3,687 research outputs found
Getting expert systems off the ground: Lessons learned from integrating model-based diagnostics with prototype flight hardware
As an initial attempt to introduce expert system technology into an onboard environment, a model based diagnostic system using the TRW MARPLE software tool was integrated with prototype flight hardware and its corresponding control software. Because this experiment was designed primarily to test the effectiveness of the model based reasoning technique used, the expert system ran on a separate hardware platform, and interactions between the control software and the model based diagnostics were limited. While this project met its objective of showing that model based reasoning can effectively isolate failures in flight hardware, it also identified the need for an integrated development path for expert system and control software for onboard applications. In developing expert systems that are ready for flight, artificial intelligence techniques must be evaluated to determine whether they offer a real advantage onboard, identify which diagnostic functions should be performed by the expert systems and which are better left to the procedural software, and work closely with both the hardware and the software developers from the beginning of a project to produce a well designed and thoroughly integrated application
On-board processing satellite network architecture and control study
The market for telecommunications services needs to be segmented into user classes having similar transmission requirements and hence similar network architectures. Use of the following transmission architecture was considered: satellite switched TDMA; TDMA up, TDM down; scanning (hopping) beam TDMA; FDMA up, TDM down; satellite switched MF/TDMA; and switching Hub earth stations with double hop transmission. A candidate network architecture will be selected that: comprises multiple access subnetworks optimized for each user; interconnects the subnetworks by means of a baseband processor; and optimizes the marriage of interconnection and access techniques. An overall network control architecture will be provided that will serve the needs of the baseband and satellite switched RF interconnected subnetworks. The results of the studies shall be used to identify elements of network architecture and control that require the greatest degree of technology development to realize an operational system. This will be specified in terms of: requirements of the enabling technology; difference from the current available technology; and estimate of the development requirements needed to achieve an operational system. The results obtained for each of these tasks are presented
An autonomous satellite architecture integrating deliberative reasoning and behavioural intelligence
This paper describes a method for the design of autonomous spacecraft, based upon behavioral approaches to intelligent robotics. First, a number of previous spacecraft automation projects are reviewed. A methodology for the design of autonomous spacecraft is then presented, drawing upon both the European Space Agency technological center (ESTEC) automation and robotics methodology and the subsumption architecture for autonomous robots. A layered competency model for autonomous orbital spacecraft is proposed. A simple example of low level competencies and their interaction is presented in order to illustrate the methodology. Finally, the general principles adopted for the control hardware design of the AUSTRALIS-1 spacecraft are described. This system will provide an orbital experimental platform for spacecraft autonomy studies, supporting the exploration of different logical control models, different computational metaphors within the behavioral control framework, and different mappings from the logical control model to its physical implementation
The First Verification Test of Space-Ground Collaborative Intelligence via Cloud-Native Satellites
Recent advancements in satellite technologies and the declining cost of
access to space have led to the emergence of large satellite constellations in
Low Earth Orbit. However, these constellations often rely on bent-pipe
architecture, resulting in high communication costs. Existing onboard inference
architectures suffer from limitations in terms of low accuracy and
inflexibility in the deployment and management of in-orbit applications. To
address these challenges, we propose a cloud-native-based satellite design
specifically tailored for Earth Observation tasks, enabling diverse computing
paradigms. In this work, we present a case study of a satellite-ground
collaborative inference system deployed in the Tiansuan constellation,
demonstrating a remarkable 50\% accuracy improvement and a substantial 90\%
data reduction. Our work sheds light on in-orbit energy, where in-orbit
computing accounts for 17\% of the total onboard energy consumption. Our
approach represents a significant advancement of cloud-native satellite, aiming
to enhance the accuracy of in-orbit computing while simultaneously reducing
communication cost.Comment: Accepted by China Communication
Verifying service continuity in a satellite reconfiguration procedure: application to a satellite
The paper discusses the use of the TURTLE UML profile to model and verify service continuity during dynamic reconfiguration of embedded software, and space-based telecommunication software in particular. TURTLE extends UML class diagrams with composition operators, and activity diagrams with temporal operators. Translating TURTLE to the formal description technique RT-LOTOS gives the profile a formal semantics and makes it possible to reuse verification techniques implemented by the RTL, the RT-LOTOS toolkit developed at LAAS-CNRS. The paper proposes a modeling and formal validation methodology based on TURTLE and RTL, and discusses its application to a payload software application in charge of an embedded packet switch. The paper demonstrates the benefits of using TURTLE to prove service continuity for dynamic reconfiguration of embedded software
Space Applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 1: Executive Summary
Potential applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and to their related ground support functions are explored. The specific tasks which will be required by future space projects are identified. ARAMIS options which are candidates for those space project tasks and the relative merits of these options are defined and evaluated. Promising applications of ARAMIS and specific areas for further research are identified. The ARAMIS options defined and researched by the study group span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks
NASA SpaceCube Next-Generation Artificial-Intelligence Computing for STP-H9-SCENIC on ISS
Recently, Artificial Intelligence (AI) and Machine Learning (ML) capabilities have seen an exponential increase in interest from academia and industry that can be a disruptive, transformative development for future missions. Specifically, AI/ML concepts for edge computing can be integrated into future missions for autonomous operation, constellation missions, and onboard data analysis. However, using commercial AI software frameworks onboard spacecraft is challenging because traditional radiation-hardened processors and common spacecraft processors cannot provide the necessary onboard processing capability to effectively deploy complex AI models. Advantageously, embedded AI microchips being developed for the mobile market demonstrate remarkable capability and follow similar size, weight, and power constraints that could be imposed on a space-based system. Unfortunately, many of these devices have not been qualified for use in space. Therefore, Space Test Program - Houston 9 - SpaceCube Edge-Node Intelligent Collaboration (STP-H9-SCENIC) will demonstrate inflight, cutting-edge AI applications on multiple space-based devices for next-generation onboard intelligence. SCENIC will characterize several embedded AI devices in a relevant space environment and will provide NASA and DoD with flight heritage data and lessons learned for developers seeking to enable AI/ML on future missions. Finally, SCENIC also includes new CubeSat form-factor GPS and SDR cards for guidance and navigation
From Earth to Space: A First Deployment of 5G Core Network on Satellite
Recent developments in the aerospace industry have led to a dramatic
reduction in the manufacturing and launch costs of low Earth orbit satellites.
The new trend enables the paradigm shift of satellite-terrestrial integrated
networks with global coverage. In particular, the integration of 5G
communication systems and satellites has the potential to restructure
next-generation mobile networks. By leveraging the network function
virtualization and network slicing, the orbital 5G core networks will
facilitate the coordination and management of network functions in
satellite-terrestrial integrated networks. We are the first to deploy a
lightweight 5G core network on a real-world satellite to investigate its
feasibility. We conducted experiments to validate the onboard 5G core network
functions. The validated procedures include registration and session setup
procedures. The results show that the 5G core network can function normally and
generate correct signaling.Comment: This paper has been accepted by China Communication
Engineering Resilient Space Systems
Several distinct trends will influence space exploration missions in the next decade. Destinations are
becoming more remote and mysterious, science questions more sophisticated, and, as mission experience
accumulates, the most accessible targets are visited, advancing the knowledge frontier to more difficult,
harsh, and inaccessible environments. This leads to new challenges including: hazardous conditions that
limit mission lifetime, such as high radiation levels surrounding interesting destinations like Europa or
toxic atmospheres of planetary bodies like Venus; unconstrained environments with navigation hazards,
such as free-floating active small bodies; multielement missions required to answer more sophisticated
questions, such as Mars Sample Return (MSR); and long-range missions, such as Kuiper belt exploration,
that must survive equipment failures over the span of decades. These missions will need to be successful
without a priori knowledge of the most efficient data collection techniques for optimum science return.
Science objectives will have to be revised āon the flyā, with new data collection and navigation decisions
on short timescales.
Yet, even as science objectives are becoming more ambitious, several critical resources remain
unchanged. Since physics imposes insurmountable light-time delays, anticipated improvements to the
Deep Space Network (DSN) will only marginally improve the bandwidth and communications cadence to
remote spacecraft. Fiscal resources are increasingly limited, resulting in fewer flagship missions, smaller
spacecraft, and less subsystem redundancy. As missions visit more distant and formidable locations, the
job of the operations team becomes more challenging, seemingly inconsistent with the trend of shrinking
mission budgets for operations support. How can we continue to explore challenging new locations
without increasing risk or system complexity?
These challenges are present, to some degree, for the entire Decadal Survey mission portfolio, as
documented in Vision and Voyages for Planetary Science in the Decade 2013ā2022 (National Research
Council, 2011), but are especially acute for the following mission examples, identified in our recently
completed KISS Engineering Resilient Space Systems (ERSS) study:
1. A Venus lander, designed to sample the atmosphere and surface of Venus, would have to perform
science operations as components and subsystems degrade and fail;
2. A Trojan asteroid tour spacecraft would spend significant time cruising to its ultimate destination
(essentially hibernating to save on operations costs), then upon arrival, would have to act as its
own surveyor, finding new objects and targets of opportunity as it approaches each asteroid,
requiring response on short notice; and
3. A MSR campaign would not only be required to perform fast reconnaissance over long distances
on the surface of Mars, interact with an unknown physical surface, and handle degradations and
faults, but would also contain multiple components (launch vehicle, cruise stage, entry and
landing vehicle, surface rover, ascent vehicle, orbiting cache, and Earth return vehicle) that
dramatically increase the need for resilience to failure across the complex system.
The concept of resilience and its relevance and application in various domains was a focus during the
study, with several definitions of resilience proposed and discussed. While there was substantial variation
in the specifics, there was a common conceptual core that emergedāadaptation in the presence of
changing circumstances. These changes were couched in various waysāanomalies, disruptions,
discoveriesābut they all ultimately had to do with changes in underlying assumptions. Invalid
assumptions, whether due to unexpected changes in the environment, or an inadequate understanding of
interactions within the system, may cause unexpected or unintended system behavior. A system is
resilient if it continues to perform the intended functions in the presence of invalid assumptions.
Our study focused on areas of resilience that we felt needed additional exploration and integration,
namely system and software architectures and capabilities, and autonomy technologies. (While also an
important consideration, resilience in hardware is being addressed in multiple other venues, including
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other KISS studies.) The study consisted of two workshops, separated by a seven-month focused study
period. The first workshop (Workshop #1) explored the āproblem spaceā as an organizing theme, and the
second workshop (Workshop #2) explored the āsolution spaceā. In each workshop, focused discussions
and exercises were interspersed with presentations from participants and invited speakers.
The study period between the two workshops was organized as part of the synthesis activity during the
first workshop. The study participants, after spending the initial days of the first workshop discussing the
nature of resilience and its impact on future science missions, decided to split into three focus groups,
each with a particular thrust, to explore specific ideas further and develop material needed for the second
workshop. The three focus groups and areas of exploration were:
1. Reference missions: address/refine the resilience needs by exploring a set of reference missions
2. Capability survey: collect, document, and assess current efforts to develop capabilities and
technology that could be used to address the documented needs, both inside and outside NASA
3. Architecture: analyze the impact of architecture on system resilience, and provide principles and
guidance for architecting greater resilience in our future systems
The key product of the second workshop was a set of capability roadmaps pertaining to the three
reference missions selected for their representative coverage of the types of space missions envisioned for
the future. From these three roadmaps, we have extracted several common capability patterns that would
be appropriate targets for near-term technical development: one focused on graceful degradation of
system functionality, a second focused on data understanding for science and engineering applications,
and a third focused on hazard avoidance and environmental uncertainty. Continuing work is extending
these roadmaps to identify candidate enablers of the capabilities from the following three categories:
architecture solutions, technology solutions, and process solutions.
The KISS study allowed a collection of diverse and engaged engineers, researchers, and scientists to think
deeply about the theory, approaches, and technical issues involved in developing and applying resilience
capabilities. The conclusions summarize the varied and disparate discussions that occurred during the
study, and include new insights about the nature of the challenge and potential solutions:
1. There is a clear and definitive need for more resilient space systems. During our study period,
the key scientists/engineers we engaged to understand potential future missions confirmed the
scientific and risk reduction value of greater resilience in the systems used to perform these
missions.
2. Resilience can be quantified in measurable termsāproject cost, mission risk, and quality of
science return. In order to consider resilience properly in the set of engineering trades performed
during the design, integration, and operation of space systems, the benefits and costs of resilience
need to be quantified. We believe, based on the work done during the study, that appropriate
metrics to measure resilience must relate to risk, cost, and science quality/opportunity. Additional
work is required to explicitly tie design decisions to these first-order concerns.
3. There are many existing basic technologies that can be applied to engineering resilient space
systems. Through the discussions during the study, we found many varied approaches and
research that address the various facets of resilience, some within NASA, and many more
beyond. Examples from civil architecture, Department of Defense (DoD) / Defense Advanced
Research Projects Agency (DARPA) initiatives, āsmartā power grid control, cyber-physical
systems, software architecture, and application of formal verification methods for software were
identified and discussed. The variety and scope of related efforts is encouraging and presents
many opportunities for collaboration and development, and we expect many collaborative
proposals and joint research as a result of the study.
4. Use of principled architectural approaches is key to managing complexity and integrating
disparate technologies. The main challenge inherent in considering highly resilient space
systems is that the increase in capability can result in an increase in complexity with all of the
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risks and costs associated with more complex systems. What is needed is a better way of
conceiving space systems that enables incorporation of capabilities without increasing
complexity. We believe principled architecting approaches provide the needed means to convey a
unified understanding of the system to primary stakeholders, thereby controlling complexity in
the conception and development of resilient systems, and enabling the integration of disparate
approaches and technologies. A representative architectural example is included in Appendix F.
5. Developing trusted resilience capabilities will require a diverse yet strategically directed
research program. Despite the interest in, and benefits of, deploying resilience space systems, to
date, there has been a notable lack of meaningful demonstrated progress in systems capable of
working in hazardous uncertain situations. The roadmaps completed during the study, and
documented in this report, provide the basis for a real funded plan that considers the required
fundamental work and evolution of needed capabilities.
Exploring space is a challenging and difficult endeavor. Future space missions will require more
resilience in order to perform the desired science in new environments under constraints of development
and operations cost, acceptable risk, and communications delays. Development of space systems with
resilient capabilities has the potential to expand the limits of possibility, revolutionizing space science by
enabling as yet unforeseen missions and breakthrough science observations.
Our KISS study provided an essential venue for the consideration of these challenges and goals.
Additional work and future steps are needed to realize the potential of resilient systemsāthis study
provided the necessary catalyst to begin this process
HL-Pow: A Learning-Based Power Modeling Framework for High-Level Synthesis
High-level synthesis (HLS) enables designers to customize hardware designs
efficiently. However, it is still challenging to foresee the correlation
between power consumption and HLS-based applications at an early design stage.
To overcome this problem, we introduce HL-Pow, a power modeling framework for
FPGA HLS based on state-of-the-art machine learning techniques. HL-Pow
incorporates an automated feature construction flow to efficiently identify and
extract features that exert a major influence on power consumption, simply
based upon HLS results, and a modeling flow that can build an accurate and
generic power model applicable to a variety of designs with HLS. By using
HL-Pow, the power evaluation process for FPGA designs can be significantly
expedited because the power inference of HL-Pow is established on HLS instead
of the time-consuming register-transfer level (RTL) implementation flow.
Experimental results demonstrate that HL-Pow can achieve accurate power
modeling that is only 4.67% (24.02 mW) away from onboard power measurement. To
further facilitate power-oriented optimizations, we describe a novel design
space exploration (DSE) algorithm built on top of HL-Pow to trade off between
latency and power consumption. This algorithm can reach a close approximation
of the real Pareto frontier while only requiring running HLS flow for 20% of
design points in the entire design space.Comment: published as a conference paper in ASP-DAC 202
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