the 2024 timeframe. At the same time, NASA researchers are thinking about how to solve the even larger challenges that future deep space missions will bring – primary among them is crew autonomy. Deep space mission crews will face communication delays and blackouts, and in those situations, the crew may not have access to Mission Control Center (MCC) experts. They will be dependent on each other and the available information onboard to stay alive, healthy, and achieve the mission. The only conceivable way to meet the challenges of Earth independence is to enable the crew to monitor their own health and performance. A number of technologies are presently being explored by NASA to enable crew self-monitoring. This paper highlights select projects, and provides broader discussion about the need for advanced monitoring technologies

Kritina Anderson↗

Autonomy for SOHO Ground Operations

The SOLAR and HELIOSPHERIC OBSERVATORY (SOHO) project [SOHO Web Page] is being carried out by the European Space Agency (ESA) and the US National Aeronautics and Space Administration (NASA) as a cooperative effort between the two agencies in the framework of the Solar Terrestrial Science Program (STSP) comprising SOHO and other missions. SOHO was launched on December 2, 1995. The SOHO spacecraft was built in Europe by an industry team led by Matra, and instruments were provided by European and American scientists. There are nine European Principal Investigators (PI's) and three American ones. Large engineering teams and more than 200 co-investigators from many institutions support the PI's in the development of the instruments and in the preparation of their operations and data analysis. NASA is responsible for the launch and mission operations. Large radio dishes around the world, which form NASA's Deep Space Network (DSN), are used to track the spacecraft beyond the Earths orbit. Mission control is based at Goddard Space Flight Center in Maryland. The agent group at the NASA Goddard Space Flight Center, in collaboration with JPL, is currently involved with the design and development of an agent-based system to provide intelligent interactions with the control center personnel for SOHO. The basic approach that is being taken is to develop a sub-community of agents for each major subsystem of SOHO and to integrate these sub-communities into an overall SOHO community. Agents in all sub-communities will be capable of advanced understanding (deep reasoning) of the associated spacecraft subsystem.

Truszkowski, Walt↗

Crew Autonomy through Self-Scheduling: Guidelines for Crew Scheduling Performance Envelope and Mitigation Strategies

As NASA considers long-duration exploration missions, it is envisioned that crews will behave more autonomously as compared to low-Earth orbit missions. When considering long duration exploration missions, Flight Operations Directorate (FOD) identified the following benefits and challenges presented on this slide. As missions operate further from Earth, the communication latency between the spacecraft and Mission Control Center will increase requiring the crew to take a more active role in reacting to daily planning. However, one of the key challenges for crew self-scheduling is that we do not want to overwhelm astronauts who are not expert mission planners. This leads to our primary research objective: to first, characterize the human performance envelope for the task of planning and scheduling in the context of human spaceflight operations, then, develop countermeasure aids to mitigate adverse performance effects due to plan complexity, and finally, inform performance standards and guidelines based on research results. Our next steps are to finish summarizing our findings from the experiment in conference papers and our upcoming yearly report. In the upcoming year, we will continue to collect data in analog missions and begin summarizing our recommended standards and guidelines.

user experience↗

Autonomous Optical Navigation for Interplanetary Missions

The automation of interplanetary spacecraft is becoming increasingly desirable to meet various mission requirements. A prototype of an autonomous spacecraft which will fly by an asteroid and comet is slated for flight in mid-1998 as part of NASA's New Millenium Program.

optical↗

Starling Formation-Flying Optical Experiment: Initial Operations and Flight Results

This paper presents initial flight results for angles-only navigation of a swarm of small spacecraft, conducted during the Starling Formation-Flying Optical Experiment (StarFOX). StarFOX is one of four experiments aboard the NASA Starling mission, which consists of four CubeSats launched in July 2023. Angles-only methods apply inter-satellite bearing angles obtained by on-board cameras for navigation, increasing satellite autonomy and enabling new mission concepts. Nevertheless, prior flight demonstrations have only featured one observer and target and have relied upon a-priori target orbit knowledge for initialization, translational maneuvers to resolve target range, and external absolute orbit updates to maintain convergence. StarFOX overcomes these limitations by applying the angles-only Absolute and Relative Trajectory Measurement System (ARTMS), which integrates three novel algorithms. Image Processing detects and tracks multiple targets in images, using multi-hypothesis methods and domain-specific kinematic modeling, and computes target bearing angles. Batch Orbit Determination computes initial swarm orbit estimates from bearing angle batches, via iterative batch least squares and sampling of the weakly observable target range. Sequential Orbit Determination leverages an adaptive, efficient unscented Kalman filter with nonlinear models to refine swarm state estimates over time. Multi-observer measurements shared over an intersatellite link are seamlessly fused to enable robust absolute and relative orbit determination. Initial StarFOX experiments are the first demonstrations of autonomous angles-only navigation for a satellite swarm, including multi-target and multi-observer relative navigation; autonomous initialization of navigation for unknown targets; and simultaneous absolute and relative orbit determination. Relative navigation accuracy of 1% (single observer) and 0.2% (multi-observer) of the inter-satellite range is achieved under challenging measurement conditions. Results demonstrate promising performance with regards to ongoing StarFOX campaigns and the application of angles-only navigation to future distributed missions.

Relative Navigation↗

A vision system planner for increasing the autonomy of the Extravehicular Activity Helper/Retriever

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.

Magee, Michael↗

Autonomous Formation Flying from Ground to Flight

The cost of on-orbit operations remains a significant and increasingly visible concern in the support of satellite missions. Headway has been made in automating some ground operations; however, increased mission complexity and more precise orbital constraints have compelled continuing human involvement in mission design and maneuver planning operations. AI Solutions, Inc. in cooperation with the National Aeronautics and Space Administration's (NASA) Goddard Space Flight Center (GSFC) has tackled these more complex problems through the development of AutoCon as a tool for an automated solution. NASA is using AutoCon to automate the maneuver planning for the Earth Orbiter-1 (EO-1) mission. AutoCon was developed originally as a ground system tool. The EO-1 mission will be using a scaled version of AutoCon on-board the EO-1 satellite to command orbit adjustment maneuvers. The flight version of AutoCon plans maneuvers based on formation flying algorithms developed by GSFC, JPL, and other industry partners. In its fully autonomous mode, an AutoCon planned maneuver will be executed on-board the satellite without intervention from the ground. This paper describes how AutoCon automates maneuver planning for the formation flying constraints of the EO-1 mission. AutoCon was modified in a number of ways to automate the maneuver planning on-board the satellite. This paper describes how the interface and functionality of AutoCon were modified to support the on-board system. A significant component of this modification was the implementation of a data smoother, based on a Kalman filter, that ensures that the spacecraft states estimated by an on-board GPS receiver are as accurate as possible for maneuver planning. This paper also presents the methodology use to scale the AutoCon functionality to fit and execute on the flight hardware. This paper also presents the modes built that allow the incremental phasing in of autonomy. New technologies for autonomous operations are usually received with significant, and probably appropriate trepidation. A number of safeguards have been designed in both AutoCon and the interfacing systems to alleviate the potential of mission-impacting anomalies from the on-board autonomous system. This paper describes the error checking, input data integrity validation and limits set on maneuvers in AutoCon and the on-board system.

Chapman, Keith B.↗

V & V of A Spacecrafts Autonomous Planner Through Extended Automation

We have introduced and used significant automation during the verification and validation (V & V) of a spacecraft's autonomous planner. This paper describes the problem we faced, the solution we employed, and the applicability of our approach in a general V & V setting.

planning↗

Agent Technology from a NASA Perspective

NASA's drive toward realizing higher levels of autonomy, in both its ground and space systems, is supporting an active and growing interest in agent technology. This paper will address the expanding research in this exciting technology area. As examples of current work, the Lights-Out Ground Operations System (LOGOS), under prototyping at the Goddard Space Flight Center (GSFC), and the spacecraft-oriented Remote Agent project under development at the Ames Research Center (ARC) and the Jet Propulsion Laboratory (JPL) will be presented.

Truszkowski, Walt↗

Inserting New Technologies into Human-Computer Interfaces for Future Lunar and Mars Missions

Plans call for human cislunar operations and lunar surface access, to prepare for eventual Mars missions. NASA will also develop new opportunities in lunar orbit that provide the foundation and act as a gateway for human exploration deeper into the solar system. Current human spaceflight is complex and requires as many as fifty people to support the International Space Station (ISS) Mission Control Center (MCC) in Houston, Texas. These flight controllers in the front and back rooms of the MCC, serve as an extra pair of eyes overseeing the numerous station systems. Deep space missions - to the moon, Mars, and beyond - will be more complex and place challenging mission constraints on the crew. As the round-trip communication delays increase in deep space exploration, more on-board systems autonomy and functionality will be needed to maintain and control the vehicle. These mission constraints will change the Earth-based ground control approach and will demand efficient and effective human-computer interfaces (HCI) to control a highly complex vehicle or habitat system. All of this necessitates a different approach to designing and developing spacecraft and habitats. In the beginning of new human spaceflight programs, focus is typically on launch vehicle and uncrewed spacecraft design and development. The reasoning behind this focus to enable flight testing of an integrated launch vehicle and spacecraft system to ensure it will be safe enough to allow humans on board. This is an essential process for new spacecraft, however, the practical effect is a lack of funding for the spacecraft’s human interfaces development. It can be many years before the human interface development begins, putting it late in the spacecraft lifecycle, when almost all other spacecraft systems and subsystems are already in place. This forces the usage of existing and proven technologies for the HCI interfaces. We posit that putting the human first in a spacecraft design process will yield a more effective spacecraft for exploration and long duration missions. NASA Human Research Program (HRP) has identified inadequate HCI as a risk for future missions. New tools and procedures to aid the crew in operating a complex spacecraft will be required. This paper discusses ongoing activities in the development of the next generation HCI components and systems, and a new approach toward human interfaces for spacecraft.

Salazar, George A.↗

Autonomous Formation Flying from the Ground to Flight

The cost of on-orbit operations remains a significant and increasingly visible concern in the support of satellite missions. Headway has been made in automating some ground operations; however, increased mission complexity and more precise orbital constraints have compelled continuing human involvement in mission design and maneuver planning operations. AI Solutions, Inc. in cooperation with the National Aeronautics and Space Administration's (NASA) Goddard Space Flight Center (GSFC) has tackled these more complex problems through the development of AutoCon(TM) as a tool for an automated solution. NASA is using AutoCon(TM) to automate the maneuver planning for the Earth Orbiter-1 (EO-1) mission. AutoCon(TM) was developed originally as a ground system tool. The EO-1 mission will be using a scaled version of AutoCon(TM) on-board the EO-1 satellite to command orbit adjustment maneuvers. The flight version of AutoCon(TM) plans maneuvers based on formation flying algorithms developed by GSFC, JPL, and other industry partners. In its fully autonomous mode, an AutoCon(TM) planned maneuver will be executed on-board the satellite without intervention from the ground. This paper describes how AutoCon(TM) automates maneuver planning for the formation flying constraints of the EO-1 mission. AutoCon(TM) was modified in a number of ways to automate the maneuver planning on-board the satellite. This paper describes how the interface and functionality of AutoCon(TM) were modified to support the on-board system. A significant component of this modification was the implementation of a data smoother, based on a Kalman filter, that ensures that the spacecraft states estimated by an on-board GPS receiver are as accurate as possible for maneuver planning. This paper also presents the methodology used to scale the AutoCon(TM) functionality to fit and execute on the flight hardware. This paper also presents the modes built into the system that allow the incremental phasing in of autonomy. New technologies for autonomous operations are usually received with significant, and probably appropriate, trepidation. A number of safeguards have been designed in both AutoCon(TM) and the interfacing systems to alleviate the potential of mission-impacting anomalies from the on-board autonomous system. This paper describes the error checking, input data integrity validation, and limits set on maneuvers in AutoCon(TM) and the on-board system.

Chapman, Keith B.↗

Architectural and Behavioral Systems Design Methodology and Analysis for Optimal Habitation in a Volume-Limited Spacecraft for Long Duration Flights

As our human spaceflight missions change as we reach towards Mars, the risk of an adverse behavioral outcome increases, and requirements for crew health, safety, and performance, and the internal architecture, will need to change to accommodate unprecedented mission demands. Evidence shows that architectural arrangement and habitability elements impact behavior. Net habitable volume is the volume available to the crew after accounting for elements that decrease the functional volume of the spacecraft. Determination of minimum acceptable net habitable volume and associated architectural design elements, as mission duration and environment varies, is key to enabling, maintaining, andor enhancing human performance and psychological and behavioral health. Current NASA efforts to derive minimum acceptable net habitable volumes and study the interaction of covariates and stressors, such as sensory stimulation, communication, autonomy, and privacy, and application to internal architecture design layouts, attributes, and use of advanced accommodations will be presented. Furthermore, implications of crew adaptation to available volume as they transfer from Earth accommodations, to deep space travel, to planetary surface habitats, and return, will be discussed.

Human Space Flight↗

Coordinating Multiple Spacecraft Assets for Joint Science Campaigns

This paper describes technology to support a new paradigm of space science campaigns. These campaigns enable opportunistic science observations to be autonomously coordinated between multiple spacecraft. Coordinated spacecraft can consist of multiple orbiters, landers, rovers, or other in-situ vehicles (such as an aerobot). In this paradigm, opportunistic science detections can be cued by any of these assets where additional spacecraft are requested to take further observations characterizing the identified event or surface feature. Such coordination will enable a number of science campaigns not possible with present spacecraft technology. Examples from Mars include enabling rapid data collection from multiple craft on dynamic events such as new Mars dark slope streaks, dust-devils or trace gases. Technology to support the identification of opportunistic science events and/or the re-tasking of a spacecraft to take new measurements of the event is already in place on several individual missions such as the Mars Exploration Rover (MER) Mission and the Earth Observing One (EO1) Mission. This technology includes onboard data analysis techniques as well as capabilities for planning and scheduling. This paper describes how these techniques can be cue and coordinate multiple spacecraft in observing the same science event from their different vantage points.

automated commanding↗

The Impact of Information Technology on the Design, Development, and Implementation of a Lunar Exploration Mission

From the beginning to the present expeditions to the Moon have involved a large investment of human labor. This has been true for all aspects of the process, from the initial design of the mission, whether scientific or technological, through the development of the instruments and the spacecraft, to the flight and operational phases. In addition to the time constraints that this situation imposes, there is also a significant cost associated with the large labor costs. As a result lunar expeditions have been limited to a few robotic missions and the manned Apollo program missions of the 1970s. With the rapid rise of the new information technologies, new paradigms are emerging that promise to greatly reduce both the time and cost of such missions. With the rapidly increasing capabilities of computer hardware and software systems, as well as networks and communication systems, a new balance of work is being developed between the human and the machine system. This new balance holds the promise of greatly increased exploration capability, along with dramatically reduced design, development, and operating costs. These new information technologies, utilizing knowledge-based software and very highspeed computer systems, will provide new design and development tools, scheduling mechanisms, and vehicle and system health monitoring capabilities that have hitherto been unavailable to the mission and spacecraft designer and the system operator. This paper will utilize typical lunar missions, both robotic and crewed, as a basis to describe and illustrate how these new information system technologies could be applied to all aspects such missions. In particular, new system design tradeoff tools will be described along with technologies that will allow a very much greater degree of autonomy of exploration vehicles than has heretofore been possible. In addition, new information technologies that will significantly reduce the human operational requirements will be discussed.

Gross, Anthony R.↗

AI-enabled Autonomous Systems: Space Power Applications

Advances in artificial intelligence (A.I.) over the last decade opens new opportunities for NASA to advance the state of the art in autonomous missions beyond low-Earth-orbit. Still, many challenges remain before these technologies can be deployed on manned spacecraft. This presentation summarizes many of these challenges, provides recommendations on how to address these problems, and provides an example of NASA’s Autonomous Power Controller's A.I.-based fault detection system.

Artificial Intelligence↗