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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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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

The Challenges of Human-Autonomy Teaming

Machine intelligence is improving rapidly based on advances in big data analytics, deep learning algorithms, networked operations, and continuing exponential growth in computing power (Moores Law). This growth in the power and applicability of increasingly intelligent systems will change the roles humans, shifting them to tasks where adaptive problem solving, reasoning and decision-making is required. This talk will address the challenges involved in engineering autonomous systems that function effectively with humans in aeronautics domains.

artificial intelligence↗
NASA NTRSDate not supplied

Role of PHM in Autonomous Decision-Making: Aerospace applications

There is an increased need for onboard decision-making capabilities in cyber-physical systems be it in energy, automotive, aviation, space, or other industries as they aim for increased efficiency, resiliency, and mission assurance capabilities. Emerging next-gen technologies such as multi-rover planetary missions, distributed satellites, unmanned ground and aerial vehicle operations and smart grid systems rely on in-time risk assessment and autonomous decision-making. One critical piece of the autonomy puzzle is reliable prediction of system behavior under time-varying and potentially uncertain environmental conditions. Further, if agent states change during operation such as initiation of faults or degradation, reliable diagnostic tools need to be investigated. In this tutorial, we will revise approaches that integrates existing physics-based and data-driven models of agents interacting with probability models of the environment and component operation state. Role of existing PHM methodologies as they feed into decision-making under uncertainty will be studied. Balancing critical trade-offs between high-fidelity prognostic models, prediction time-horizons and the computational requirements for in-time cost-effective decision-making will be discussed through the implementation of surrogate models. Finally, the audience will be introduced to a real-time application of in-time trajectory planning of an unmanned aerial system (UAS) based on its PHM assessments under uncertain and varying wind conditions.

decision-making↗
NASA NTRS1994

Distributed intelligence for ground/space systems

DI is short for Distributed Intelligence for Ground/Space Systems and the DI Study is one in a series of ESA projects concerned with the development of new concepts and architectures for future autonomous spacecraft systems. The kick-off of DI was in January 1994 and the planned duration is three years. The background of DI is the desire to design future ground/space systems with a higher degree of autonomy than seen in today's missions. The aim of introducing autonomy in spacecraft systems is to: (1) lift the role of the spacecraft operators from routine work and basic troubleshooting to supervision; (2) ease access to and increase availability of spacecraft resources; (3) carry out basic mission planning for users; (4) enable missions which have not yet been feasible due to eg. propagation delays, insufficient ground station coverage etc.; and (5) possibly reduce mission cost. The study serves to identify the feasibility of using state-of-the-art technologies in the area of planning, scheduling, fault detection using model-based diagnosis and knowledge processing to obtain a higher level of autonomy in ground/space systems.

Aarup, Mads↗
NASA NTRS1993

Development and verification testing of automation and robotics for assembly of space structures

A program was initiated within the past several years to develop operational procedures for automated assembly of truss structures suitable for large-aperture antennas. The assembly operations require the use of a robotic manipulator and are based on the principle of supervised autonomy to minimize crew resources. A hardware testbed was established to support development and evaluation testing. A brute-force automation approach was used to develop the baseline assembly hardware and software techniques. As the system matured and an operation was proven, upgrades were incorprated and assessed against the baseline test results. This paper summarizes the developmental phases of the program, the results of several assembly tests, the current status, and a series of proposed developments for additional hardware and software control capability. No problems that would preclude automated in-space assembly of truss structures have been encountered. The current system was developed at a breadboard level and continued development at an enhanced level is warranted.

Rhodes, Marvin D.↗
NASA NTRS1998

Spacecraft Autonomy and the Missions of Exploration

Practitioners of artificial intelligence are being engaged to help develop the next generation of flight software for NASA missions, in partnership with other computer scientists, mission designers, operations personnel, spacecraft engineers, systems engineers, software engineers, and scientists.

artificial intelligence spacecraft autonomy ground↗
NASA NTRS2014

Human Mars Surface Science Operations

Human missions to the surface of Mars will have challenging science operations. This paper will explore some of those challenges, based on science operations considerations as part of more general operational concepts being developed by NASA's Human Spaceflight Architecture (HAT) Mars Destination Operations Team (DOT). The HAT Mars DOT has been developing comprehensive surface operations concepts with an initial emphasis on a multi-phased mission that includes a 500-day surface stay. This paper will address crew science activities, operational details and potential architectural and system implications in the areas of (a) traverse planning and execution, (b) sample acquisition and sample handling, (c) in-situ science analysis, and (d) planetary protection. Three cross-cutting themes will also be explored in this paper: (a) contamination control, (b) low-latency telerobotic science, and (c) crew autonomy. The present traverses under consideration are based on the report, Planning for the Scientific Exploration of Mars by Humans1, by the Mars Exploration Planning and Analysis Group (MEPAG) Human Exploration of Mars-Science Analysis Group (HEM-SAG). The traverses are ambitious and the role of science in those traverses is a key component that will be discussed in this paper. The process of obtaining, handling, and analyzing samples will be an important part of ensuring acceptable science return. Meeting planetary protection protocols will be a key challenge and this paper will explore operational strategies and system designs to meet the challenges of planetary protection, particularly with respect to the exploration of "special regions." A significant challenge for Mars surface science operations with crew is preserving science sample integrity in what will likely be an uncertain environment. Crewed mission surface assets -- such as habitats, spacesuits, and pressurized rovers -- could be a significant source of contamination due to venting, out-gassing and cleanliness levels associated with crew presence. Low-latency telerobotic science operations has the potential to address a number of contamination control and planetary protection issues and will be explored in this paper. Crew autonomy is another key cross-cutting challenge regarding Mars surface science operations, because the communications delay between earth and Mars could as high as 20 minutes one way, likely requiring the crew to perform many science tasks without direct timely intervention from ground support on earth. Striking the operational balance between crew autonomy and earth support will be a key challenge that this paper will address.

Bobskill, Marianne R.↗
NASA NTRS2019

Autonomy @ NASA for Aeronautics

NASA has been exploring the use of artificial intelligence technologies to improve vehicle and airspace capabilities for over 20 years. This research has ranged from the use of neural nets to allow adaptive control, to autonomous rotorcraft operations, to real-time prognostics, to an eventual goal of autonomous vehicles operating in an autonomous airspace, in the context of smart communities. Examples of past research and future directions in autonomy for aeronautics will be presented.

Van Dalsem, William R.↗
NASA NTRSDate not supplied

Airspace Integration Considerations for Increasingly Autonomous Flight and Operations

There is much interest in autonomous systems and their operations. When it comes to autonomous systems, it is critical to understand various levels of proposed autonomy, reasons for selecting autonomous capabilities, certification approaches and various challenges or research needs associated with the integration of autonomous systems in the National Airspace System (NAS). Furthermore, there are levels of autonomous systems proposed for different types of aircraft including: single pilot operations, remotely piloted operations, fully autonomous operations, and multiple aircraft managed by a single, remote pilot. This preliminary article aims to provide a broad overview rather than concentrate on a specific element of autonomous flight. Hence, the focus of this article is on enabling various types of increasingly autonomous aircraft and their operations routinely within the next five to ten years, consistent with the FAA’s Info-Centric NAS (ICN) vision. Under ICN, a fully integrated information environment supports collaboration across diverse traffic management services and shifts decision-making to the most appropriate actor supported by automation. However, the roles and responsibilities of Air Traffic Services (ATS) personnel providing traditional services are likely to be very similar to today in that humans will still be responsible for majority of decision making. There is another NASA research effort beyond ICN is underway called Sky For All which is not covered in this article as it is in the planning stages.

autonomy↗
NASA NTRSDate not supplied

Test & Measurement System Security in an IT World

Automated test and measurement systems are coming under increased cybersecurity scrutiny. Most of these systems fall under the “Operational Technology” designation, as defined by NIST, and often have unique requirements that conflict with enterprise security policy. These systems are typically not well understood by traditional enterprise IT personnel, which leaves them ill-supported or invalidated.▪This presentation attempts to help Test System owners recognize the security landscape, determine their unique system requirements and concerns, and negotiate a peer-level working arrangement with an existing IT department while maintaining a NIST-recommended level of autonomy and sovereignty.

automated test↗
NASA NTRS2020

Supervised Autonomy for Communication-Degraded Subterranean Exploration by a Robot Team

The importance of autonomy in robotics is magnified when the robots need to be deployed and operated in areas that are too dangerous or not accessible for humans, ranging from disaster areas (to assist in emergency situations) to Mars exploration (to uncover the mystery of our neighboring planet). The DARPA Subterranean (SubT) Challenge presents a great opportunity and a formidable robotics challenge to foster such technological advancement for operations in extreme and underground environments. Robot teams are expected to rapidly map, navigate, and search underground environments including natural cave networks, tunnel systems, and urban underground infrastructure. Subterranean environments pose significant challenges for manned and unmanned operations due to limited situational awareness. In the first phase of the DARPA Subterranean Challenge (held in August 2019; targeting underground tunnels and mines), Team CoSTAR, led by NASA JPL, placed second among 11 teams across the world, accurately mapping several kilometers of two mine systems and localizing 17 target objects in the course of four one-hour missions. While the main goal of Team CoSTAR at the end of this threeyear challenge (August 2021) is a fully autonomous robotic solution, this paper describes Team CoSTAR’s results in the first phase of the challenge (August 2019), focusing on supervised autonomy of a multi-robot team under severe communication constraints. This paper also presents the design and initial results obtained from field test campaigns conducted in various tunnel-like environments, leading to the competition.

Otsu, Kyohei↗
NASA NTRSDate not supplied

Distributed Spacecraft Autonomy - Development of Swarm Autonomy Capability and Scalability for Spacecraft

The Distributed Spacecraft Autonomy project is developing a suite of software tools that enable an operator to command and receive data from a swarm as a single entity, enable a swarm to autonomously coordinate its actions via distributed decision making and reactive closed-loop control, and model swarm behavior in the presence of anomalies or failures. Our use case is the mapping of the electron density of the ionosphere using radio tomography by coordinating the selection of appropriate GPS channels, and by recording Total Electron Count (TEC) measurements. DSA will be demonstrated onboard the NASA Ames Starling mission – a swarm of four small, LEO spacecraft, scheduled to launch in 2021. We will also perform a ground demonstration with simulated and hardware-in-the-loop elements, to validate the tools for controlling swarms of up to 100 assets. The capability to communicate autonomously between the swarm satellites is demonstrated via a sophisticated simulation architecture. Historical Plasmasphere TEC data obtained via dual-band Novatel GPS Receivers are utilized as a representative input dataset for the swarm. The representative TEC data and GPS satellite observability information is fed to the autonomous software package in place of a true real-time ground data collection process. The swarm satellites actively share status updates amongst one another and utilize multi-agent decision making to optimally identify regions of interest in the TEC distribution. The software, aware of the bandwidth limitations of the swarm satellites, prioritizes explorative measurements, which define the range of observability for the satellites, as well as exploitative measurements, which focus on maximizing the observance potential of regions with prolonged, elevated TEC density. The science of this study can ultimately be used to determine the dynamics and coupling of Earth’s magnetosphere, ionosphere, and atmosphere and their response to solar and terrestrial inputs. The findings can be applied to the imaging of critical, transient phenomena in the magnetosphere in later missions. Meanwhile, the swarm autonomy capabilities have far reaching potential in future satellite missions. As an experimental demonstration of the autonomous capabilities of the network, a message is first printed within a core Flight Executive (cFE) application. Two cFE applications that communicate with one another within the same core Flight System (cFS) are shown. Communication between mission applications on the internal cFE bus is extended to utilize Data Distribution Service (DDS) for vehicle-to-vehicle networking. The DDS middleware provides reliable delivery, routing, and topic subscription features over User Datagram Protocol (UDP). Leveraging Linux containerization, a networked set of satellite instances are generated by script to simulate swarm behavior. Swarm commanding and synchronization through the network is demonstrated under various topologies and data-loss conditions. Finally, autonomous swarm scalability from 2 satellites to 100 satellites is shown.

Distributed Autonomy↗
NASA NTRS2020

Distributed Spacecraft Autonomy (DSA): Development of Swarm Autonomy Capability and Scalability for Spacecraft

The Distributed Spacecraft Autonomy project is developing a suite of software tools that enable an operator to command and receive data from a swarm as a single entity, enable a swarm to autonomously coordinate its actions via distributed decision making and reactive closed-loop control, and model swarm behavior in the presence of anomalies or failures. Our use case is the mapping of the electron density of the ionosphere using radio tomography by coordinating the selection of appropriate GPS channels, and by recording Total Electron Count (TEC)measurements. DSA will be demonstrated on board the NASA Ames Starling mission a swarm of four small, LEO spacecraft, scheduled to launch in 2021. We will also perform a ground demonstration with simulated and hardware-in-the-loop elements, to validate the tools for controlling swarms of up to 100 assets.The capability to communicate autonomously between the swarm satellites is demonstrated via a sophisticated simulation architecture. Historical Plasma sphere TEC data obtained via dual-band Novatel GPS Receivers are utilized as a representative input data set for the swarm. The representative TEC data and GPS satellite observability information is fed to the autonomous software package in place of a true real-time ground data collection process. The swarm satellites actively share status updates amongst one another and utilize multi-agent decision making to optimally identify regions of interest in the TEC distribution. The software,aware of the bandwidth limitations of the swarm satellites, prioritizes explorative measurements,which define the range of observability for the satellites, as well as exploitative measurements,which focus on maximizing the observance potential of regions with prolonged, elevated TEC density. The science of this study can ultimately be used to determine the dynamics and coupling of Earth's magnetosphere, ionosphere, and atmosphere and their response to solar and terrestrial inputs. The findings can be applied to the imaging of critical, transient phenomena in the magnetosphere in later missions. Meanwhile, the swarm autonomy capabilities have far reaching potential in future satellite missions.As an experimental demonstration of the autonomous capabilities of the network, a message is first printed within a core Flight Executive (cFE) application. Two cFE applications that communicate with one another within the same core Flight System (cFS) are shown.Communication between mission applications on the internal cFE bus is extended to utilize Data Distribution Service (DDS) for vehicle-to-vehicle networking. The DDS middle ware provides reliable delivery, routing, and topic subscription features over User Data gram Protocol (UDP).Leveraging Linux containerization, a networked set of satellite instances are generated by script to simulate swarm behavior. Swarm commanding and synchronization through the network is demonstrated under various topologies and data-loss conditions. Finally, autonomous swarms calability from 2 satellites to 100 satellites is shown.

Fugate, Jason↗
NASA NTRS2022

A Framework for Dynamic Architecture and Functional Allocations for Increasing Airspace Autonomy

To enable scalability of air travel for use cases such as cargo delivery, it is anticipated that future air traffic operations will involve unmanned aircraft operated by remote pilots. Of particular interest are schemes where a small number of pilots operate a large number of vehicles, mitigating high cost and pilot shortage issues. Such architectures require increased levels of automation and supervisory control modes. They also require ensuring safe operations when the command and control link to the vehicle is degraded or lost completely, rendering the vehicle autonomous. To evaluate these variable and dynamic architectures, this paper will present a framework for decomposing the functions necessary to ensure safe, orderly, and expeditious air travel, assessing the agents in the system, and classifying the levels of autonomy. Then, an example allocation to agents of roles for the function of separation assurance is presented, highlighting the dependency of the allocation on three main factors: time criticality of a potential separation violation, the ratio of pilots to vehicles, and the loss of the command and control link.

air traffic management, function allocation, auton↗
NASA NTRS2022

A Framework for Dynamic Architecture and Functional Allocations for Increasing Airspace Autonomy

To enable scalability of air travel for use cases such as cargo delivery, it is anticipated that future air traffic operations will involve unmanned aircraft operated by remote pilots. Of particular interest are schemes where a small number of pilots operate a large number of vehicles, mitigating high cost and pilot shortage issues. Such architectures require increased levels of automation and supervisory control modes. They also require ensuring safe operations when the command and control link to the vehicle is degraded or lost completely, rendering the vehicle autonomous. To evaluate these variable and dynamic architectures, this paper will present a framework for decomposing the functions necessary to ensure safe, orderly, and expeditious air travel, assessing the agents in the system, and classifying the levels of autonomy. Then, an example allocation to agents of roles for the function of separation assurance is presented, highlighting the dependency of the allocation on three main factors; time criticality of a potential separation violation, the ratio of pilots to vehicles, and the loss of the command and control link.

air traffic management↗
NASA NTRS1988

Blackboard architectures and their relationship to autonomous space systems

The blackboard architecture provides a powerful paradigm for the autonomy expected in future spaceborne systems, especially SDI and Space Station. Autonomous systems will require skill in both the classic task of information analysis and the newer tasks of decision making, planning and system control. Successful blackboard systems have been built to deal with each of these tasks separately. The blackboard paradigm achieves success in difficult domains through its ability to integrate several uncertain sources of knowledge. In addition to flexible behavior during autonomous operation, the system must also be capable of incrementally growing from semiautonomy to full autonomy. The blackboard structure allows this development. The blackboard's ability to handle error, its flexible execution, and variants of this paradigm are discussed as they apply to specific problems of the space environment.

Thornbrugh, Allison↗
NASA NTRS1992

Ground-remote control for space station telerobotics with time delay

The study proposes a ground-remote telerobot control architecture which could be used for control of Space Station Freedom manipulators. The architecture provides two local-site operator control stations representing potential earth-based and remote Space Station-based operator control stations. A unified control system at the remote site provides autonomous, shared, and teleoperation control for single-and dual-arm task execution. An operational laboratory system which demonstrates the feasibility of various technologies in the proposed architecture, including teleoperation, shared control, and supervised autonomy, is described. Enhancements to the system currently under development, including remote site implementation in Ada, integration and control of a redundant 7-DOF manipulator, and local site advanced operator aids, are also described.

Backes, Paul G.↗
NASA NTRS2021

FRESCO: A Framework for Spacecraft Systems Autonomy

Achieving the science exploration and defense goals of the following decades will require flight systems capable of operations with limited operator contact, system mode changes and retasking based on sensor data, and complex robotic operations. To support these capabilities, increasingly autonomous flight systems are required that can perform dedicated mission functions, e.g. payload targeting and communications, and system-level functions, e.g. planning and goal monitoring. Architecting an autonomous system requires a well-reasoned, self-consistent framework to avoid \textit{ad hoc} design choices that will introduce complexity and risk. The Framework for Robust Execution and Scheduling of Commands On-Board, FRESCO, is the result of lessons learned in developing a software architecture to enable autonomous solar system exploration. FRESCO generalizes this work to offer a modular, software-agnostic approach to developing verifiable architecture for autonomous space systems. FRESCO specifies guiding principles, functions, interfaces, and interactions from which mission-specific autonomous control architectures can be derived. FRESCO is a principled framework relying on explicit, state-based goal definitions, centralized management of state knowledge, clearly separated control boundaries, and hierarchical reasoning. Using components from FRESCO reference architecture, an autonomous decision-making architecture can be designed for spacecraft which can then be mapped to flight software architecture. FRESCO is flexibly defined to enable autonomous control of flight systems built using extensive software and hardware heritage. Finally, FRESCO-derived architectures support a spectrum of operator/spacecraft interactions, ranging from traditional commanding to goal-driven commanding with the ability to change mission goals autonomously. FRESCO has been used in defining the autonomy architectures for the ASTERIA mission and have been demonstrated in laboratory and software simulation for small body rendezvous and in-space servicing missions.

Kolcio, Ksenia↗
NASA NTRS1992

Meeting human needs

The degree of autonomy of future long duration manned missions will emphasize interactions between human operators and automated systems aimed at the most effective allocations of tasks between humans and machines. Knowledge of crewmembers' physical status, encompassing both capabilities and limitations, will also be critical during EVA and planetary roving missions; psychological evaluation and support, with a view to both individual health and group cohesion and productivity, may become a critical consideration. Attention is here given to crewmembers' medical and psychological vulnerabilities.

Nicogossian, Arnauld E.↗