Automated Planning for Spacecraft and Mission Design
We present an AI planning system that automatically generates and evaluates mission plans for specified spacecraft designs.
planning↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Space Shuttle processing - A case study in artificial intelligence
A scheduling system incorporating AI is described and applied to the automated processing of the Space Shuttle. The unique problem of addressing the temporal, resource, and orbiter-configuration requirements of shuttle processing is described with comparisons to traditional project management for manufacturing processes. The present scheduling system is developed to handle the late inputs and complex programs that characterize shuttle processing by incorporating fixed preemptive scheduling, constraint-based simulated annealing, and the characteristics of an 'anytime' algorithm. The Space-Shuttle processing environment is modeled with 500 activities broken down into 4000 subtasks and with 1600 temporal constraints, 8000 resource constraints, and 3900 state requirements. The algorithm is shown to scale to very large problems and maintain anytime characteristics suggesting that an automated scheduling process is achievable and potentially cost-effective.
Mollikarimi, Cindy↗
The use of an automated flight test management system in the development of a rapid-prototyping flight research facility
An automated flight test management system (ATMS) and its use to develop a rapid-prototyping flight research facility for artificial intelligence (AI) based flight systems concepts are described. The ATMS provides a flight test engineer with a set of tools that assist in flight planning and simulation. This system will be capable of controlling an aircraft during the flight test by performing closed-loop guidance functions, range management, and maneuver-quality monitoring. The rapid-prototyping flight research facility is being developed at the Dryden Flight Research Facility of the NASA Ames Research Center (Ames-Dryden) to provide early flight assessment of emerging AI technology. The facility is being developed as one element of the aircraft automation program which focuses on the qualification and validation of embedded real-time AI-based systems.
Duke, Eugene L.↗
A framework for qualitative reasoning about solid objects
Predicting the behavior of a qualitatively described system of solid objects requires a combination of geometrical, temporal, and physical reasoning. Methods based upon formulating and solving differential equations are not adequate for robust prediction, since the behavior of a system over extended time may be much simpler than its behavior over local time. A first-order logic, in which one can state simple physical problems and derive their solution deductively, without recourse to solving the differential equations, is discussed. This logic is substantially more expressive and powerful than any previous AI representational system in this domain.
Davis, E.↗
Artificial intelligence and design: Opportunities, research problems and directions
The issues of industrial productivity and economic competitiveness are of major significance in the U.S. at present. By advancing the science of design, and by creating a broad computer-based methodology for automating the design of artifacts and of industrial processes, we can attain dramatic improvements in productivity. It is our thesis that developments in computer science, especially in Artificial Intelligence (AI) and in related areas of advanced computing, provide us with a unique opportunity to push beyond the present level of computer aided automation technology and to attain substantial advances in the understanding and mechanization of design processes. To attain these goals, we need to build on top of the present state of AI, and to accelerate research and development in areas that are especially relevant to design problems of realistic complexity. We propose an approach to the special challenges in this area, which combines 'core work' in AI with the development of systems for handling significant design tasks. We discuss the general nature of design problems, the scientific issues involved in studying them with the help of AI approaches, and the methodological/technical issues that one must face in developing AI systems for handling advanced design tasks. Looking at basic work in AI from the perspective of design automation, we identify a number of research problems that need special attention. These include finding solution methods for handling multiple interacting goals, formation problems, problem decompositions, and redesign problems; choosing representations for design problems with emphasis on the concept of a design record; and developing approaches for the acquisition and structuring of domain knowledge with emphasis on finding useful approximations to domain theories. Progress in handling these research problems will have major impact both on our understanding of design processes and their automation, and also on several fundamental questions that are of intrinsic concern to AI. We present examples of current AI work on specific design tasks, and discuss new directions of research, both as extensions of current work and in the context of new design tasks where domain knowledge is either intractable or incomplete. The domains discussed include Digital Circuit Design, Mechanical Design of Rotational Transmissions, Design of Computer Architectures, Marine Design, Aircraft Design, and Design of Chemical Processes and Materials. Work in these domains is significant on technical grounds, and it is also important for economic and policy reasons.
Amarel, Saul↗
Generative AI for Power Grid Operations
Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Social Bias in AI and its Implications
Previous studies have documented many different types of biases that exist in artificial intelligence (AI) and machine learning (ML) systems. We reviewed the literature on AI and ML bias with a focus on social implications and found that bias in AI and ML can potentially have harmful social impacts on individuals and/or groups of people. By affecting people differently according to characteristics such as race, gender, or sexual orientation, AI and ML systems may lead to harm by exacerbating social inequities. We recount examples of issues that have occurred in systems that use technology that might be used at NASA and elsewhere so that similar issues might be identified and mitigated in future systems. We also provide interested parties with a gateway into existing work on social bias in AI and ML systems.
artificial intelligence (AI)↗
An integrated information retrieval and document management system
This paper describes the requirements and prototype development for an intelligent document management and information retrieval system that will be capable of handling millions of pages of text or other data. Technologies for scanning, Optical Character Recognition (OCR), magneto-optical storage, and multiplatform retrieval using a Standard Query Language (SQL) will be discussed. The semantic ambiguity inherent in the English language is somewhat compensated-for through the use of coefficients or weighting factors for partial synonyms. Such coefficients are used both for defining structured query trees for routine queries and for establishing long-term interest profiles that can be used on a regular basis to alert individual users to the presence of relevant documents that may have just arrived from an external source, such as a news wire service. Although this attempt at evidential reasoning is limited in comparison with the latest developments in AI Expert Systems technology, it has the advantage of being commercially available.
Coles, L. Stephen↗
Using neural networks and Dyna algorithm for integrated planning, reacting and learning in systems
The traditional AI answer to the decision making problem for a robot is planning. However, planning is usually CPU-time consuming, depending on the availability and accuracy of a world model. The Dyna system generally described in earlier work, uses trial and error to learn a world model which is simultaneously used to plan reactions resulting in optimal action sequences. It is an attempt to integrate planning, reactive, and learning systems. The architecture of Dyna is presented. The different blocks are described. There are three main components of the system. The first is the world model used by the robot for internal world representation. The input of the world model is the current state and the action taken in the current state. The output is the corresponding reward and resulting state. The second module in the system is the policy. The policy observes the current state and outputs the action to be executed by the robot. At the beginning of program execution, the policy is stochastic and through learning progressively becomes deterministic. The policy decides upon an action according to the output of an evaluation function, which is the third module of the system. The evaluation function takes the following as input: the current state of the system, the action taken in that state, the resulting state, and a reward generated by the world which is proportional to the current distance from the goal state. Originally, the work proposed was as follows: (1) to implement a simple 2-D world where a 'robot' is navigating around obstacles, to learn the path to a goal, by using lookup tables; (2) to substitute the world model and Q estimate function Q by neural networks; and (3) to apply the algorithm to a more complex world where the use of a neural network would be fully justified. In this paper, the system design and achieved results will be described. First we implement the world model with a neural network and leave Q implemented as a look up table. Next, we use a lookup table for the world model and implement the Q function with a neural net. Time limitations prevented the combination of these two approaches. The final section discusses the results and gives clues for future work.
Lima, Pedro↗
Artificial Intelligence Medical Support for Long-Duration Space Missions
We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible. In order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations. As a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios. Then, question and answer tasks should be designed to interrogate the model for predictions and recommendations, with acceptable answers already identified.
Artificial Intelligence↗
Hydrogen-Recovery Using an AI-Arc-Plasma Learning Operational System for Produced Water (HALO)
recovery of hydrogen from produced water waste stream
02 PETROLEUM↗
Exploring Enhancements to Search Systems User Experience with AI-features: Design Explorations & Feedback [Slides]
Abstract not provided.
97 MATHEMATICS AND COMPUTING↗
Boundary Layer Exploration of Aerosols and Clouds ON Ships (BEACONS)
BEACONS aims to demonstrate autonomous deployment of a shipborne system that routinely observes atmospheric, cloud, and aerosol properties. These observations enable scientists to study aerosol-cloud interactions, advancing energy resilience and the Department of Energy's mission to improve model prediction. Key Project Outcomes Autonomous deployment of aerosol and cloud measurement systems aboard a Pasha Hawaii Marjorie C commercial ship. Six-month campaign for continuous, high-resolution aerosol and cloud data collection with minimal crew intervention. Development and testing of instruments in two phases, advancing from essential to complex systems. AI-ready processed data on the BEACONS Data Platform for near-real-time data access. Validation of aerosol-cloud interaction hypotheses for improved modeling. Establishment of best practices for instrument deployment in marine environments. Scientific collaboration to support the U.S. Department of Energy (DOE) Biological and Environmental Research (BER) mission.
54 ENVIRONMENTAL SCIENCES↗
An intelligent robotics control scheme
The problem of robot control is viewed at the level of communicating high-level commands produced by intelligent algorithms to the actuator/sensor controllers. Four topics are considered in the design of an integrated control and communications scheme for an intelligent robotic system: the use of abstraction spaces, hierarchical versus heterarchical control, distributed processing, and the interleaving of the steps of plan creation and plan execution. A scheme is presented for an n-level distributed hierarchical/heterarchical control system that effectively interleaves intelligent planning, execution, and sensory feedback. A three-level version of this scheme has been successfully implemented in the Intelligent Systems Research Lab at NASA Langley Research Center. This implementation forms the control structure for DAISIE (Distributed Artificially Intelligent System for Interacting with the Environment), a testbed system integrating AI software with robotics hardware.
Orlando, N. E.↗
How to feed a spacecraft
The uplink process between ground computers and the spacecraft computer is examined. Data is uplinked to a spacecraft by a load (a sequence of preplanned commands) or by real-time commands; the differences between these two types of uplinks are discussed. The sequencing of a load involves: (1) request generation, (2) request integration, (3) reference generation, and (4) transmitting the load. The functions of each of the sequencing steps are described. The development of new sequencing methods using expert systems and AI is being studied. A symbolic processing software which has the ability to transmit data typed into a computer in English was developed. Consideration is given to the composition, capabilities of the parser, and application of the symbolic processing software to the Comet Renedezvous Asteroid Flyby spacecraft.
Mclaughlin, William↗
Artificial intelligent decision support for low-cost launch vehicle integrated mission operations
The feasibility, benefits, and risks associated with Artificial Intelligence (AI) Expert Systems applied to low cost space expendable launch vehicle systems are reviewed. This study is in support of the joint USAF/NASA effort to define the next generation of a heavy-lift Advanced Launch System (ALS) which will provide economical and routine access to space. The significant technical goals of the ALS program include: a 10 fold reduction in cost per pound to orbit, launch processing in under 3 weeks, and higher reliability and safety standards than current expendables. Knowledge-based system techniques are being explored for the purpose of automating decision support processes in onboard and ground systems for pre-launch checkout and in-flight operations. Issues such as: satisfying real-time requirements, providing safety validation, hardware and Data Base Management System (DBMS) interfacing, system synergistic effects, human interfaces, and ease of maintainability, have an effect on the viability of expert systems as a useful tool.
Szatkowski, Gerard P.↗
Treatment of uncertainty in artificial intelligence
The present assessment of the development status of research efforts concerned with AI reasoning under conditions of uncertainty emphasizes the importance of appropriateness in the approach selected for both the epistemic and the computational levels. At the former level, attention is given to the form of uncertainty-representation and the fidelity of its reflection of actual problems' uncertainties; at the latter level, such issues as the availability of the requisite information and the complexity of the reasoning process must be considered. The tradeoff between these levels must always be the focus of AI system-developers' attention.
Berenji, Hamid R.↗
Space life sciences perspectives for Space Station Freedom
It is now generally acknowledged that the life science discipline will be the primary beneficiary of Space Station Freedom. The unique facility will permit advances in understanding the consequences of long duration exposure to weightlessness and evaluation of the effectiveness of countermeasures. It will also provide an unprecedented opportunity for basic gravitational biology, on plants and animals as well as human subjects. The major advantages of SSF are the long duration exposure and the availability of sufficient crew to serve as subjects and operators. In order to fully benefit from the SSF, life sciences will need both sufficient crew time and communication abilities. Unlike many physical science experiments, the life science investigations are largely exploratory, and frequently bring unexpected results and opportunities for study of newly discovered phenomena. They are typically crew-time intensive, and require a high degree of specialized training to be able to react in real time to various unexpected problems or potentially exciting findings. Because of the long duration tours and the large number of experiments, it will be more difficult than with Spacelab to maintain astronaut proficiency on all experiments. This places more of a burden on adequate communication and data links to the ground, and suggests the use of AI expert system technology to assist in astronaut management of the experiment. Typical life science experiments, including those flown on Spacelab Life Sciences 1, will be described from the point of view of the demands on the astronaut. A new expert system, 'PI in a Box,' will be introduced for SLS-2, and its applicability to other SSF experiments discussed. (This paper consists on an abstract and ten viewgraphs.)
Young, Laurence R.↗