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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Community Benchmark Problem for Intelligent Contingency Management

This paper introduces a Community Benchmark Problem (CBP) for Intelligent Contingency Management (ICM) for Urban Air Mobility (UAM) aircraft. The CBP aims to provide a common framework for measuring and comparing the progress of autonomy solutions for UAM aircraft in handling emergency situations. The paper proposes a methodology for defining and quantifying five measures of complexity that capture the challenges and requirements of ICM for UAM: Mission, Environmental, Autonomy, Decision-Making, and Mission Fault. In addition, it proposes a methodology for defining and quantifying mission risk acceptability with the same goals: Contingency Management, Mission Success, Operational, Mission Redefinition, and Environmental. We describe how to use these measures to track progress of the development of ICM capability, as well as to create scenarios and evaluate the performance of different autonomy solutions.

autonomy↗
NASA NTRSDate not supplied

Risk-Reduction Autonomy Implementation to Enable NASA Artemis Missions

To achieve NASA’s Artemis program mission objectives a high level of autonomy and ubiquitous autonomy throughout the systems that are being developed will be necessary. The autonomous systems of Artemis will require a distributed autonomy capability, with autonomous systems organized functionally in a hierarchical architecture, where systems at higher levels of the hierarchy have authority over systems at lower levels. The challenge of developing autonomy technologies and concepts of operations for Artemis has been undertaken by the NASA Gateway Working Group. This group has developed requirements, architectures, concepts of operations, and interface control documents, in the context of a hierarchical distributed architecture that includes the following: a Vehicle System Manager (VSM) that autonomously manages the entire Gateway; Module System Managers (MSMs) that autonomously manage each module; and System Managers (SMs) that autonomously manage systems within a module(i.e. ECLSS).A substantially high level of autonomy needs to be achieved by each element of the hierarchy (VSM, MSM, SM)to meet requirements for uncrewed operations; this includes conditions that will have minimal and/or delayed ground intervention (i.e. requirements for sustainability for months of operation without crew or ground support). To advance an implementation of this autonomy design (Gateway Autonomy Design –GAD), a collaboration was established between the Autonomous Systems Laboratory (ASL) at NASA Stennis Space Center and Lockheed Martin. The objectives of this partnership were the following: (1)to implement autonomy at the VSM, MSM, and SM levels;(2) to implement communications among a VSM, 2 MSMs, ORION (a visiting vehicle somewhat equivalent to a module) and 1 SM (a power system), and (3) test autonomous operations with representative use cases. A SM backed by a high-fidelity simulation was created to facilitate demonstrations of use cases that originated in a system of a module. Communication between the VSM and MSMs was implemented according to Concepts of Operations and Interface Control Documents (ICDs). Demonstrations were conducted to address nominal and off-nominal operations and multi-module interactions with VSM. Additionally, user interfaces were created to provide awareness about ongoing processes and results while enhancing the demonstration. Demonstrations included the following use cases: (1)Orion as visiting vehicle registers with VSM;(2) VSM reschedules a module’s timelines when another module’s MSM task fails; and (3) a module’s Power System Manager (PSM) standalone demonstration that included component failure diagnostics, tracing component failure to effected components, which in turn, reports failure information up to the VSM for acknowledgement and display. This paper will describe the detailed technology and autonomous systems developed, and the integrated multi-module demonstrations conducted. Also, challenges that must be met to fully implement the GAD defined by Gateway will be addressed.

Autonomous Systems↗
NASA NTRSDate not supplied

Risk-Reduction Autonomy Implementation to Enable NASA Artemis Missions

To achieve NASA’s Artemis program mission objectives a high level of autonomy that is ubiquitous throughout the systems that are being developed will be necessary. The autonomous systems of Artemis will require a distributed autonomy capability, with autonomous systems organized functionally in a hierarchical architecture, where systems at higher levels of the hierarchy have authority over systems at lower levels. The challenge of developing autonomy technologies and Concepts of Operations (ConOps) for Artemis has been undertaken by the NASA Gateway Working Group. This group has developed requirements, architectures, ConOps, and interface control documents (ICDs), in the context of a hierarchical distributed architecture that includes the following: a Vehicle System Manager (VSM) that autonomously manages the entire Gateway; Module System Managers (MSMs) that autonomously manage each module; and System Managers (SMs) that autonomously manage systems within a module (i.e. ECLSS). A substantially high level of autonomy needs to be achieved by each element of the hierarchy (VSM, MSM, SM) to meet requirements for uncrewed operations; this includes conditions that will have minimal and/or delayed ground intervention (i.e. requirements for sustainability for months of operation without crew or ground support). To advance an implementation of this autonomy design (Gateway Autonomy Design – GAD), a collaboration was established between the Autonomous Systems Laboratory (ASL) at NASA Stennis Space Center and Lockheed Martin. The objectives of this partnership were the following: (1) to implement autonomy at the VSM, MSM, and SM levels; (2) to implement communications among a VSM, 2 MSMs, ORION (a visiting vehicle somewhat equivalent to a module) and 1 SM (a power system), and (3) test autonomous operations with representative use cases. A SM backed by a high-fidelity simulation was created to facilitate demonstrations of use cases that originated in a system of a module. Communication between the VSM and MSMs was implemented according to Concepts of Operations and Interface Control Documents (ICDs). Demonstrations were conducted to address nominal and off-nominal operations and multi-module interactions with the VSM. Additionally, user interfaces were created to provide awareness about ongoing processes and results while enhancing the demonstration. Demonstrations included the following use cases: (1) Orion as visiting vehicle registers with VSM; (2) VSM reschedules a module’s timelines when another module’s MSM task fails; and (3) a module’s Power System Manager (PSM) demonstration that included component failure diagnostics, tracing component failure to effected components, which in turn, reports failure information up to the VSM for acknowledgement and display. This paper will describe the detailed technology and autonomous systems developed, and the integrated multi-module demonstrations conducted. Also, challenges that must be met to fully implement the GAD defined by Gateway will be addressed.

Fernando Figueroa↗
NASA NTRS2004

Onboard Autonomy on the Earth Observing One Mission

The Earth Observing One Spacecraft is currently flying The Autonomous Sciencecraft Experiment (ASE) - onboard autonomy software to improve science return. The ASE software enables the spacecraft to autonomously detect and respond to science events occurring on the Earth. ASE includes software systems that perform science data analysis, mission planning, and run-time robust execution. In this article we describe the autonomy flight software and how it enables a new paradigm of autonomous science and mission operations.

autonomy↗
NASA NTRS2014

Software Engineering for Human Spaceflight

The Spacecraft Software Engineering Branch of NASA Johnson Space Center (JSC) provides world‐class products, leadership, and technical expertise in software engineering, processes, technology, and systems management for human spaceflight. The branch contributes to major NASA programs (e.g. ISS, MPCV/Orion) with in‐house software development and prime contractor oversight, and maintains the JSC Engineering Directorate CMMI rating for flight software development. Software engineering teams work with hardware developers, mission planners, and system operators to integrate flight vehicles, habitats, robotics, and other spacecraft elements. They seek to infuse automation and autonomy into missions, and apply new technologies to flight processor and computational architectures. This presentation will provide an overview of key software‐related projects, software methodologies and tools, and technology pursuits of interest to the JSC Spacecraft Software Engineering Branch.

Fredrickson, Steven E.↗
NASA NTRS2003

Developing An Autonomy Infusion Infrastructure for Robotic Exploration

Future robotic exploration missions will require autonomy in order to accomplish mission goals for operational efficiency and science return. For example, it will require three communication cycles for the Mars Exploration Rovers, Spirit and Opportunity, to place an instrument on a science target. Reducing this time necessitates highly accurate navigation, obstacle avoidance, target tracking, target analysis, manipulation, and fault diagnosis. Technologies to address these and other operational elements are currently being developed at NASA and within academia. However, infusion into missions has always been a difficult task for researchers. In order to keep risk down, mission managers are reluctant to include new technologies unless they have undergone extensive testing and verification under flight-realistic conditions. Furthermore, infusion of new technologies into missions is made more difficult by the variety of software frameworks under which these technologies are developed. Missions would like to see competing solutions demonstrated on a common platform so that they can compare performance and choose the solution best suited to their application.

Bualat, Maria G.↗
NASA NTRS2016

The Earth Observing System (EOS) Ground System: Leveraging an Existing Operational Ground System Infrastructure to Support New Missions

The Earth Observer System (EOS) was officially established in 1990 and went operational in December 1999 with the launch of its flagship spacecraft Terra. Aqua followed in 2002 and Aura in 2004. All three spacecraft are still operational and producing valuable scientific data. While all are beyond their original design lifetime, they are expected to remain viable well into the 2020s. The EOS Ground System is a multi-mission system based at NASA Goddard Space Flight Center that supports science and spacecraft operations for these three missions. Over its operational lifetime to date, the EOS Ground System has evolved as needed to accommodate mission requirements. With an eye towards the future, several updates are currently being deployed. Subsystem interconnects are being upgraded to reduce data latency and improve system performance. End-of-life hardware and operating systems are being replaced to mitigate security concerns and eliminate vendor support gaps. Subsystem hardware is being consolidated through the migration to Virtual Machine based platforms. While mission operations autonomy was not a design goal of the original system concept, there is an active effort to apply state-of-the-art products from the Goddard Mission Services Evolution Center (GMSEC) to facilitate automation where possible within the existing heritage architecture. This presentation will provide background information on the EOS ground system architecture and evolution, discuss latest improvements, and conclude with the results of a recent effort that investigated how the current system could accommodate a proposed new earth science mission.

Earth Science Mission Operations (ESMO)↗
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 and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. 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 space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human 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 interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology 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 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 NTRS2002

Systems Architecture for Fully Autonomous Space Missions

The NASA Goddard Space Flight Center is working to develop a revolutionary new system architecture concept in support of fully autonomous missions. As part of GSFC's contribution to the New Millenium Program (NMP) Space Technology 7 Autonomy and on-Board Processing (ST7-A) Concept Definition Study, the system incorporates the latest commercial Internet and software development ideas and extends them into NASA ground and space segment architectures. The unique challenges facing the exploration of remote and inaccessible locales and the need to incorporate corresponding autonomy technologies within reasonable cost necessitate the re-thinking of traditional mission architectures. A measure of the resiliency of this architecture in its application to a broad range of future autonomy missions will depend on its effectiveness in leveraging from commercial tools developed for the personal computer and Internet markets. Specialized test stations and supporting software come to past as spacecraft take advantage of the extensive tools and research investments of billion-dollar commercial ventures. The projected improvements of the Internet and supporting infrastructure go hand-in-hand with market pressures that provide continuity in research. By taking advantage of consumer-oriented methods and processes, space-flight missions will continue to leverage on investments tailored to provide better services at reduced cost. The application of ground and space segment architectures each based on Local Area Networks (LAN), the use of personal computer-based operating systems, and the execution of activities and operations through a Wide Area Network (Internet) enable a revolution in spacecraft mission formulation, implementation, and flight operations. Hardware and software design, development, integration, test, and flight operations are all tied-in closely to a common thread that enables the smooth transitioning between program phases. The application of commercial software development techniques lays the foundation for delivery of product-oriented flight software modules and models. Software can then be readily applied to support the on-board autonomy required for mission self-management. An on-board intelligent system, based on advanced scripting languages, facilitates the mission autonomy required to offload ground system resources, and enables the spacecraft to manage itself safely through an efficient and effective process of reactive planning, science data acquisition, synthesis, and transmission to the ground. Autonomous ground systems in turn coordinate and support schedule contact times with the spacecraft. Specific autonomy software modules on-board include mission and science planners, instrument and subsystem control, and fault tolerance response software, all residing within a distributed computing environment supported through the flight LAN. Autonomy also requires the minimization of human intervention between users on the ground and the spacecraft, and hence calls for the elimination of the traditional operations control center as a funnel for data manipulation. Basic goal-oriented commands are sent directly from the user to the spacecraft through a distributed internet-based payload operations "center". The ensuing architecture calls for the use of spacecraft as point extensions on the Internet. This paper will detail the system architecture implementation chosen to enable cost-effective autonomous missions with applicability to a broad range of conditions. It will define the structure needed for implementation of such missions, including software and hardware infrastructures. The overall architecture is then laid out as a common thread in the mission life cycle from formulation through implementation and flight operations.

Esper, Jamie↗
NASA NTRS2011

Process Algebra Approach for Action Recognition in the Maritime Domain

The maritime environment poses a number of challenges for autonomous operation of surface boats. Among these challenges are the highly dynamic nature of the environment, the onboard sensing and reasoning requirements for obeying the navigational rules of the road, and the need for robust day/night hazard detection and avoidance. Development of full mission level autonomy entails addressing these challenges, coupled with inference of the tactical and strategic intent of possibly adversarial vehicles in the surrounding environment. This paper introduces PACIFIC (Process Algebra Capture of Intent From Information Content), an onboard system based on formal process algebras that is capable of extracting actions/activities from sensory inputs and reasoning within a mission context to ensure proper responses. PACIFIC is part of the Behavior Engine in CARACaS (Cognitive Architecture for Robotic Agent Command and Sensing), a system that is currently running on a number of U.S. Navy unmanned surface and underwater vehicles. Results from a series of experimental studies that demonstrate the effectiveness of the system are also presented.

process algebras↗
NASA NTRS2000

The Impact of Autonomous Systems Technology on JPL Mission Software

This paper discusses the following topics: (1) Autonomy for Future Missions- Mars Outposts, Titan Aerobot, and Europa Cryobot / Hydrobot; (2) Emergence of Autonomy- Remote Agent Architecture, Closing Loops Onboard, and New Millennium Flight Experiment; and (3) Software Engineering Challenges- Influence of Remote Agent, Scalable Autonomy, Autonomy Software Validation, Analytic Verification Technology, and Autonomy and Software Software Engineering.

Doyle, Richard J.↗
NASA NTRS2010

A Modular Instrumentation System for NASA's Habitat Demonstration Unit

NASA's human spaceflight program is focused on developing technologies to expand the reaches of human exploration and science activities beyond low earth orbit. A critical aspect of living in space or on planetary surfaces is habitation, which provides a safe and comfortable space in which humans can live and work. NASA is seeking out the best option for habitation by exploring several different concepts through the Habitat Demonstration Unit (HDU) project. The purpose of this HDU is to develop a fully autonomous habitation system that enables human exploration of space. One critical feature of the HDU project that helps to accomplish its mission of autonomy is the instrumentation system that monitors key subsystems operating within a Habitat configuration. The following paper will discuss previous instrumentation systems used in analog habitat concepts and how the current instrumentation system being implemented on the HDU1-PEM, or pressurized excursion module, is building upon the lessons learned of those previous systems. Additionally, this paper will discuss the benefits and the limitations of implementing a wireless sensor network (WSN) as the basis for data transport in the instrumentation system. Finally, this paper will address the experiences and lessons learned with integration, testing prior to deployment, and field testing at the JSC rock yard. NASA is developing the HDU1-PEM as a step towards a fully autonomous habitation system that enables human exploration of space. To accomplish this purpose, the HDU project is focusing on development, integration, testing, and evaluation of habitation systems. The HDU will be used as a technology pull, testbed, and integration environment in which to advance NASA's understanding of alternative mission architectures, requirements, and operations concepts definition and validation. This project is a multi-year effort. In 2010, the HDU1-PEM will be in a pressurized excursion module configuration, and in 2011 the module will be reconfigured for a pressurized core module configuration. Each year the HDU configurations will undergo testing at NASA's Desert Research and Technology Studies (D-RaTS) in Arizona [1]. As part of this project, a modular instrumentation system is developed to meet the monitoring needs of the HDU subsystems and to integrate with the current command and data handling infrastructure that has been developed for the project. The main objective of this study is to provide for the monitoring needs of the HDU. The requirements necessary to meet this objective are developed by working with the subsystem managers of the HDU to understand their monitoring needs. Additionally, the instrumentation system design leverages knowledge and lessons learned from previous studies, such as the inflatable habitat health monitoring system that was deployed in Antarctica [2], the integrated health monitoring system developed for NASA's Microhab [3], and the JSC Lunar Habitat Wireless Testbed to demonstrate a "standardsbased" approach to a wireless instrumentation system [4]. The HDU also requires flexibility in reconfiguration options, and it is necessary to demonstrate and evaluate a modular approach to an instrumentation system. Thus, the instrumentation system is designed in two parts: the primary system employs a standard WSN configuration, and the secondary system employs a wired USB hub. The WSN design provides for reconfiguration or replacement of sensors due to malfunctions or upgrades by using a wireless node that accepts ten instrument inputs and wirelessly transmits the data to the command and data handling system. The USB hub is necessary for those instruments that operate using a wired USB connection, although the design attempts to limit the amount of sensors that need to be wired connections.

Rojdev, Kristina↗
NASA NTRSDate not supplied

Reinforcement Learning for Spacecraft Navigation & Environment Characterization in the Planar-Restricted Two-Body Problem

As science, exploration, and commercial space missions become increasingly complex, so does the need for efficient, autonomous, and integrated spacecraft navigation and operations techniques. Key operational functions, including data collection and transmission, environment characterization, systems constraints, human factors, and navigation, often are intertwined and conflicted. Deep Reinforcement Learning (DRL) offers a framework for addressing integrated spacecraft navigation and planning in an uncertain dynamical environment. The goal of this study is to evaluate the utility of DRL for integrated spacecraft navigation and planning. This is achieved by developing a simple environmental characterization training environment in the Planar-Restricted 2-Body Problem (PR2BP), establishing benchmarks and heuristic baselines, and designing a previously unstudied Markov Decision Process (MDP) formulation. This MDP formulation enables the spacecraft DRL agents to appropriately balance navigation and actuation capabilities. The resulting DRL-derived policy exceeds a random or untrained policy and meets or exceeds the level of performance of a heuristic without actuation. In the process, valuable intuition is gained about the problem with insight into how DRL methods could scale to increasingly more realistic scenarios, including net-work design and training architectures, efficient state space representations, and methods for encouraging exploration in a parametric action space, among others.

navigation↗
NASA NTRS1994

Design of a walking robot

Carnegie Mellon University's Autonomous Planetary Exploration Program (APEX) is currently building the Daedalus robot; a system capable of performing extended autonomous planetary exploration missions. Extended autonomy is an important capability because the continued exploration of the Moon, Mars and other solid bodies within the solar system will probably be carried out by autonomous robotic systems. There are a number of reasons for this - the most important of which are the high cost of placing a man in space, the high risk associated with human exploration and communication delays that make teleoperation infeasible. The Daedalus robot represents an evolutionary approach to robot mechanism design and software system architecture. Daedalus incorporates key features from a number of predecessor systems. Using previously proven technologies, the Apex project endeavors to encompass all of the capabilities necessary for robust planetary exploration. The Ambler, a six-legged walking machine was developed by CMU for demonstration of technologies required for planetary exploration. In its five years of life, the Ambler project brought major breakthroughs in various areas of robotic technology. Significant progress was made in: mechanism and control, by introducing a novel gait pattern (circulating gait) and use of orthogonal legs; perception, by developing sophisticated algorithms for map building; and planning, by developing and implementing the Task Control Architecture to coordinate tasks and control complex system functions. The APEX project is the successor of the Ambler project.

Whittaker, William↗