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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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NASA NTRS2022

Psyche Project Implementation During the COVID Pandemic

In January 2017, “Psyche: Journey to a Metal World” was selected for implementation as part of NASA’s Discovery program. The Psyche mission will utilize electric propulsion with SPT-140 Hall thrusters to rendezvous and orbit the metal-rich asteroid (16) Psyche, in the main asteroid belt between Mars and Jupiter. The Psyche spacecraft requires no chemical propulsion and, when launched in 2022, will be the first mission to use Hall thrusters beyond lunar orbit. The Psyche spacecraft is a hybrid development with Jet Propulsion Laboratory (JPL)-provided deep-space avionics and communications equipment mounted on a high-heritage MAXAR (formerly Space Systems Loral) Solar-Electric Propulsion (SEP) Chassis, based on their 1300 series of GEO communications satellites. The spacecraft is equipped to support a suite of science instruments, as well as a demonstration of the Deep Space Optical Communications (DSOC) technology. The spacecraft has sufficient onboard resources, autonomy, redundancy, and operability to complete a 3.5-year cruise to 16 Psyche, followed by a 20-month campaign of science investigations while orbiting the asteroid.The mission’s early concept design and progress through Preliminary Design Review (PDR) has been described in previous work. The paper will cover the recent mission progress from the Critical Design Review (CDR) through the start of Spacecraft Environmental Testing, which took place during the COVID pandemic.The authors will highlight the successful remote collaboration between the major partners: ASU, JPL, MAXAR, and the Payload teams that led to the initiation of the Assembly, Test, Launch, Operations Phase (ATLO) in early March 2021. Emphasis will be placed on the effects that the COVID-19 pandemic had on the work environment over the last 16+ months, including challenges to delivery of flight hardware and test equipment. In addition to the COVID-19 challenges, other significant anomalies discovered during design and test will be described along with any impacts to the overall science capability of the mission.

Lord, Peter↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRS2019

The TASAR Project: Launching Aviation on an Optimized Route Toward Aircraft Autonomy

The Traffic Aware Strategic Aircrew Request (TASAR) concept applies onboard automation for the purpose of advising the pilot of route modifications that would be beneficial to the flight. Leveraging onboard computing platforms with connectivity to avionics and diverse data sources on and off the aircraft, TASAR introduces a new, powerful capability for in-flight trajectory management to the cockpit and its flight crew that is anticipated to induce a significant culture change in airspace operations. Flight crews empowered by TASAR and its derivative technologies could transform from today’s flight plan followers to proactive trajectory managers, taking an initial critical step towards increasing autonomy in the airspace system. TASAR was developed as a catalyst for operational autonomy, a future vision where the responsibilities and authorities of trajectory management reside with the aircraft operator and are distributed among participating aircraft, thus fulfilling a vision dating back decades and enabling a fully scalable airspace system. This NASA Technical Paper maps TASAR to its foundational vision and traces its research and development from initial concept generation to an operational evaluation by a U.S. airline in revenue service, the final stage before technology transfer and commercialization.

Wing, David J.↗
NASA NTRSDate not supplied

Human-Autonomy Teaming Research in Support of m:N Operations

Since 2019, the NASA and industry partners have been involved in research focused on a novel paradigm for operations of remotely piloted aircraft. This paradigm involves multiple people sharing a fleet of multiple vehicles between them. Referred to as m:N (pronounced “em-to-en”), this configuration describes a ratio where m is the number of operators and N is the number of vehicles. Through force and asset multiplication, the m:N concept seeks to enable a scalable and resilient operation of remotely crewed vehicles. The primary means of obtaining such a robust operation is through allowing a flexible crew of variable size to dynamically attend to the needs of assets in while performing real-time operator workload management. It is in that sense that assets are shared between operators: as needed (such as in events of elevated workload) an operator in an m:N context can “handoff” the responsibility for some amount of assets, nh < N, to be absorbed by the m – 1 crew members on staff. At some time later, these nh assets could be returned to their original owner or they may be further distributed to other crew if called for by the mission. During this panel, I will elaborate on the research activity undertaken by the Human-Autonomy Teaming (HAT) Laboratory at NASA Ames Research Center over the previous three years. The studies conducted by the HAT Lab range from interviews with subject matter experts, a cognitive walkthrough, a task analysis, and two simulation experiments to-date. During experimentation, pilots made use of an advanced Ground Control Station developed by the HAT Lab and industry partners to simulate m:N operations in two large, metropolitan areas of Southern California: Los Angeles and San Diego. Further experimentation planned over the next few years. Results from our research to-date indicates that pilots of a moderately sized fleet of about a dozen remotely crewed aircraft adequately maintained safety performance and situation awareness of their aircraft, even when presented with unexpected situations of heightened workload.

multi-vehicle control↗
NASA NTRSDate not supplied

Human-Autonomy Teaming Assistant to Support Small Uncrewed Aircraft Systems for Wildland Firefighting Operations

An exploratory human-in-the-loop simulation was conducted to investigate and characterize a Human-Autonomy Teaming (HAT) Assistant to support a remote operator of multiple small Uncrewed Aircraft Systems (sUAS) using a ground control station (GCS) in the context of a wildland fire surveillance mission. Operator performance using the GCS with the HAT Assistant (Assisted Mode) was compared to operator performance using the GCS without the HAT Assistant (Unassisted Mode) during two types of contingency-event scenarios (Low and High Complexity). In the Assisted Mode, the HAT Assistant provided updates to the level of risk to the mission along with recommendations for risk mitigation, which were not provided in the Unassisted Mode. No significant differences in objective performance and subjective ratings of workload, situation awareness, and trust in automation between the Assisted and Unassisted Modes were detected, however there were indications that participants preferred the Assisted GCS over the Unassisted GCS and directions for further development were explored. Additional work is necessary to further refine the HAT Assistant and better characterize its effects on remote operator performance while managing multiple sUAS assets. Future work is recommended to optimize the implementation of an assistant to support operator performance during different missions and across vehicle classes

Human-Autonomy Teaming↗
NASA NTRSDate not supplied

Human-Autonomy Teaming Assistant to Support Small Uncrewed Aircraft Systems for Wildland Firefighting Operations

An exploratory human-in-the-loop simulation was conducted to investigate and characterize a Human-Autonomy Teaming (HAT) Assistant to support a remote operator of multiple small Uncrewed Aircraft Systems (sUAS) using a ground control station (GCS) in the context of a wildland fire surveillance mission. Operator performance using the GCS with the HAT Assistant (Assisted Mode) was compared to operator performance using the GCS without the HAT Assistant (Unassisted Mode) during two types of contingency-event scenarios (Low and High Complexity). In the Assisted Mode, the HAT Assistant provided updates to the level of risk to the mission along with recommendations for risk mitigation, which were not provided in the Unassisted Mode. No significant differences in objective performance and subjective ratings of workload, situation awareness, and trust in automation between the Assisted and Unassisted Modes were detected, however there were indications that participants preferred the Assisted GCS over the Unassisted GCS and directions for further development were explored. Additional work is necessary to further refine the HAT Assistant and better characterize its effects on remote operator performance while managing multiple sUAS assets. Future work is recommended to optimize the implementation of an assistant to support operator performance during different missions and across vehicle classes.

Human-Autonomy Teaming↗
NASA NTRS2008

Lunar and Mars Exploration: The Autonomy Factor

Long duration space flight crews have relied heavily on almost constant communication with ground control mission support. Ground control teams provide vehicle status and system monitoring, while offering near real time support for specific tasks, emergencies, and ensuring crew health and well being. With extended exploration goals to lunar and Mars outposts, real time communication with ground control teams and the ground s ability to conduct mission monitoring will be very limited compared to the resources provided to current International Space Station (ISS) crews. An operational shift toward more autonomy and a heavier reliance on the crew to monitor their vehicle and operations will be required for these future missions. NASA s future exploration endeavors and the subsequent increased autonomy will require a shift in crew skill composition, i.e. engineer, doctor, mission specialist etc. and lead to new training challenges and mission scenarios. Specifically, operational and design changes will be necessary in many areas including: Habitat Infrastructure and Support Systems, Crew Composition, Training, Procedures and Mission Planning. This paper will specifically address how to apply ISS lessons learned to further use ISS as a test bed to address decreased amounts of ground support to achieve full autonomous operations for lunar and Mars missions. Understanding these lessons learned and applying them to current operations will help to address the future impacts of increased crew autonomy for the lunar and Mars outposts and pave the way for success in increasingly longer mission durations.

Rando, Cynthia M.↗
NASA NTRS2008

Autonomy and Sensor Webs: The Evolution of Mission Operations

Demonstration of these sensor web capabilities will enable fast responding science campaigns that combine spaceborne, airborne, and ground assets. Sensor webs will also require new operations paradigms. These sensor webs will be operated directly by scientists using science goals to control their instruments. We will explore these new operations architectures through a study of existing sensor web prototypes.

Sherwood, Rob↗
NASA NTRS2019

Prognostics As-A-Service: A Scalable Cloud Architecture for Prognostics

Comprehensive aircraft system health-state awareness is critical for maintaining safe, efficient growth in global operations, enabling higher levels of autonomy, and facilitating new forms of aviation. Maintainers, vehicle operators, air traffic controllers, dispatchers, pilots, autonomous systems, and other decision-makers must have reliable real-time knowledge of the vehicle health, the health of its critical composite systems, predictions of how health changes with time, and forecasts of how its capabilities change with health degradation to preserve safety and efficiency. Providing this information in a reliable manner in computationally constrained environments and across a wide range of vehicles and systems continues to be a challenge. This challenge can be partially resolved through cloud computing, where the execution of prognostic and diagnostic algorithms is performed on a network of remote servers hosted on the internet. NASA is developing a cloud computing service, Prognostics As-A-Service (PaaS), that explores the feasibility and challenges of cloud-enhanced prognostics. Though such a system has broad applicability, this research effort is focused on aviation applications.

Prognostics↗
NASA NTRS2013

Beyond NextGen: AutoMax Overview and Update

Main Message: National and Global Needs - Develop scalable airspace operations management system to accommodate increased mobility needs, emerging airspace uses, mix, future demand. Be affordable and economically viable. Sense of Urgency. Saturation (delays), emerging airspace uses, proactive development. Autonomy is Needed for Airspace Operations to Meet Future Needs. Costs, time critical decisions, mobility, scalability, limits of cognitive workload. AutoMax to Accommodate National and Global Needs. Auto: Automation, autonomy, autonomicity for airspace operations. Max: Maximizing performance of the National Airspace System. Interesting Challenges and Path Forward.

ATM↗
NASA NTRS2024

Autonomy to Enable NASA Missions from Aeronautics to Space

2024 ASCEND Call for Sessions Session Format: Panel Session Topic: Space Exploration and Infrastructure: Exploring, Living, and Working in Space (The panel must map to one of six Session Topics - https://www.ascend.events/presenters/call-for-sessions/#sessiontopics) Title: Autonomy to Enable NASA Missions from Aeronautics to Space Short Session Description: In this panel discussion the National Aeronautics and Space Administration (NASA) will discuss the role that autonomy and Artificial Intelligence (AI) will play as humanity moves off-world. Recent advances in general autonomy tools are changing the way NASA and its partners leverage autonomy for its air and space initiatives, including Advanced Air Mobility (AAM) concepts and potential lunar and Martian operations. The panelists will consist of autonomy subject matter experts familiar with the current state of the art for autonomy across both aeronautical and space domains. They will discuss how those technologies could evolve as operations become more complex and which autonomy technologies can be used in both the space and aeronautical domains. For example, perhaps autonomy work originally developed for terrestrial applications, like Advanced Air Mobility (AAM), could be applied to off-world lunar and Martian applications and vice versa. Additionally, the panel will address common misconceptions of these technologies, obstacles to implementation, and possible solutions for overcoming those obstacles. Join NASA in exploring how research activities can align to streamline autonomy development efforts, advancing NASA's goals to expand humanity's reach beyond Earth for the benefit of all. Contact Information: • Dr. Adam Yingling • Office of Technology, Policy, and Strategy (OTPS) • Adam.j.yingling@nasa.gov, 703-416-9129 Session Length: 1.25 hours, 75 minutes Extended Description: Moderator: Dr. Adam Yingling, NASA, Office of Technology, Policy, and Strategy Panel Speakers: • Autonomy Forum Principals o Dr. Charles Norton, Deputy Chief Technologist, Jet Propulsion Laboratory (JPL) o Dr. Carolyn Mercer, Chief Technologist, Space Mission Directorate (SMD) o Dr. Parimal Kopardekar (PK), Advanced Air Mobility (AAM) Integration Manager o Danette Alan, NASA, Senior Leader for Autonomous Systems, Space Technology Mission Directorate (STMD) o Duane Armstrong, Intelligent Systems Lead, Autonomous Systems Laboratory (ASL) Panel Format: • Introduction (10 min): The panel moderator will provide a 10-minute session introduction that will include an overview NASA’s Moon to Mars architecture, AAM autonomy research, and an introduction of the principals as panel speakers. • Moderated Session Part 1 (30 min): There will be a 30-minute moderated session among the moderator and the five autonomy principals to discuss the current state of art for autonomy and how work developed in one domain may be applicable to other domains; including the merits and challenges for implementing those technologies. • Moderated Session Part 2 (25 min): The moderator will then ask the principals to consider how technologies developed across all the domains might be able to address the most salient challenges identified in the previous session. • Q&A (10 min): The session will conclude with 10-minutes of audience Q&A. Session Goals and Outcomes: The session goals are 1) to communicate the importance of autonomy for both aeronautical and space mission, 2) Investigate potential synergies across autonomy research efforts that will enable scalable operations, and 3) to receive community feedback as NASA leverages autonomy to evolve aviation on Earth and enable humanity to live and work off-world.

Aerospace↗
NASA NTRS2010

Constellation Architecture Team-Lunar Scenario 12.0 Habitation Overview

This paper will describe an overview of the Constellation Architecture Team Lunar Scenario 12.0 (LS-12) surface habitation approach and concept performed during the study definition. The Lunar Scenario 12 architecture study focused on two primary habitation approaches: a horizontally-oriented habitation module (LS-12.0) and a vertically-oriented habitation module (LS-12.1). This paper will provide an overview of the 12.0 lunar surface campaign, the associated outpost architecture, habitation functionality, concept description, system integration strategy, mass and power resource estimates. The Scenario 12 architecture resulted from combining three previous scenario attributes from Scenario 4 "Optimized Exploration", Scenario 5 "Fission Surface Power System" and Scenario 8 "Initial Extensive Mobility" into Scenario 12 along with an added emphasis on defining the excursion ConOps while the crew is away from the outpost location. This paper will describe an overview of the CxAT-Lunar Scenario 12.0 habitation concepts and their functionality. The Crew Operations area includes basic crew accommodations such as sleeping, eating, hygiene and stowage. The EVA Operations area includes additional EVA capability beyond the suitlock function such as suit maintenance, spares stowage, and suit stowage. The Logistics Operations area includes the enhanced accommodations for 180 days such as enhanced life support systems hardware, consumable stowage, spares stowage, interconnection to the other habitation elements, a common interface mechanism for future growth, and mating to a pressurized rover or Pressurized Logistics Module (PLM). The Mission & Science Operations area includes enhanced outpost autonomy such as an IVA glove box, life support, medical operations, and exercise equipment.

Kennedy, Kriss J.↗
NASA NTRS1998

Mission Operations with an Autonomous Agent

The Remote Agent (RA) is an Artificial Intelligence (AI) system which automates some of the tasks normally reserved for human mission operators and performs these tasks autonomously on-board the spacecraft. These tasks include activity generation, sequencing, spacecraft analysis, and failure recovery. The RA will be demonstrated as a flight experiment on Deep Space One (DSI), the first deep space mission of the NASA's New Millennium Program (NMP). As we moved from prototyping into actual flight code development and teamed with ground operators, we made several major extensions to the RA architecture to address the broader operational context in which PA would be used. These extensions support ground operators and the RA sharing a long-range mission profile with facilities for asynchronous ground updates; support ground operators monitoring and commanding the spacecraft at multiple levels of detail simultaneously; and enable ground operators to provide additional knowledge to the RA, such as parameter updates, model updates, and diagnostic information, without interfering with the activities of the RA or leaving the system in an inconsistent state. The resulting architecture supports incremental autonomy, in which a basic agent can be delivered early and then used in an increasingly autonomous manner over the lifetime of the mission. It also supports variable autonomy, as it enables ground operators to benefit from autonomy when L'@ey want it, but does not inhibit them from obtaining a detailed understanding and exercising tighter control when necessary. These issues are critical to the successful development and operation of autonomous spacecraft.

Pell, Barney↗
NASA NTRS2005

Inspection with Robotic Microscopic Imaging

Future Mars rover missions will require more advanced onboard autonomy for increased scientific productivity and reduced mission operations cost. One such form of autonomy can be achieved by targeting precise science measurements to be made in a single command uplink cycle. In this paper we present an overview of our solution to the subproblems of navigating a rover into place for microscopic imaging, mapping an instrument target point selected by an operator using far away science camera images to close up hazard camera images, verifying the safety of placing a contact instrument on a sample or finding nearby safe points, and analyzing the data that comes back from the rover. The system developed includes portions used in the Multiple Target Single Cycle Instrument Placement demonstration at NASA Ames in October 2004, and portions of the MI Toolkit delivered to the Athena Microscopic Imager Instrument Team for the MER mission still operating on Mars today. Some of the component technologies are also under consideration for MSL mission infusion.

Pedersen, Liam↗
NASA NTRS2000

Experience Using Formal Methods for Specifying a Multi-Agent System

The process and results of using formal methods to specify the Lights Out Ground Operations System (LOGOS) is presented in this paper. LOGOS is a prototype multi-agent system developed to show the feasibility of providing autonomy to satellite ground operations functions at NASA Goddard Space Flight Center (GSFC). After the initial implementation of LOGOS the development team decided to use formal methods to check for race conditions, deadlocks and omissions. The specification exercise revealed several omissions as well as race conditions. After completing the specification, the team concluded that certain tools would have made the specification process easier. This paper gives a sample specification of two of the agents in the LOGOS system and examples of omissions and race conditions found. It concludes with describing an architecture of tools that would better support the future specification of agents and other concurrent systems.

Rouff, Christopher↗