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NASA NTRSDate not supplied

Development and Field Test Results of Distributed Ground Sensor Fusion Based Object Tracking

Autonomous operations are a crucial aspect in the context of Advanced Air Mobility and other emerging aviation markets. In order to enable this autonomy, an accurate and detailed understanding of the positions of the various vehicles in the air is necessary. Full localization independent of on-board sensors makes the system suitable for noncooperative vehicles. This paper focuses on the object tracking part that relies on distributed ground-based RF and other sensor fusion, considering specific properties and limitations of different sensor types. Results show satisfactory performance in nominal scenarios with full coverage for some sensor types, but RF signals are challenging because of their nature. This paper includes the results from simulations as well as field tests to support the observations and conclusions.

sensor fusion↗

NASA NTRS2023

Distributed Ground Sensor Fusion Based Object Tracking for Autonomous Advanced Air Mobility Operations

Autonomous operations are a crucial aspect in the context of Advanced Air Mobility and other emerging aviation markets. In order to enable this autonomy, an accurate and detailed understanding of the positions of the various vehicles in the air is necessary. Full localization independent of on-board sensors makes the system suitable for noncooperative vehicles. This paper focuses on the object tracking part that relies on distributed ground-based sensor fusion, considering specific properties and limitations of different sensor types. Results show satisfactory performance in nominal scenarios with full coverage. Dropouts of individual sensors affect the accuracy of the tracking results, which agrees with expectations for partial coverage, when full localization is not achievable anymore. Finally, a study is performed to identify which parameters have the largest impact on the fit error.

Autonomous↗

NASA NTRS2022

Science Autonomy for Ocean Worlds Astrobiology: A Perspective

Astrobiology missions to ocean worlds in our solar system must overcome both scientific and technological challenges due to extreme temperature and radiation conditions, long communication times, and limited bandwidth. While such tools could not replace ground-based analysis by science and engineering teams, machine learning algorithms could enhance the science return of these missions through development of autonomous science capabilities. Examples of science autonomy include onboard data analysis and subsequent instrument optimization, data prioritization (for transmission), and real-time decision-making based on data analysis. Similar advances could be made to develop streamlined data processing software for rapid ground-based analyses. Here we discuss several ways machine learning and autonomy could be used for astrobiology missions, including landing site selection, prioritization and targeting of samples, classification of “features” (e.g., proposed biosignatures) and novelties (uncharacterized, “new” features, which may be of most interest to agnostic astrobiological investigations), and data transmission.

ocean worlds↗

NASA NTRSDate not supplied

2018 Workshop on Autonomy for Future NASA Science Missions

Autonomy is changing our world; commercial enterprises and academic institutions are developing and deploying drones, robots, self-driving vehicles and other autonomous capabilities to great effect here on Earth. Autonomous technologies will also play a critical and enabling role in future NASA science missions, and the Agency requires a specific strategy to leverage these advances and infuse them into its missions. To address this need, NASA sponsored the 2018 Workshop on Autonomy for NASA Science Missions, held at Carnegie Mellon University, October 10-11, 2018.

Florence W Tan↗

NASA NTRS1990

Demonstrating artificial intelligence for space systems - Integration and project management issues

As part of its Systems Autonomy Demonstration Project (SADP), NASA has recently demonstrated the Thermal Expert System (TEXSYS). Advanced real-time expert system and human interface technology was successfully developed and integrated with conventional controllers of prototype space hardware to provide intelligent fault detection, isolation, and recovery capability. Many specialized skills were required, and responsibility for the various phases of the project therefore spanned multiple NASA centers, internal departments and contractor organizations. The test environment required communication among many types of hardware and software as well as between many people. The integration, testing, and configuration management tools and methodologies which were applied to the TEXSYS project to assure its safe and successful completion are detailed. The project demonstrated that artificial intelligence technology, including model-based reasoning, is capable of the monitoring and control of a large, complex system in real time.

Hack, Edmund C.↗

NASA NTRS2022

Starling Animation

NASA’s Starling mission is advancing the readiness of various technologies for cooperative groups of spacecraft – also known as distributed missions, clusters, or swarms. Starling will demonstrate technologies to enable multipoint science data collection by several small spacecraft flying in swarms. The six-month mission will use four CubeSats in low-Earth orbit to test four technologies that let spacecraft operate in a synchronized manner without resources from the ground. The technologies will advance the following capabilities. • Network Communication • Cluster Flight Control • Relative navigation • Distributed Spacecraft Autonomy The Starling mission will test whether the technologies work as expected, what their limitations are, and what developments are still needed for CubeSat swarms to be successful. This animation illustrates Starling mission operations, offering an overview of each technology being demonstrated.

Howard N Cannon↗

NASA NTRS2021

m:N and Human Autonomy Teaming Concepts for High Density Vertiport Operations

This report focuses on the role of the Fleet Manager (FM) and, in particular, the ways in which automation could support their position as they manage multiple aircraft and operators in a highly dynamic, advanced air mobility (e.g., air taxi) environment, specifically "high-density vertiport" (HDV) regions. Similar to terminal area operations for traditional aviation, operations involving HDVs will need to be highly structured while also remaining resilient to the various contingencies that can happen in that environment. The role of the FM is consistent with an “m:N” architecture, where “m” number of operators cooperatively manage “N” number of vehicles (where “N” is always larger than “m”). In such a paradigm it is critical to provide the operator with the tools and information necessary to manage their fleet safely and navigate the known pitfalls with highly automated, complex systems (e.g., brittleness, insufficient situation awareness, skill degradation). As the field of m:N has expanded as an area of study, a set of higher-level automation concepts have emerged—namely “plays,” “working agreements,” and “human-autonomy teaming” (HAT)—that could support operators in this new role.

human autonomy teaming↗

NASA NTRS1988

Ascent Guidance for a Winged Boost Vehicle

The objective of the advanced ascent guidance study was to investigate guidance concepts which could contribute to increased autonomy during ascent operations in a winged boost vehicle such as the proposed Shuttle II. The guidance scheme was required to yield near a full-optimal ascent in the presence of vehicle system and environmental dispersions. The study included consideration of trajectory shaping issues, trajectory design, closed loop and predictive adaptive guidance techniques and control of dynamic pressure by throttling. An extensive ascent vehicle simulation capability was developed for use in the study.

Corvin, Michael Alexander↗

NASA NTRS1990

Evolution and advanced technology

The NASREM architecture with its standard interfaces permits development and evolution of the Flight Telerobotic Servicer to greater autonomy. Technologies in control strategies for an arm with seven DOF, including a safety system containing skin sensors for obstacle avoidance, are being developed. Planning and robotic execution software includes symbolic task planning, world model data bases, and path planning algorithms. Research over the last five years has led to the development of laser scanning and ranging systems, which use coherent semiconductor laser diodes for short range sensing. The possibility of using a robot to autonomously assemble space structures is being investigated. A control framework compatible with NASREM is being developed that allows direct global control of the manipulator. Researchers are developing systems that permit an operator to quickly reconfigure the telerobot to do new tasks safely.

Ollendorf, Stanford↗

NASA NTRS2003

CLARAty Functional-Layer Software

Functional-layer software for the Coupled Layer Architecture for Robotics Autonomy (CLARAty) is being developed. [CLARAty was described in Coupled-Layer Architecture for Advanced Software for Robots (NPO-21218), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48. To recapitulate: CLARAty was proposed to improve the modularity of robotic software while tightening the coupling between planning/execution and control subsystems. Whereas prior robotic software architectures have typically contained three levels, the CLARAty architecture contains two layers: a decision layer and a functional layer.] Just as an operating system provides abstraction from computational hardware, the CLARAty functional-layer software provides for abstraction for the different robotic systems. The functional-layer software establishes interrelated, object-oriented hierarchies that contain active and passive objects that represent the different levels of system abstrations and components. The functional-layer software is decomposed into a set of reusable core components and a set of extended components that adapt the reusable set to specific hardware implementations. The reusable components (a) provide behavior and interface definitions and implementations of basic functionality, (b) provide local executive capabilities, (c) manage local resources, and (d) support state and resource queries by the decision layer. Software for robotic systems can be built by use of these components.

Nesnas, Issa↗

NASA NTRS2016

Requirements for Designing Life Support System Architectures for Crewed Exploration Missions Beyond Low-Earth Orbit

NASA's technology development roadmaps provide guidance to focus technological development on areas that enable crewed exploration missions beyond low-Earth orbit. Specifically, the technology area roadmap on human health, life support and habitation systems describes the need for life support system (LSS) technologies that can improve reliability and in-situ maintainability within a minimally-sized package while enabling a high degree of mission autonomy. To address the needs outlined by the guiding technology area roadmap, NASA's Advanced Exploration Systems (AES) Program has commissioned the Life Support Systems (LSS) Project to lead technology development in the areas of water recovery and management, atmosphere revitalization, and environmental monitoring. A notional exploration LSS architecture derived from the International Space has been developed and serves as the developmental basis for these efforts. Functional requirements and key performance parameters that guide the exploration LSS technology development efforts are presented and discussed. Areas where LSS flight operations aboard the ISS afford lessons learned that are relevant to exploration missions are highlighted.

Howard, David↗

NASA NTRS2016

Guiding Requirements for Designing Life Support System Architectures for Crewed Exploration Missions Beyond Low-Earth Orbit

The National Aeronautics and Space Administration's (NASA) technology development roadmaps provide guidance to focus technological development in areas that enable crewed exploration missions beyond low-Earth orbit. Specifically, the technology area roadmap on human health, life support and habitation systems describes the need for life support system (LSS) technologies that can improve reliability and in-flight maintainability within a minimally-sized package while enabling a high degree of mission autonomy. To address the needs outlined by the guiding technology area roadmap, NASA's Advanced Exploration Systems (AES) Program has commissioned the Life Support Systems (LSS) Project to lead technology development in the areas of water recovery and management, atmosphere revitalization, and environmental monitoring. A notional exploration LSS architecture derived from the International Space has been developed and serves as the developmental basis for these efforts. Functional requirements and key performance parameters that guide the exploration LSS technology development efforts are presented and discussed. Areas where LSS flight operations aboard the ISS afford lessons learned that are relevant to exploration missions are highlighted.

Perry, Jay L.↗

NASA NTRS2016

Architectural and Behavioral Systems Design Methodology and Analysis for Optimal Habitation in a Volume-Limited Spacecraft for Long Duration Flights

As our human spaceflight missions change as we reach towards Mars, the risk of an adverse behavioral outcome increases, and requirements for crew health, safety, and performance, and the internal architecture, will need to change to accommodate unprecedented mission demands. Evidence shows that architectural arrangement and habitability elements impact behavior. Net habitable volume is the volume available to the crew after accounting for elements that decrease the functional volume of the spacecraft. Determination of minimum acceptable net habitable volume and associated architectural design elements, as mission duration and environment varies, is key to enabling, maintaining, andor enhancing human performance and psychological and behavioral health. Current NASA efforts to derive minimum acceptable net habitable volumes and study the interaction of covariates and stressors, such as sensory stimulation, communication, autonomy, and privacy, and application to internal architecture design layouts, attributes, and use of advanced accommodations will be presented. Furthermore, implications of crew adaptation to available volume as they transfer from Earth accommodations, to deep space travel, to planetary surface habitats, and return, will be discussed.

Human Space Flight↗

NASA NTRS1997

The TSTAR Autonomy Test Tool

The new breed of autonomous goal-driven spacecraft contain much more onboard capability than their syquence-driven predecessors, demanding corresponding advances in software verification techniques. Although autonomous systems are deterministic, they are hightly sensitive to the environment, such that the response of a system in certain contexts must be explored in detail in order to prove confidence in both the design and implementaiton.

Autonomy↗

NASA NTRSDate not supplied

Where is the Human in the Loop? Human Factors Analysis of Extended Visual Line of Sight Unmanned Aerial System Operations within a Remote Operations Environment

Many envisioned technological and conceptual innovations focus on allocating more functions to automation, relegating the human as an afterthought if not a nuisance. Yet, until complete autonomy is realized, the human will remain “in the loop”. The National Aeronautics and Space Administration is supporting research for the development and maturation of automated technologies and architectures for the future of advanced air mobility. Standing up a remote operations center used to control, manage, and monitor multiple highly automated vehicles is an important step towards realizing the advanced air mobility vision. At the National Aeronautics and Space Administration’s Langley Research Center, a remote operation center exists and has been tested using humans piloting simulated vehicles. In this paper, we explore the human element within live flight operations that rely on increasingly automated technologies. Four ground control station operators performed multiple live flight operations. We employed a naturalistic approach and relied on qualitative data such as interviews and discussions with subject matter experts to help facilitate discovery. The present work evaluates the functions that the human and the automation had during the live operations, lists psychological constructs that may have promoted or reduced task performance, and provides recommendations on design of the remote operations environment and training of future ground control station operators.

Advanced Air Mobility↗

NASA NTRSDate not supplied

Where is the Human in the Loop? Human Factors Analysis of Extended Visual Line of Sight Unmanned Aerial System Operations within a Remote Operations Environment

Advanced Air Mobility↗

NASA NTRSDate not supplied

Thinking outside the box: The human role in increasingly automated aviation systems

Rapid advances in artificial intelligence are enabling automated systems to operate in an increasingly autonomous manner in domains that previously required the involvement of human operators. Examples are rail transport systems, self-driving cars, and warehouse delivery systems. From time to time, such automation encounters operational conditions that fall outside a “competency box” within which the system has been designed to operate. Human operators add resilience because they can see and act outside the competency box of scenarios and environments for which the system was designed. The system’s competencies can be expanded over time with modifications to software, sensors, etc.; however, it is unclear at what point the competency box becomes large enough to safely eliminate the role of the human operator. One area where advanced automation may be applied is Urban Air Mobility (UAM). Current UAM concepts envision fleets of highly automated air vehicles providing on-demand transport for people and goods. A phased development of UAM has been proposed, beginning with on-board pilots and transitioning to a future state where automated vehicles operate with minimal human involvement. Proponents of UAM note that this final state reduces cost as well as eliminating pilot error, identified as a contributing factor in many aircraft accidents. However, eliminating human involvement also risks eliminating their positive contributions to system resilience. Here we examine Concepts of Operation proposed for future UAM systems and explore how humans can best be incorporated to maintain resilience while minimizing cost and risk. A human-autonomy teaming approach is suggested.

Advanced Air Mobility↗

NASA NTRS2016

Application of Human-Autonomy Teaming (HAT) Patterns to Reduced Crew Operations (RCO)

As part of the Air Force - NASA Bi-Annual Research Council Meeting, slides will be presented on recent Reduced Crew Operations (RCO) work. Unmanned aerial systems, robotics, advanced cockpits, and air traffic management are all examples of domains that are seeing dramatic increases in automation. While automation may take on some tasks previously performed by humans, humans will still be required, for the foreseeable future, to remain in the system. The collaboration with humans and these increasingly autonomous systems will begin to resemble cooperation between teammates, rather than simple task allocation. It is critical to understand this human-autonomy teaming (HAT) to optimize these systems in the future. One methodology to understand HAT is by identifying recurring patterns of HAT that have similar characteristics and solutions. A methodology for identifying HAT patterns to an advanced cockpit project is discussed.

Human-Autonomy Teaming (HAT)↗