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142 records · Page 8

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

A Rapid Target-Search Technique for KBO Exploration Trajectories

A rapid, grid-based, target-search algorithm is presented to find candidate se-quences of small-body encounters for mission design. The algorithm is especially relevant for cases with large combinatorial spaces. In this paper, the al-gorithm is used to identify candidate flyby sequences of multiple Kuiper-Belt Ob-jects (KBOs). Before reaching the first KBO in the sequence, the trajectories in this paper first use gravity assists at one or more of the giant planets to pump-uptheir orbital energy—reducing launch C3. The target-search algorithm consists offour sequential steps: (1) parameter definition, (2) fine-tuned Lambert-based gridsearch of ballistic trajectories visiting one KBO, (3) rapid, ∆V-based proximitysearch for additional KBOs using the state transition matrices (STMs), and (4) tra-jectory optimization of the most promising KBO sequences using the EvolutionaryMission Trajectory Generator (EMTG). The paper also defines an empirical-basedprocess to characterize the maximum step size for the target arrival dates in theLambert grid search. Lastly, a candidate mission to two KBOs is presented. Theresults indicate that the ∆V computed from the STM propagations is not repre-sentative of the final ∆V computed in EMTG; however, it does serve as a useful‘reachability’ metric to identify nearby KBOs.

Miguel Benayas Penas↗

NASA NTRSDate not supplied

Assured Contingency Landing Management for Advanced Air Mobility

Advanced Air Mobility (AAM) is quickly developing as a new air transportation system that moves people and packages in the regions previously not / less served by the current aviation systems. Such AAM must operate safely despite the potential to encounter hazards and experience anomalies and failures in-flight. It becomes especially important to have systematic auto-mitigation strategies to perform safe contingency actions in AAM flight operations, as pilots have limited Situational Awareness (SA) and limited time to make prompt decisions when encountering failures/anomalies in high-density low altitude airspace. This paper presents Assured Contingency Landing Management (ACLM) with an online landing strategy selection to decide between the following three options when a contingency landing is required: (1) Return-to-launch landing site, (2) Land immediately at a nearby clear but unprepared site, (3) Land at a prepared landing site from the approximate footprint. Our presented algorithm shows a real-time auto-mitigation loop with multiple threads that run simultaneously to check controllability, reachability, and intermediate decisions to hold/ loiter or continue the flight plan as the landing strategy solution is being computed. Case study simulation is demonstrated with the safety-critical propulsion system and battery system and shows how different failure scenarios impact the landing strategy selection.

Autonomous Mitigation↗

NASA NTRSDate not supplied

Explaining Soft-Goal Conflicts through Constraint Relaxations

Recent work suggests to explain trade-offs between soft goals in terms of their conflicts, i. e., minimal unsolvable soft-goal subsets. But this does not explain the conflicts themselves: Why can a given set of soft-goals not be jointly achieved? Here we approach that question in terms of the underlying constraints on plans in the task at hand, namely resource availability and time windows. In this context, a natural form of explanation for a soft-goal conflict is a minimal constraint relaxation under which the conflict disappears (“if the deadline was 1 hour later, it would work”). We explore algorithms for computing such explanations. A baseline is to simply loop over all relaxed tasks and compute the conflicts for each separately. We improve over this by two algorithms that leverage information – conflicts, reachable states – across relaxed tasks. We show that these algorithms can exponentially outperform the baseline in theory, and we run experiments confirming that advantage in practice.

Planning↗

NASA NTRS2020

Navigation on the Line: Traversability Analysis and Path Planning for Extreme-Terrain Rappelling Rovers

Many areas of scientific interest in planetaryexploration, such as lunar pits, icy-moon crevasses, and Martiancraters, are inaccessible to current wheeled rovers. Rappellingrovers can safely traverse these steep surfaces, but requiretechniques to navigate their complex terrain. This dynamicnavigation is inherently time-critical and communication constraints(e.g. delays and small communication windows) willrequire planetary systems to have some autonomy.Autonomous navigation for Martian rovers is well studiedon moderately sloped and locally planar surfaces, but thesemethods do not readily transfer to tethered systems in nonplanar3D environments. Rappelling rovers in these situationshave additional challenges, including terrain-tether interactionand its effects on rover stability, path planning and control.This paper presents novel traversability analysis and pathplanning algorithms for rappelling rovers operating on steepterrains that account for terrain-tether interaction and theunique stability and reachability constraints of a rapellingsystem. The system is evaluated with a series of simulations andan analogue mission. In simulation, the planner was shown toreliably find safe paths down a 55 degree slope when a stabletether-terrain configuration exists and never recommended anunsafe path when one did not. In a planetary analogue mission,elements of the system were used to autonomously navigateAxel, a JPL rappelling rover, down a 30 degree slope with95% autonomy by distance travelled over 46 meters.

Nesnas, Issa↗

NASA NTRS2020

Model Checking as a Service: Towards Pragmatic Hidden Formal Methods

Executable models can be used to support all engineering activities in Model-Based Systems Engineering. Testing and simulation of such models can provide early feedback about design choices. How-ever, in today’s complex systems failures could arise due to subtle errors that are hard to find without checking all possible execution paths. Formal methods, and especially model checking can uncover such subtle errors, yet their usage in practice is limited due to the specialized expertise and high computing power required. There-fore we created an automated, cloud-based environment that can verify complex reachability properties on SysML State Machines using hidden model checkers. The approach and the prototype is illustrated using an example from the aerospace domain.

Karban, Robert↗

NASA NTRSDate not supplied

Photometric Correction of Hayabusa2's NIRS3 Spectra of Asteroid 162173 Ryugu Using Empirical Photometric Models

In 2018, Japanese Aerospace Exploration Agency’s spacecraft Hayabusa2 began a near infrared spectroscopic imaging survey of near-Earth asteroid 162173 Ryugu. Hayabusa2 is a successful sample-return mission with an overarching goal to provide a better understanding of the origin and evolution of our solar system. The target, Ryugu, is a low-albedo carbonaceous asteroid (Cb-type) that is linked to carbonaceous chondrite meteorites. It is thought to have originated in the main asteroid belt and migrated inward to become a near-Earth asteroid, and therefore reachable by spacecraft. Preliminary findings from processed NIRS3data have shown that hydroxyl-bearing minerals are present on the surface of Ryugu, and it is likely the result of impact fragments from an aqueously altered parent body.In this project, we have used newly calibrated and processed NIRS3 data using updated shape models of Ryugu. NIRS3 spectra have both a thermal and reflectance component. The thermal component (beyond 2.5 microns) was modeled and removed from all NIRS3 spectra. Ryugu spectra were taken at different viewing geometries, and a photometric model needed to be developed to normalize all of the spectra at a common geometry for each mission phase. We have used three empirical models: Minnaert, Lommel-Seeliger, and ROLO (RObotic LunarOrbiter). These models were chosen for their ability to relate the surface reflectance to the viewing geometry, as well as their compatibility with the asteroid’s albedo range. The three models provide the global light scattering properties of Ryugu’s surface and subsequently enable us to calculate the geometric albedo, phase integral, spherical bond albedo, and the average surface normal albedo for Ryugu.

Lucille Grace Williamson↗

NASA NTRS2021

Mars 2020 Perseverance Edl Gnc Safe Target Selection Reconstruction

On February 18th, 2021, NASA landed Perseverance on the Jezero crater (on Mars) using a new Terrain Relative Navigation (TRN) capability. TRN is com-prised of a new sensor, the Lander Vision System (LVS), and a new GNC algo-rithm, the Safe Target Selection (STS). LVS localized the descent vehicle with respect to a map. STS selected the safe landing target within a reachable region from the on-board Safe Targets Map (STM). The landing target was then handed to the Mars Science Laboratory (MSL) heritage powered descent GNC system to execute the landing. This paper describes the design and the as-flown performance of the STS algorithm.

Dutta, Soumyo↗

NASA NTRS2023

Study of Pairwise Deconfliction Metrics to Analyze Air Traffic Complexity in Upper Class E Airspace

Upper Class E Traffic Management (ETM) is envisioned to cooperatively facilitate operations of a diverse set of aerial vehicles, such as high-altitude long-endurance fixed-wing unmanned aircraft (low-speed and high-speed), high-altitude platforms, airships, stratospheric balloons, supersonic unmanned and commercial aircraft, etc., with a wide variety of mission types, performance characteristics, communication, navigation and surveillance capabilities, maneuverability, and on-board avionics in the National Airspace System (NAS) ’above’ 60,000 feet above mean sea level, without an active and direct control from human air traffic controllers. A diverse mixture of aerial vehicle types creates significant challenges in understanding air traffic complexity, which may not correlate strongly with air traffic density. One key step for determining air traffic complexity in upper class E airspace is to first understand pairwise deconfliction metrics such as reachability, reserve area, and reserve flight time for each pair of unique aerial vehicle types under potential conflict. Therefore, pairwise deconfliction metrics are first defined, and analytical equations are derived for conflict resolution using the heading change maneuver. Next, case studies are performed to analyze deconfliction metrics to avoid secondary conflicts in upper class E airspace. The study shows that pairwise deconfliction metrics are functions of maneuverability, performance characteristics, uncertainty in position and velocity, heading angle change, and conflict angle of aerial vehicles. The next step for this research is to build a mathematical model for air traffic complexity using pairwise deconfliction metrics and validate it in an upper Class E simulation environment.

Airspace Complexity↗

NASA NTRS2022

Mars 2020 Lander Vision System Flight Performance 1

The Mars 2020 Entry Descent and Landing (EDL) system delivered the Perseverance rover to the surface of Mars on February 18th, 2021. A large fraction of the Jezero Crater landing site was covered with landing hazards including cliffs, inescapable dune fields and rocks. These hazards were identified or inferred using orbital imagery before launch so that they could be avoided using Terrain Relative Navigation (TRN) which was composed of two parts: the Lander Vision System (LVS) and Safe Target Selection (STS). During EDL, the LVS successfully estimated map relative position by fusing landmarks matched between descent imagery and a map of the landing site with Inertial Measurement Unit (IMU) data. This position estimate was used by STS to identify the safest target for landing that was also reachable given fuel and other constraints. The EDL system then used the powered descent phase to retarget to this location and land safely. The overall error between the targeted location and actual landing location was 5m which was an order of magnitude less than the 60m touchdown error requirement. This paper will describe the final tests of the LVS before launch, the checkout of the LVS during operations and the LVS performance during EDL.

Zheng, Jason↗

NASA NTRSDate not supplied

Study of Pairwise Deconfliction Metrics to Analyze Air Traffic Complexity in Upper Class E Airspace

Airspace Complexity↗

NASA NTRSDate not supplied

A Temporal Differential Dynamic Logic Formal Embedding

Differential dynamic logic is a formal framework to specify and reason about hybrid programs (HPs). The core of dL is a proof calculus that contains a collection of axioms and rules for the rigorous verification of properties of HPs. Recently, dL has been embedded within the theorem prover Prototype Verification System (PVS) resulting in the tool Plaidypvs2. The integration of dL into PVS expands its expressive power; user defined functions, such as trigonometric and other transcendental functions, can be used inside the dL framework, and meta-reasoning about HPs can be performed, including reasoning about entire classes of HPs, specified using dependent types in PVS. The differential temporal dynamic logic (dTL2) extends dL with temporal logic operators to reason about all the states reachable during the execution of an HP. This paper presents a work in progress focusing on embedding dTL2 in PVS as an extension of Plaidypvs. Plaidypvs is expanded with the formalization of a trace semantics for HPs, the definition of the LTL temporal operators eventually and globally, and the implementation of the proof calculus for dTL2. This new embedding has the same capabilties as Plaidypvs, which allows user defined functions and meta-reasoning of properties of HPs. To the best of the authors’ knowledge this is the first implementation of dTL2.

differential dynamic logic↗

NASA NTRSDate not supplied

BurstCube: Behind the Scenes of a Do-No-Harm I&T Production

BurstCube is one of the most recent 6-U CubeSats built and developed by NASA Goddard Spaceflight Center (GSFC). As an astrophysics mission, BurstCube will be a rapid detection alert end-to-end mission system for short astrophysical gamma-ray bursts with the aim of increasing the chance of coincident detection of gamma-ray bursts. In addition, the mission is intended to augment the current fleet of gamma-ray astronomy satellites. The payload instrument includes 4 scintillator heads read out by arrays of silicon photomultipliers which will detect short astrophysical gamma-ray bursts. BurstCube provides a high field of view previously unavailable to larger missions and is intended to provide rapid alerts for follow up observations with other assets, increasing the chance of a coincident detection of an event. From the design to the integration and test phases, the project aimed to provide realistic test plans and stimulus to help verify and validate reachable areas of this innovative payload/instrument system and even spacecraft performance. Typical robust integration and test phases for space missions are unaligned with the budget and risk postures of small satellite or CubeSat missions, often designated as “Do No Harm” projects where the primary requirement is not harming the host platform or other payloads. Despite this status, CubeSats are complex missions that mix new and prior technologies. Integration and test for these missions requires responsible engineering, creative collaboration, and careful observation to deliver a reliable mission. This paper will provide an overview of the payload instrument and mission system, areas of injected automation (current and future), environmental testing results, and lessons learned during the integration and test phase.

CubeSats↗

DOE OSTI2023

A Data Processing Pipeline for Adversarial Socio-Technical Network Analysis

With the rapid adoption of emerging technologies, there is a need to catalog and model sociotechnical interdependencies that have been historically used to influence the operation of Critical Infrastructure networks including the impacts of mergers and acquisitions, hostile takeovers, and foreign investment. Our research intends to address this need with two primary contributions. First, we have developed a data curation and processing pipeline to generate sociotechnical networks extracted from a variety of data sources including SEC filings and infrastructure asset databases. The pipeline, implemented in Apache Airflow, extracts and normalizes the representation of entities and relations, specified within ontologies. Our intent is to provide an extensible, machine-actionable approach to quickly communicate such models, reproduce previous results, and adapt them to new, unanticipated situations. Second, networks produced by our pipeline enable the development of graph-theoretic metrics that consider the properties of network components in addition to its topology. Metadata associated with network components---whether semantic, temporal, or geospatial---affects the alignment of generated networks with assumptions underlying complexity metrics. Validation of generated networks relative to component types defined by an ontology, may allow the research community to adapt metrics to the semantics of the domains being studied. Generated networks may be processed as knowledge, dynamic, or spatial graphs and enables a variety of analyses including automated reasoning and measures of network complexity. Automated reasoning views extracted entities and relations as a knowledge graph; this enables application of inference rules that represent historically-attested adversarial business methods and applies that behavior to a specific geographic context. Measures of network complexity, including degree distribution, reachability analyses, temporal analysis, and community detection can be adapted to indicate adversarial organizational influence.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

MFANS 2024 - Formally Proving Characteristics of Cyber-Physical Systems

Cyber-physical systems (CPS) are engineered systems that rely on the smooth integration of computational algorithms and physical elements. This integration presents new challenges for verifying that systems will behave as expected. The goal of this presentation is to present current challenges and potential solutions for the formal verification of cyber-physical systems. For cyber systems, formal methods refer to systematically rigorous mathematical techniques employed in the specification, development, analysis, and verification of both software and hardware systems. Recent advancements in computer science have yielded sophisticated tools specifically designed to address challenges associated with formal methods in complex systems. These tools leverage various foundational concepts such as logic, formal languages, program semantics, type systems, type theory, and automata theory. A notable achievement in the application of formal methods is the seL4 microkernel, claimed to be the first general-purpose operating-system kernel to be verified. Its proof implies the absence of bugs and guarantees that the kernel meets specifications. For physical systems, dynamic and control theory has a history of using rigorous analytic techniques to prove functional correctness. Lyapunov, optimal, classical, modern, and robust control theories all provide rigorous mathematical methods both to analyze system performance and to design controller that can be guaranteed to meet certain objectives. Recent computational techniques like level set theory and reachability analysis provide assertions that a system's state will avoid unsafe regions. Even though success has been independently achieved for cyber systems and physical systems, the integration of such systems creates new challenges. In particular, there is an obvious discrepancy between finite-state machines and infinite-state systems, resulting in different approaches for modeling and analyzing these system. While it is possible to simulate hybrid systems, this provides only a demonstration of a performance and not proof. For hybrid systems, current formal methods and system analysis approaches typically require a workarounds to work on hybrid systems like CPS. This paper will outline the state of the art and limits of current practice for formally verifying CPS and will identify possible research directions that require attention.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗

DOE OSTI2025

Safe Physics-Informed Machine Learning for Dynamics and Control

This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learning techniques enhance the modeling and control of complex dynamical systems, ensuring safety and stability remains a critical challenge, especially in safety-critical applications like autonomous vehicles, robotics, medical decision-making, and energy systems. We explore various approaches for embedding and ensuring safety constraints, including structural priors, Lyapunov and Control Barrier Functions, predictive control, projections, and robust optimization techniques. Additionally, we delve into methods for uncertainty quantification and safety verification, including reachability analysis and neural network verification tools, which help validate that control policies remain within safe operating bounds even in uncertain environments. The paper includes illustrative examples demonstrating the implementation aspects of safe learning frameworks that combine the strengths of data-driven approaches with the rigor of physical principles, offering a path toward the safe control of complex dynamical systems.

Drgona, Jan↗