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NASA NTRS2018

A Conceptual Enterprise Framework for Managing Scientific Data Stewardship

Scientific data stewardship is an important part of long-term preservation and the use/reuse of digital research data. It is critical for ensuring trustworthiness of data, products, and services, which is important for decision-making. Recent U.S. federal government directives and scientific organization guidelines have levied specific requirements, increasing the need for a more formal approach to ensuring that stewardship activities support compliance verification and reporting. However, many science data centers lack an integrated, systematic, and holistic framework to support such efforts. The current business- and process-oriented stewardship frameworks are too costly and lengthy for most data centers to implement. They often do not explicitly address the federal stewardship requirements and/or the uniqueness of geospatial data. This work proposes a data-centric conceptual enterprise framework for managing stewardship activities, based on the philosophy behind the Plan-Do-Check-Act (PDCA) cycle, a proven industrial concept. This framework, which includes the application of maturity assessment models, allows for quantitative evaluation of how organizations manage their stewardship activities and supports informed decision-making for continual improvement towards full compliance with federal, agency, and user requirements.

Scientific data stewardship↗

NASA NTRS2019

Application of AI in the NAS - the Rationale for AI-Enhanced Airspace Management

This paper extends on the initial findings of "Application of Artificial Intelligence in the National Airspace System: A Primer" (Stroup & Niewoehner: Herndon, VA; ICNS-2019), and looks at why the current technologies, enterprise architecture, and future program plans may not be enough to address persistent operational challenges. This paper further explores why emergent operational concepts, business models, and demand profiles may necessitate AI-enhanced Communications, Navigation and Communications (CNS) infrastructure to disrupt current operational impediments. European airspace, as well as the NAS, has similar challenges. Key challenges explored in this study include: quantifiable improvements to NAS capacity, efficiency, and resiliency; traffic flow management of diverse users; UTM-ATM airspace integration; equitable access to airspace; and airborne-ground interoperability of AI applications. Finally, we examine why trustworthiness and resiliency will be key mileposts on the regulatory pathway to AI certification.

Aviation↗

NASA NTRS2018

Towards Informing an Intuitive Mission Planning Interface for Autonomous Multi-Asset Teams via Image Descriptions

Establishing a basis for certification of autonomous systems using trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR). The Human-Machine Interface (HMI) team is working to capture and utilize the multitude of ways in which humans are already comfortable communicating mission goals and translate that into an intuitive mission planning interface. Several input/output modalities (speech/audio, typing/text, touch, and gesture) are being considered and investigated in the context human-machine teaming for the ATTRACTOR design reference mission (DRM) of Search and Rescue or (more generally) intelligence, surveillance, and reconnaissance (ISR). The first of these investigations, the Human Informed Natural-language GANs Evaluation (HINGE) data collection effort, is aimed at building an image description database to train a Generative Adversarial Network (GAN). In addition to building an image description database, the HMI team was interested if, and how, modality (spoken vs. written) affects different aspects of the image description given. The results will be analyzed to better inform the designing of an interface for mission planning.

Generative Adversarial Network (GAN)↗

NASA NTRS2019

3+2+X: What Is the Most Useful Depolarization Input for Inverting Lidar Measurements of Non-Spherical Particles to Microphysical Properties?

The typical multiwavelength aerosol lidar data set for inversion of optical to microphysical parameters is composed of three backscatter coefficients (β ) at 355, 532, and 1064 nm and two extinction coefficients (α ) at 355 and 532 nm. This data combination is referred to as 3β +2α or 3+2 data set. This set of data is sufficient for retrieving some important microphysical particle parameters if the particles have spherical shape. Here, we investigate the effect of including the particle linear depolarization ratio (δ) as a third input parameter to the inversion of lidar data. The inversion algorithm is generally not used if measurements show values of δ that exceed 0.10 at 532 nm, i.e. in the presence of non-spherical particles such as desert dust, volcanic ash, and under special circumstances biomass-burning smoke.We use experimental data collected with instruments that are capable of measuring δ at all three lidar wavelengths with an inversion routine that uses the theory of light scattering by randomly oriented spheroids to replicate scattering properties of non-spherical particles. This is the first systematic test of the effect of using all theoretically possible combinations of δ taken at 355, 532, and 1064 nm as input in the lidar data inversion. We find that depolarization information at least at one wavelength already provides useful information in the in version of optical data that describe light-scattering by nonspherical particles. However, any choice of δ(λ) will give lower values of the single-scattering albedo than the traditional 3+2 data set. We find that input data sets that include 355 give a non-spherical fraction that closely resembles the dust ratio we obtain from using β(532) and δ(532) in a methodology applied in aerosol-type separation. The use of 355 in data sets of two or three reduces the fraction of non-spherical particles that is retrieved when using δ(532) and δ(1064). Use of the latter two without accounting for 355 generally leads to high fractions of non-spherical particles that we consider not trustworthy. The use of three δ(λ) instead of two δ(λ) including the constraint that one of these is measured at 355 nm does not provide any advantage over using 3+2+δ(355). Because of the technical challenges involved with accurately measuring δ(1064) we conclude that — depending on measurement capability — the future standard input for inversion using spheroid kernels might be 3+2+δ(355) or 3+2+δ(355)+δ(532).

Tesche, M.↗

NASA NTRSDate not supplied

Enhancing Neural Network Decision-Making with Variational Autoencoders

Machine intelligence has been used to tackle increasingly complex problems and deep learning solutions are at the forefront of tackling these problems. In general, these architectures have a great number of parameters that are methodically updated in training. The vast number and complexity of deep neural networks makes it very difficult to decipher the inner workings of the neurons and layers that make up the network. This paper posits that trustworthiness and trust in autonomous systems are increased through eXplainable Artificial Intelligence (XAI) and presents a method that enhances the explainability and understanding of a neural network decision. We leverage variational autoencoders to produce human interpretable features from complex data sets. We show that the explainable features can then be used for machine learning applications. This explainability encourages people to be more inclined to justifiably trust machine decision-making.

Loc Tran↗

NASA NTRSDate not supplied

Spatial and Temporal Deconfliction of Trajectories in the Presence of Uncertainties

Demonstration of conflict-free movement for multi-agent teams in challenging scenarios is crucial in developing trust and trustworthiness in an autonomous transport system. Tolerance verification queries are explored as a mechanism to enforce spatial and temporal deconfliction for a cooperating team of Unmanned Aerial Systems (UAS) with prescribed heterogeneous path-following performance. Obstacles in the environment are modelled as set of polyhedra, whereas each vehicle’s trajectory is represented as a sequence of polynomial curves with C2 continuity, expressed in a Bernstein basis. Each vehicle is modelled as a point mass and a safety distance, informed by the geometry of the UAS and the worst-case path-following error. This defines a tube around the trajectories where each UAS is most likely to fly through. In addition, obstacles in the environment have an associated safety buffer around them to account for the uncertainty in their location and geometric description. The tolerance verification queries explored in this paper combine the safety distance information from each UAS and environmental hazard to compute trajectories that are contained within the safe configuration space. Tolerance verification is also compared with other proximity queries to determine the suitability of each method along the different steps of the trajectory generation algorithm. This paper analyzes the fitness and performance of three proximity queries – collision, tolerance verification, and distance computations – between polyhedral and polynomial curves to ensure deconfliction between obstacles and vehicles, but also between polynomial curves to guarantee safe separation among cooperating UAS.

trajectory generation↗

NASA NTRSDate not supplied

Bootstrapping Multi-Agent Unmanned Aerial Vehicle (UAV) System Integration Using Ground-Based Assets: Lessons Learned

The highly dynamic nature of UAVs imposes significant challenges when conducting initial testing ranging from safety risks posed by high-capacity lithium batteries and spinning propellers to rigorous timing demands on controllers and the consequences of failures mid-air. Flight testing of a single vehicle is time and labor intensive due to these challenges and more, and the complexity increases exponentially with the number of vehicles. While simulations and hardware-in-the-loop bench testing can provide adequate environments for preliminary validation, differences in system deployment architecture, software interfaces, and hardware infrastructure between simulation and a fleet of real UAVs create a sizable gap that must be navigated carefully during system integration. In support of the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project, which had the goal of establishing a basis of certification of trust and trustworthiness in multi-agent autonomous systems, this gap was tackled from two directions. First, a novel mixed-reality simulation environment was engineered to blur the transition from simulation to flight hardware. Second, a fleet of Unmanned Surface Vehicles (USVs) was developed as a test and evaluation platform that more closely represented the final aerial fleet while eliminating many of the risks associated with air vehicles. This paper delves into the second element, analyzing the efficacy of the USV platform in performing system integration testing for the UAV system. In this paper we present the USV fleet and its role in reducing the aforementioned gaps in deployment architecture, software interfaces, and hardware infrastructure when moving from simulation to flight. An overview of the hardware and software onboard the vehicles will be provided along with supporting infrastructure. The system integration process will be documented including results in supporting both the overarching design reference mission (DRM) of ATTRACTOR and individual research efforts conducted during the project. Finally, we will discuss some of the practical lessons learned regarding the testing, deployment, and operation of multi-agent autonomous systems.

Matthew P Vaughan↗

NASA NTRSDate not supplied

Enhancing Neural Network Explainability with Variational Autoencoders

Machine intelligence has been used to tackle increasingly complex problems and deep learning solutions are at the forefront of tackling these problems. In general, these architectures have a great number of parameters that are methodically updated in training. The vast number and complexity of deep neural networks makes it very difficult to decipher the inner workings of the neurons and layers that make up the network. This paper posits that trustworthiness and trust in autonomous systems are increased through eXplainable Artificial Intelligence (XAI) and presents a method that enhances the explainability and understanding of a neural network decision. We leverage variational autoencoders to produce human interpretable features from complex data sets. We show that the explainable features can then be used for machine learning applications. Explainability inspires trust in autonomous systems that use deep learning, which is necessary for safety critical systems.

Loc Tran↗

NASA NTRSDate not supplied

Bootstrapping Multi-Agent Unmanned Aerial Vehicle (UAV) System Integration Using Ground-Based Assets: Lessons Learned

In support of the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project, a fleet of unmanned ground vehicles (UGVs) was developed as a test and evaluation (T\&E) platform to reduce system integration gaps between simulation and live flight hardware. While simulation and hardware-in-the-loop bench testing provide adequate environments for preliminary validation, differences in system deployment architecture, software interfaces, and hardware infrastructure increase the risks to safety, property, and the project. Given ATTRACTOR’s goal of establishing a basis of certification of trust and trustworthiness in multi-agent autonomous systems, bridging these gaps was critical to successful project execution and feasibility assessment. In this paper we present the UGV fleet and its role in speeding up system integration, smoothing the transition from simulation to flight, and providing researchers an easy-to-use hardware test bed. An overview of the hardware and software on-board the vehicles is provided along with supporting infrastructure. The system integration process is documented including results in supporting both the overarching design reference mission (DRM) of ATTRACTOR and individual research efforts conducted since the creation of the fleet. Finally, we discuss the practical lessons learned regarding the testing, deployment, and operation of multi-agent autonomous systems.

Matthew P. Vaughan↗

NASA NTRSDate not supplied

Exploring Multimodal Interactions in Human-Autonomy Teaming Using a Natural User Interface

The creation of a multimodal, natural user interface to facilitate multi-agent interaction is essential to establishing trust among human and machine teammates in multi-agent systems. Trust is being researched, along with trustworthiness, as a path to certification of autonomous systems by the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project at NASA. The Autonomous Mission Experimental Logistics Interactive Assistant (AMELIA) is a natural user interface that enables multimodal interaction and is designed for rapid mission planning. AMELIA is an intelligent system that considers the user’s preferred communication strategies, as well as the time-critical aspect of the multi-agent system decision-making process. Twenty-four participants planned a multi-agent search and rescue mission, with the aid of an intelligent assistant. The results show that while the combined use of touch and speech was faster than speech alone, the single modality, touch, was still the most efficient. Future research should investigate additional input technologies.

Lisa R Le Vie↗

NASA NTRSDate not supplied

Exploring Multimodal Interactions in Human-Autonomy Teaming Using a Natural User Interface

Lisa Renee Le Vie↗

NASA NTRS2018

A Standard Reference Model for Data Archives

An implementable Data Archive Architecture is being developed for trusted digital repositories based on the Reference Model for an Open Archival Information System (OAIS) – ISO 14721. A set of interoperable protocols and interface specifications are planned that will offer capabilities for accessing, merging, and re-using data, both within and across the operational boundaries of trustworthy digital repositories. The model will also provide support for the fundamental scientific need to verify the reproducibility of results. This standards development task is being performed by the Data Archive Interoperability (DAI) working group within the Consultative Committee for Space Data Systems (CCSDS). The architecture integrates concepts from the OAIS Reference Model, the ISO/IEC 11179 Metadata Registry (MDR) standard, the CCSDS Reference Architecture for Space Information Management (RASIM), the proposed draft recommended practice document, Information Preparation to Enable Long Term Use (IPELTU), and three decades of digital repository development for science research.

Ambacher, Bruce↗

NASA NTRSDate not supplied

Recommendations to Advance Space Trusted Autonomy

While the various space agencies of the U.S. federal government have distinctly different visions for future operational space systems, all share important foundational commonalities: improve mission performance, reduce mission costs, incorporate technology advances more rapidly, increase reliability and resiliency, reduce risk, and adapt to anomalies and environmental hazards. These common needs, combined with the maturation of autonomous technology and the prospect of leveraging autonomous systems to address these needs, have led each agency to consider how and when to implement increasing levels of autonomy in their space systems. Simultaneously, the related question of how to determine the trustworthiness of an autonomous system is also escalating in significance.

Christopher A Jones↗

NASA NTRSDate not supplied

Recommendations to Advance Space Trusted Autonomy

The interagency Space Science and Technology Partnership Forum was established in2015 to identify synergistic efforts and technologies across the U.S. government. While the various space agencies of the U.S. government have distinctly different visions for future operational space systems, all share important foundational common needs. These needs, combined with the maturation of autonomous technology and the prospect of leveraging autonomous systems to address those needs, have led each agency to consider how and when to to implement increasing levels of autonomy in their space systems, and how to determine the trustworthiness of an autonomous system. The Partnership facilitated dialogue among the partners, collected and analyzed data on current and desired future levels of capability, and identified gaps to motivate three recommendations that can be addressed within the Partnership community. These recommendations address the need for more robust documenting and socializing of anomalies in space system operations; the need to expand communication and trust within the community of developers, operators, and end users; and the need for a safe development and testing environment for maturing and demonstrating future autonomous space systems. These recommendations will facilitate both near-term programmatic actions and long-term steps for implementing enduring progress towards enabling space trusted autonomy.

Christopher A Jones↗

NASA NTRS2021

Reducing Risk of InSight Surface Operations Through High-Fidelity Command Sequence Modeling

Simulating spacecraft behavior is crucial for the success of deep space missions, and failure to do so may result in damages to or the loss of the spacecraft. Many previous deep space missions have made use of ground-simulation of sequenced commanding, at speeds far greater than real time, to predict spacecraft state over time through the execution of onboard sequences. This type of modeling can be done at any fidelity, and most missions have opted to decrease fidelity to reduce cost and complexity. However, NASA’s Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) mission expanded the scope of ground modeling considerably, which has led to numerous benefits over past implementations. This paper will discuss the process and products that InSight created, as well as the lessons learned from successfully operating the spacecraft on Mars. InSight is the first JPL mission to expand the scope of ground modeling to include the uplink of files from Earth to the spacecraft, rather than making the simplification that any command sequences already exist onboard the spacecraft. The advantages of modeling the uplink of files are numerous. First, it allows for accurate modeling of the onboard filesystem of the spacecraft at all points in time, meaning that all file loads and deletions throughout the mission are modeled at the exact moment they are predicted to actually happen. Second, operators can be more certain that dependencies between sequences are not broken due to the dynamic nature of the filesystem as files are deleted, copied, and uplinked. Lastly, spacecraft filesystem tracking allows for management of sequences prior to uplink, limiting the uplink to only new sequences. The onboard filesystem model became crucial to mission success, emphasizing the importance of investing in accurate models before the need for them arises. During daily tactical operations of a spacecraft on Mars, a model is only useful if the results can be interpreted quickly. In this fast-paced environment, it is essential that command products are modeled and reviewed, errors are found and diagnosed, and new command products are redelivered, remodeled, re-reviewed in a timely manner. It is impossible to review the entire model and therefore the results of the model must be condensed and presented in a fashion that is intuitive, easy-to-navigate, complete, and trustworthy. InSight developed a number of innovative sequence review products that are designed to provide operators with the information required to quickly assess the validity of command products and diagnose potential issues. Together, these products provide a complete, yet succinct picture of the command and sequence model to the operators and facilitate a quick assessment of all sequence command products. This paper will cover planning and sequencing innovations made during InSight surface operations, and will compare the tools, processes, and results to those on other missions. Additionally, the paper will cover the flexible, yet robust nature of the planning and sequencing system architecture and how that flexibility allowed for rapid development and response to the unpredictability of Mars.

Cloutier, Kyle↗

NASA NTRS2021

Automated Data Accountability for Missions in Mars Rover Data

As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.

Divsalar, Dariush↗

NASA NTRS2023

Space Trusted Autonomy Readiness Levels

Technology Readiness Levels are a mainstay for organizations that fund, develop, test, acquire, or use technologies. Technology Readiness Levels provide a standardized assessment of a technology’s maturity and enable consistent comparison among technologies. They inform decisions throughout a technology’s development life cycle, from concept, through development, to use. A variety of alternative Readiness Levels have been developed, including Algorithm Readiness Levels, manufacturing Readiness Levels, Human Readiness Levels, Commercialization Readiness Levels, Machine Learning Readiness Levels, and Technology Commitment Levels. However, while Technology Readiness Levels have been increasingly applied to emerging disciplines, there are unique challenges to assessing the rapidly developing capabilities of autonomy. This paper adopts the moniker of Space Trusted Autonomy Readiness Levels to identify a two-dimensional scale of readiness and trust appropriate for the special challenges of assessing autonomy technologies that seek space use. It draws inspiration from other readiness levels’ definitions, and from the rich field of trust and trustworthiness. The Space Trusted Autonomy Readiness Levels were developed by a collaborative Space Trusted Autonomy subgroup, which was created from The Space Science and Technology Partnership Forum between the United States Space Force, the National Aeronautics and Space Administration, and the National Reconnaissance Office.

Kerianne L Hobbs↗

NASA NTRS2022

NASA's Responsible AI Use Cases

This submission consists of NASA's Responsible Artificial Intelligence (RAI) Use Cases. These RAI Use Cases are to be made public, pursuant to the Presidential Executive Order 13960, Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government. They were collected from NASA's practicing AI research community and cover the gamut of NASA's AI activities. These Use Cases will be updated annually as required by the Executive Order. The information included consists of the NASA Center, a summary of the goal, the AI techniques being applied, information on training data, and information on source code.

Artificial Intelligence↗