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DOE OSTI2025

Governance and Resilience: A Holistic Approach to Systems Security in Complex and Chaotic Environments

Here, a systems governance approach emphasizes a holistic perspective that identifies and navigates the interdependencies and conflicts between security and operational needs. Governance is defined as a collection of metasystems that provide the necessary constraints and processes to support, steer, adapt, transform, and sustain a system (Keating et al. 2022). Utilizing the Cynefin framework, which distinguishes between simple, complicated, complex, and chaotic environments (Snowden and Boone 2007), the article highlights the challenges faced by nuclear power plants in predatory contexts and the importance of integrating security objectives into governance frameworks. By incorporating security as a fundamental aspect of governance, the article underscores its significance for persistence, adaptation, and transformation in the face of uncertainty. Additionally, it introduces key heuristics of systems security, such as the importance of context, knowledge‐based decision‐making, and organization‐specific sociological factors (Williams and Caskey 2024). Ultimately, this work provides valuable insights into enhancing resilient operations in complex environments by reinforcing the connection between effective governance and security in systems engineering.

Caskey, Susan A. [Sandia National Laboratories (SN↗

NASA NTRS1990

Compiling knowledge-based systems from KEE to Ada

The dominant technology for developing AI applications is to work in a multi-mechanism, integrated, knowledge-based system (KBS) development environment. Unfortunately, systems developed in such environments are inappropriate for delivering many applications - most importantly, they carry the baggage of the entire Lisp environment and are not written in conventional languages. One resolution of this problem would be to compile applications from complex environments to conventional languages. Here the first efforts to develop a system for compiling KBS developed in KEE to Ada (trademark). This system is called KATYDID, for KEE/Ada Translation Yields Development Into Delivery. KATYDID includes early prototypes of a run-time KEE core (object-structure) library module for Ada, and translation mechanisms for knowledge structures, rules, and Lisp code to Ada. Using these tools, part of a simple expert system was compiled (not quite automatically) to run in a purely Ada environment. This experience has given us various insights on Ada as an artificial intelligence programming language, potential solutions of some of the engineering difficulties encountered in early work, and inspiration on future system development.

Filman, Robert E.↗

NASA NTRSDate not supplied

A Predictive GNSS Performance Monitor for Autonomous Air Vehicles in Urban Environments

The emergence and development of advanced technologies and vehicle types has created a growing demand for the introduction of new forms of flight operations. These new and increasingly complex operational paradigms such as Advanced and Urban Air Mobility (AAM/UAM) present regulatory authorities and the aviation community with several design and implementation challenges – particularly for highly autonomous vehicles. An overarching and daunting task is finding methods to integrate these emerging operations without compromising safety or disrupting traditional airspace operations. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. This paper focus on the development and testing of a prognostic service aimed at estimating the quality of Global Navigation Satellite System (GNSS) performance for an autonomous aircraft in complex environments. The intent of this function is to proactively reduce a flight operations risk of exposure to states that may induce poor or unacceptable navigation system performance by factoring in estimates of GNSS quality into pre-flight and/or in-flight route planning. Methodologies for producing quality estimates are specified and results are provided for selected simulation and flight test cases.

GNSS↗

NASA NTRS2016

Human-Robot Interaction in High Vulnerability Domains

Future NASA missions will require successful integration of the human with highly complex systems. Highly complex systems are likely to involve humans, automation, and some level of robotic assistance. The complex environments will require successful integration of the human with automation, with robots, and with human-automation-robot teams to accomplish mission critical goals. Many challenges exist for the human performing in these types of operational environments with these kinds of systems. Systems must be designed to optimally integrate various levels of inputs and outputs based on the roles and responsibilities of the human, the automation, and the robots; from direct manual control, shared human-robotic control, or no active human control (i.e. human supervisory control). It is assumed that the human will remain involved at some level. Technologies that vary based on contextual demands and on operator characteristics (workload, situation awareness) will be needed when the human integrates into these systems. Predictive models that estimate the impact of the technologies on the system performance and the on the human operator are also needed to meet the challenges associated with such future complex human-automation-robot systems in extreme environments.

workload↗

NASA NTRSDate not supplied

Comparison of Visual and LiDAR SLAM Algorithms using NASA Flight Test Data

Simultaneous Localization and Mapping (SLAM) is a promising technique that provides localization information and precise mapping of the physical environment without having much prior knowledge of the surroundings. SLAM may have a vital role in aeronautics and aerospace, where vehicles and aircraft must operate in complex environments with traditional localization services that may be degraded or unavailable. This paper compares several pre-canned 3D SLAM algorithms based on vision and LiDAR, namely ORB-SLAM, ORB-SLAM2, LOAM, A-LOAM, and F-LOAM on NASA UAS (Unmanned Aircraft System) flight test data. The NASA ARC UAS flight test demonstrates preliminary SLAM algorithm results, which serve as a stepping stone to simulated AAM (Advanced Air Mobility) concepts. Conducting AFRC UAS flight test for simulated AAM approach and landing with SLAM algorithms provides an Alternative Precision Navigation and Timing solution based on distributed landmarks and fiducials in the landing zone. These algorithms use the telemetry data as ground truth for a baseline comparison. The criteria of the performance comparison include robustness, accuracy, re-localization, response to environmental changes, and real-time effectiveness, which are currently qualitative but to be quantitative in the future.

computer vision↗

NASA NTRSDate not supplied

Comparison of Visual and LiDAR SLAM Algorithms using NASA Flight Test Data

computer vision↗

NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Reinforcement Learning

To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. The goal of reinforcement learning is for an agent to learn a policy (task strategy) through interactions with an environment. When the agent performs an action, a change occurs in environment state and a numerical reward is received which informs the agent whether the action performed was good or not. Since reinforcement learning algorithms learn through trial-and-error, a simulation is a desirable first environment for development and learning. We developed two simulations, the first is a 2D excavation simulation developed to facilitate parameter selection, and a 3D simulation developed using a game physics engine, to simulate simplified soil interactions and increase the fidelity of the dynamic models of the robotic agents. The development of this 3D simulation has enabled the training of additional sensing capabilities and research both at the granular mechanics and operations levels. We experimented with various virtual sensor payloads to identify a combination that enabled efficient excavation operation and learning. Our reward function is based on how much material is excavated per step. A penalty is also received for leaving the dig site and to smooth the acceleration of the drum arms. We implemented pseudo time-of-flight sensors to report distance from each drum to ground and the height above ground which was found to be more efficient than existing solutions. Our findings suggest that reinforcement learning for autonomous operations has learned viable trenching strategies within 3000 training episodes in our simplified 2D environment and helped identify desirable sensing capabilities, arrangements, and considerations such as the positioning of time-of-flight sensors. Future work includes expanding our simulation to more complex environments and scenarios, and transfer learning from simulation to RASSOR 2.0 hardware for deployment in the Regolith Test Bin at NASA's Kennedy Space Center.

rassor↗

NASA NTRS1989

HERMIES-3: A step toward autonomous mobility, manipulation, and perception

HERMIES-III is an autonomous robot comprised of a seven degree-of-freedom (DOF) manipulator designed for human scale tasks, a laser range finder, a sonar array, an omni-directional wheel-driven chassis, multiple cameras, and a dual computer system containing a 16-node hypercube expandable to 128 nodes. The current experimental program involves performance of human-scale tasks (e.g., valve manipulation, use of tools), integration of a dexterous manipulator and platform motion in geometrically complex environments, and effective use of multiple cooperating robots (HERMIES-IIB and HERMIES-III). The environment in which the robots operate has been designed to include multiple valves, pipes, meters, obstacles on the floor, valves occluded from view, and multiple paths of differing navigation complexity. The ongoing research program supports the development of autonomous capability for HERMIES-IIB and III to perform complex navigation and manipulation under time constraints, while dealing with imprecise sensory information.

Weisbin, C. R.↗

DOE OSTI2021

Distributionally Safe Path Planning: Wasserstein Safe RRT

In this paper, we propose a Wasserstein metric-based random path planning algorithm. Wasserstein Safe RRT (W-Safe RRT) provides finite-sample probabilistic guarantees on the safety of a returned path in an uncertain obstacle environment. Vehicle and obstacle states are modeled as distributions based upon state and model observations. Additionally, we define limits on distributional sampling error so the Wasserstein distance between a vehicle state distribution and obstacle distributions can be bounded. This enables the algorithm to return safe paths with a confidence bound through combining finite sampling error bounds with calculations of the Wasserstein distance between discrete distributions. W-Safe RRT is compared against a baseline minimum encompassing ball algorithm, which ensures balls that minimally encompass discrete state and obstacle distributions do not overlap. The improved performance is verified in a 3D environment using single, multi, and rotating non-convex obstacle cases, with and without forced obstacle error in adversarial directions, showing that W-Safe RRT can handle poorly modeled complex environments.

42 ENGINEERING↗

DOE OSTI2025

Probing exciton diffusion dynamics in photosynthetic supercomplexes via exciton–exciton annihilation

Photosynthesis converts solar energy into chemical energy through coordinated energy transfer between light-harvesting complexes and reaction centers (RCs). Understanding exciton motion, particularly the exciton diffusion length, is essential for optimizing energy efficiency in photosystems. In this work, we combine intensity-cycling transient absorption spectroscopy with kinetic Monte Carlo (kMC) simulation to investigate exciton motion in the C2S2 photosystem II supercomplex of spinach. Using exciton–exciton annihilation, revealed in the fifth-order response, we experimentally estimate an exciton diffusion length of 10.9 nm based on a 3D normal diffusion model, suggesting the ability of excitons to traverse the supercomplex. However, kMC simulations reveal that exciton motion is sub-diffusive because of spatial constraints and the strong RC traps. An anomalous diffusion model analysis of the experimental data yields a diffusion length of 9.7 nm, while the simulated diffusion length is 7.4 nm. The variable exciton residence time across subunits, partly influenced by their connectivity to the trap, indicates inhomogeneous annihilation probability and suggests how plants balance efficient light harvesting with photoprotection. We also explore the influence of specific assumptions in the annihilation simulation, which are challenging to access in more complex environments, such as the thylakoid membrane. Our study provides a framework for studying exciton dynamics using exciton–exciton annihilation, which can be extended to understand the light-harvesting efficiencies of larger, more complex photosynthetic assemblies.

Zhang, Kunyan (ORCID:000000026830409X)↗

DOE OSTI2021

Aging power spectrum of membrane protein transport and other subordinated random walks

Single-particle tracking offers detailed information about the motion of molecules in complex environments such as those encountered in live cells, but the interpretation of experimental data is challenging. One of the most powerful tools in the characterization of random processes is the power spectral density. However, because anomalous diffusion processes in complex systems are usually not stationary, the traditional Wiener-Khinchin theorem for the analysis of power spectral densities is invalid. Here, we employ a recently developed tool named aging Wiener-Khinchin theorem to derive the power spectral density of fractional Brownian motion coexisting with a scale-free continuous time random walk, the two most typical anomalous diffusion processes. Using this analysis, we characterize the motion of voltage-gated sodium channels on the surface of hippocampal neurons. Our results show aging where the power spectral density can either increase or decrease with observation time depending on the specific parameters of both underlying processes.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2021

Applying the Cognitive Space Gateway to Swarm Topologies

NASA's future vision for interplanetary networking includes a lunar network, Cube Satellite (CubeSat) constellations, and deep space robotic missions, comprising what could be viewed as a network of networks. Delay-tolerant networking (DTN) architecture and protocols provide a standard network layer among these varying scenarios and mitigate many challenges of the space environment, such as long delays, unplanned service interruptions, and asymmetric links. The Cognitive Space Gateway (CSG) is a routing method in a DTN architecture that uses spiking neural networks as the learning element to optimize routing decisions in a complex environment. This work aims to further develop cognitive networking technologies in several critical areas, including DTN, the CSG algorithm, CubeSat swarm topologies, and cloud services. To test the algorithm in a realistic scenario, the emulated network topology is based on a CubeSat swarm. The swarm may function as a mesh of nodes or as a hub-and-spoke network. An emulation environment will be built upon a commercial cloud service, such as Amazon Web Services (AWS) Elastic Compute Cloud. The cloud environment may enable a flexible, lower maintenance approach versus a multi-hop network based in a physical laboratory. The cloud platform will provide a secure environment allowing for collaboration among government and academic entities.

Ricardo Lent↗

NASA NTRSDate not supplied

Applying the Cognitive Space Gateway to Swarm Topologies

NASA’s future vision for interplanetary networking includes a lunar network, Cube Satellite (CubeSat) constellations, and deep space robotic missions, comprising what could be viewed as a network of networks. Delay-tolerant networking (DTN) architecture and protocols provide a standard network layer among these varying scenarios and mitigate many challenges of the space environment, such as long delays, unplanned service interruptions, and asymmetric links. The Cognitive Space Gateway (CSG) is a routing method in a DTN architecture that uses spiking neural networks as the learning element to optimize outing decisions in a complex environment. This work aims to further develop cognitive networking technologies in several critical areas, including DTN, the CSG algorithm, SmallSat swarm topologies, and cloud services. The CSG algorithm is tested in a realistic scenario in which the emulated network topology is based on a SmallSat swarm. The emulation environment will be built upon a commercial cloud service, such as Amazon Web Services (AWS) Elastic Compute Cloud. This work investigates the ability of such a platform to enable a flexible, lower maintenance approach to creating a multihop network outside of a physical laboratory. The cloud platform will provide a secure environment allowing for collaboration among government and academic entities.

Ricardo Lent↗

DOE OSTI2021

Tailored computational approaches to interrogate heavy element chemistry and structure in condensed phase

In this chapter we are presenting a brief review of the challenges encountered in the study of 4f and 5f block elements in the condensed phase. Their recovery, use in molten salt reactors and other interesting applications necessitate the use of molecular dynamics and large-scale models that take into account both the electronic structure and relativistic corrections. Sampling the multitude of electronic and atomic configurational states is at the heart of reliable predictions of structure, reactivity, dynamics and transport of heavy metals in complex environments. We present three examples that combine lanthanide elements with large scale models: i) our recent developments of a versatile adaptive learning method that enables global optimization in high dimensional spaces, ii) results of computed pKa values of lanthanide aqua complexes, and iii) structure and computed EXAFS of heavy elements in molten salts. The latter two employ our recently-optimized lanthanide pseudo-potentials and companion basis sets for condensed phase ab initio molecular dynamics.

Nguyen, Manh Thuong↗

DOE OSTI2021

Complex Formation and Dissociation Dynamics on Amorphous Silica Surfaces

Benzene complex formation and dissociation dynamics with silanols on the amorphous silica surfaces of nanoporous SiO 2 , from a benzene/carbon tetrachloride solution, were measured by the growth of off-diagonal peaks in the two dimensional infrared (2D IR) chemical exchange spectrum of the isolated Si-OD stretch. The presence of two types of isolated silanols, referred to as type I and II, was revealed, with dissociation time constants of 82 ps and 4.0 ps for type I and II complexes, respectively. The type I silanols are associated with the main IR absorption feature in the Si-OD stretching region, while the type II silanols give rise to a broader shoulder to lower frequency. Polarization selective pump-probe (PSPP) measurements provided the vibrational lifetimes and orientational relaxation rates of the two silanols in the CCl 4 (free) and benzene (complex) environments. The type II silanols constitute roughly 30% of the isolated silanol population and exhibit a substantially faster rate of vibrational relaxation, making the type I dynamics the dominant contribution to the PSPP and 2D IR signals. From the measured dissociation times, the enthalpies of formation for the two surface complexes were obtained; the formation of the type I complex being significantly more exothermic. Furthermore, as the type II site is preferentially removed from the amorphous silica surface with increasing activation temperature, the results provide a reasonable explanation for the increased exothermicity of benzene adsorption on silica with increasing activation temperature in previous calorimetry experiments.

14 SOLAR ENERGY↗

NASA NTRS2024

Looking to the Future: A Call to Action for Advanced GNC Algorithm Verification and Validation

Future space systems will rely on autonomous Guidance, Navigation, and Control (GNC) functions to efficiently manage safe and precise self-directed operations in uncertain complex environments. Fundamentally, the GNC system plays a key role in mission performance and safety because it computes the ideal trajectory (Guidance), determines the actual trajectory (Navigation), and executes the ideal trajectory (Control) of a vehicle’s position and attitude. Our current GNC systems are highly automated and already have a high degree of complexity. As missions become more ambitious, GNC systems for launch vehicles and space platforms (e.g., spacecraft, probes, and landers) will require higher levels of performance and autonomous operation than previously encountered, for example, this includes GNC for optimizing aerodynamic and/or propulsion performance during planetary entry. This GNC Verification and Validation (V&V) paper highlights concerns with what undoubtedly will be a trend towards increased complexity as fully autonomous GNC systems are developed for future space missions. Clearly, complex GNC systems pose challenges in the prelaunch V&V phase, which is a relatively expensive part of a mission’s life cycle. Essentially the V&V phase is focused on checking that the system effectively meets all the design and operational requirements for the mission. The authors of this paper (i.e., the Inter-Agency Working Group of GNC subject matter experts) focused on this fundamental question over the past few years: Will the GNC engineering community of practice be sufficiently prepared to perform the necessary V&V on evolving GNC architectures that are driven by very demanding requirements for autonomy, resiliency, reconfigurability, adaptability, and mission cost-benefit balance? It is the viewpoint of our Inter-Agency team that the GNC V&V approaches and processes needed to address the next generation of complex GNC systems, which likely will employ various forms of modern GNC technology, are not currently established to the level the community will need in the future. While researchers and practitioners have made some progress in developing new GNC V&V methods for modern GNC systems, a good deal of work remains to be done to codify such methods in a comprehensive and systematic manner. Thus, the Inter-Agency team’s partner organizations [the National Aeronautics and Space Administration (NASA), the European Space Agency (ESA), the National Centre for Space Studies (CNES), the German Aerospace Center (DLR), the French Aerospace Lab (ONERA), and ISAE-SUPAERO] have conducted preliminary investigations into advancing GNC V&V techniques, which resulted in the identification of the need for education, new V&V tools, and benchmark problems for the GNC community. The necessary proactive steps to be taken to meet the challenges and fill the gaps in GNC V&V are summarized in this paper. The first steps include identifying advanced analysis tools, developing a GNC V&V roadmap, and expanding education and training programs for GNC practitioners. This paper is a call to action and proposes a comprehensive set of recommended actions for all our stakeholders: space agencies, researchers, and industry.

Samir Bennani↗

DOE OSTI2021

Influence of non-adiabatic effects on linear absorption spectra in the condensed phase: Methylene blue

Modeling linear absorption spectra of solvated chromophores is highly challenging as contributions are present both from coupling of the electronic states to nuclear vibrations and from solute–solvent interactions. In systems where excited states intersect in the Condon region, significant non-adiabatic contributions to absorption line shapes can also be observed. Here, we introduce a robust approach to model linear absorption spectra accounting for both environmental and non-adiabatic effects from first principles. This model parameterizes a linear vibronic coupling (LVC) Hamiltonian directly from energy gap fluctuations calculated along molecular dynamics (MD) trajectories of the chromophore in solution, accounting for both anharmonicity in the potential and direct solute–solvent interactions. The resulting system dynamics described by the LVC Hamiltonian are solved exactly using the thermalized time-evolving density operator with orthogonal polynomials algorithm (T-TEDOPA). The approach is applied to the linear absorption spectrum of methylene blue in water. We show that the strong shoulder in the experimental spectrum is caused by vibrationally driven population transfer between the bright S1 and the dark S2 states. The treatment of the solvent environment is one of many factors that strongly influence the population transfer and line shape; accurate modeling can only be achieved through the use of explicit quantum mechanical solvation. The efficiency of T-TEDOPA, combined with LVC Hamiltonian parameterizations from MD, leads to an attractive method for describing a large variety of systems in complex environments from first principles.

Dunnett, Angus J. (ORCID:0000000285797826)↗

NASA NTRS2019

Advancing Aircraft Operations in a Net-Centric Environment with the Incorporation of Increasingly Autonomous Systems and Human Teaming

NextGen has begun the modernization of the nation’s air transportation system, with goals to improve system safety, increase operation efficiency and capacity, provide enhanced predictability, resilience and robustness. With these improvements, NextGen is poised to handle significant increases in air traffic operations, more than twice the number recorded in 2016, by 2025.1 NextGen is evolving toward collaborative decision-making across many agents, including automation, by use of a Net-Centric architecture, which in itself creates a very complex environment in which the navigation and operation of aircraft are to take place. An intricate environment such as this, coupled with the expected upsurge of air traffic operations generates concern respecting the ability of the human-agent to both fly and manage aircraft within. Therefore, it is both necessary and practical to begin the process of increasingly autonomous systems within the cockpit that will act independently to assist the human-agent achieve the overall goal of NextGen. However, the straightforward technological development and implementation of intelligent machines into the cockpit is only part of what is necessary to maintain, at minimum, or improve human-agent functionality, as desired, while operating in NextGen. The full integration of Increasingly Autonomous Systems (IAS) within the cockpit can only be accomplished when the IAS works in concert with the human, formulating trust between the two, thereby establishing a team atmosphere. Imperative to cockpit implementation is ensuring the proper performance of the IAS by the development team and the human-agent with which it will be paired when given a specific piloting, navigation, or observational task. Described in this paper are the steps taken, at NASA Langley Research Center, during the second and third phases of the development of an IAS, the Traffic Data Manager (TDM), its verification and validation by human-agents, and the foundational development of Human Autonomy Teaming (HAT) between the two.

Houston, Vincent E.↗