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At least 739 records · Page 41

Exploring Large Language Model Agents in Cybersecurity: A Literature Review with Experiments

The accelerated development and integration of large language model (LLM) agents have led researchers and developers to explore their effectiveness in cybersecurity, specifically with penetration testing (pentesting). Recent research efforts have attempted to use LLM agents to automate the process of pentesting because of the cost and time requirements that are required to perform a manual review. However, not all of the tools perform as expected. This paper reviews some of the newest and most popular autonomous pentesting frameworks, highlighting the capabilities and limitations of each one with the goal of providing the components needed to successfully and effectively build an autonomous pentesting agent in the future.

97 MATHEMATICS AND COMPUTING↗

Precise Image-Based Motion Estimation for Autonomous Small Body Exploration

We have developed and tested a software algorithm that enables onboard autonomous motion estimation near small bodies using descent camera imagery and laser altimetry. Through simulation and testing, we have shown that visual feature tracking can decrease uncertainty in spacecraft motion to a level that makes landing on small, irregularly shaped, bodies feasible. Possible future work will include qualification of the algorithm as a flight experiment for the Deep Space 4/Champollion comet lander mission currently under study at the Jet Propulsion Laboratory.

Johnson, Andrew Edie↗

GPS-Based Navigation And Orbit Determination for the AMSAT AO-40 Satellite

The AMSAT OSCAR-40 (AO-40) spacecraft occupies a highly elliptical orbit (HEO) to support amateur radio experiments. An interesting aspect of the mission is the attempted use of GPS for navigation and attitude determination in HEO. Previous experiences with GPS tracking in such orbits have demonstrated the ability to acquire GPS signals, but very little data were produced for navigation and orbit determination studies. The AO-40 spacecraft, flying two Trimble Advanced Navigation Sensor (TANS) Vector GPS receivers for signal reception at apogee and at perigee, is the first to demonstrate autonomous tracking of GPS signals from within a HEO with no interaction from ground controllers. Moreover, over 11 weeks of total operations as of June 2002, the receiver has returned a continuous stream of code phase, Doppler, and carrier phase measurements useful for studying GPS signal characteristics and performing post-processed orbit determination studies in HEO. This paper presents the initial efforts to generate AO-40 navigation solutions from pseudorange data reconstructed from the TANS Vector code phase, as well as to generate a precise orbit solution for the AO-40 spacecraft using a batch filter.

Davis, George↗

Shear History Extensional Rheology Experiment: A Proposed ISS Experiment

The Shear History Extensional Rheology Experiment (SHERE) is a proposed International Space Station (ISS) glovebox experiment designed to study the effect of preshear on the transient evolution of the microstructure and viscoelastic tensile stresses for monodisperse dilute polymer solutions. Collectively referred to as Boger fluids, these polymer solutions have become a popular choice for rheological studies of non-Newtonian fluids and are the non-Newtonian fluid used in this experiment. The SHERE hardware consists of the Rheometer, Camera Arm, Interface Box, Cabling, Keyboard, Tool Box, Fluid Modules, and Stowage Tray. Each component will be described in detail in this paper. In the area of space exploration, the development of in-situ fabrication and repair technology represents a critical element in evolution of autonomous exploration capability. SHERE has the capability to provide data for engineering design tools needed for polymer parts manufacturing systems to ensure their rheological properties have not been impacted in the variable gravity environment and this will be briefly addressed.

Hall, Nancy R.↗

Playbook for UAS: UX of Goal-Oriented Planning & Execution

We are evaluating Playbook for CASAS (Connected Autonomous Smart Aerospace Systems), a tool designed to aid first responders in disaster relief efforts. We are adapting an existing tool, Playbook, to support a future unmanned aircraft system (UAS) swarm demonstration. Playbook for CASAS will be used to plan, edit, and monitor simulated UAS swarms, and we are interested in evaluating the user experience of this prototype as well as developing recommendations for future UAS interfaces. Allocation of roles and responsibilities between human-automation systems is key to promoting productive cooperation between users and automation. Future interfaces, however, must allow for adaptive management of the swarm not a constant split in human-automation control. Our early research indicates that when a single pilot is controlling swarms of robotic agents, such as UAS or ground rovers, operators require a higher level, goal-based interface with usability at its core. Along with that high-level control, users can leverage sensors within the swarm to be notified when lower level actions must be taken by the pilot. First responders working in disaster relief efforts require a high level of situational awareness (SA) and precise control at key moments within a mission. This balance in operator workload paired with SA can lead to improved safety and mission outcomes. Our research below outlines leverage points as well as the balance between human involvement and autonomy in UAS interfaces.

UAS↗

Integrated System for Autonomous and Adaptive Caretaking (ISAAC) Simulated Cargo Logistics Demo Video

This video shows a simulation of autonomous cargo logistics using ISAAC (Integrated System for Autonomous and Adaptive Caretaking). The video shows autonomous planning and execution of a cargo scenario in which the R2 robot moves a cargo bag from a stowed location to a transfer location in the US Lab on the International Space Station. The Astrobee robot then moves the bag from the transfer location to a new temporary stowage location in the Japanese Experiment Module (JEM).

ISAAC↗

Spaceflight Autonomous Multigenerational Microbial Sequencer in Support of Plant-Growth Systems

The CubeSat platform has proven successful in obtaining meaningful life science information when biological payloads are incorporated. Examples include: 1) the first-ever CubeSat with a biological payload, GeneSat-1, which demonstrated decreased growth rates for flight samples of Escherichia coli in low Earth orbit (Parra et al. 2008); 2) PharmaSat, demonstrated that Saccharomyces cerevisiae in the microgravity environment exhibits a significant level of metabolic activity even at high doses of applied antifungal (Ricco et al. 2011); 3) O/OREOS, which used Bacillus subtilis(bacteria) to demonstrate for the first time that microorganisms can be loaded in a dried, dormant form and then rehydrated and grown in orbit months after launch (Nicholson et al. 2011; Ehrenfreund et al. 2014; 4) the SporeSat payload, which investigated Ceratopteris richardii(fern spores) using lab-on-a-chip devices (BioCDs) and minicentrifuges to produce artificial gravitational forces in ground studies (Park et al. 2017), with demonstration of the BioCD and minicentrifuge in space; 5) EcAMSat, the first CubeSat to be directly deployed from the ISS for an experiment assessing antibiotic resistance of E. coli in the microgravity environment (Padgen et al. 2020); 6) BioSentinel, exposed a culture of yeast to galactic cosmic radiation (GCR) and solar particle events while in heliocentric orbit to measure the rate of double-strand-break repair using DNA-repair-deficient mutants. This effort measures the metabolic parameters of yeast in a deep-space environment compared to Earth ambient conditions using a 3-color LED detection system (Ricco et al. 2020; Padgen et al. 2021). We aim to expand this list to include a Spaceflight Autonomous Multigenerational Microbial Sequencer (SAMMS). SAMMS will allow for the genome level understanding of changes in growth and metabolic activity for any organism. While microbes are suitable for early studies in our proposed platform because of their small size, small and relatively less-complicated genomes, fast generation times, and relevance to life support systems; multicellular organisms can similarly be evaluated for their genetic response to the spaceflight environment. The Spaceflight Autonomous Multigenerational Microbial Sequencer (SAMMS) will enable autonomous sequencing of biological samples in plant production units, cislunar orbit and on the lunar surface to examine spaceflight effects (ie. radiation, altered gravity, reduced pressures) on plant and microbial genomes.On this team a Kennedy Space Center (KSC) space crop production and water systems microbiologist/molecular biologist works with a Johnson Space Center (JSC) International Space Station (ISS) microbial sequencing expert and an Ames Research Center (ARC) CubeSat Engineering team to convert an automated Oxford Nanopore librarypreparation and sequencing method to a fluidic CubeSat payload system. The Oxford Nanopore MinION sequencing platform has proven successful in the spaceflight environment onboard the ISS (Stahl-Rommel et al. 2021). Further long-duration spaceflight and exposure to high levels of radiation will cause genotypic effects in biological organisms that may affect their function. Monitoring the adaption of a population to the spaceflight environment and any subsequent beneficial mutations will allow for the harnessing of organisms best suited for use in life support systems. This will ensure that the selected life support-essential microorganisms maintain their intended specified function over generations of culturing in the relevant spaceflight environment without becoming hazardous to crew or spacecraft systems.

Aubrie E Orourke↗

AutoTG: Reinforcement Learning-Based Symbolic Optimization for AI-Assisted Power Converter Design

Power converters are pervasive in modern electronic component design. They can be found in all electronic devices from household appliances and cellphone chargers to vehicles. Currently, designing new circuit topologies is hard because it requires human expertise based on experience and is difficult to automate. However, artificial-intelligence-assisted design can significantly facilitate the development of new power converters and/or improve the final result. Intelligently designed highly efficient power converters can have a significant effect on many important attributes, such as power efficiency, layout size, cost, heat dissemination, energy requirements, etc. We propose Autonomous Topology Generator (AutoTG), a reinforcement-learning-based framework that generates power converter topology candidates based on user specifications, optimized for user preferences. By modeling power converter design as a symbolic optimization problem, we sequentially sample components in an autoregressive manner until new topologies are formed, providing both the topology specification and the sizing (magnitude of each component parameter) of the proposed power converter. Here, we provide an empirical evaluation and show that AutoTG is able to generate varied high-efficiency topologies within component restrictions based on user input and show that previously unknown topologies can be found for further evaluation.

(AI)-based design↗

A global spacecraft control network for spacecraft autonomy research

The development and implementation of the Automated Space System Experimental Testbed (ASSET) space operations and control network, is reported on. This network will serve as a command and control architecture for spacecraft operations and will offer a real testbed for the application and validation of advanced autonomous spacecraft operations strategies. The proposed network will initially consist of globally distributed amateur radio ground stations at locations throughout North America and Europe. These stations will be linked via Internet to various control centers. The Stanford (CA) control center will be capable of human and computer based decision making for the coordination of user experiments, resource scheduling and fault management. The project's system architecture is described together with its proposed use as a command and control system, its value as a testbed for spacecraft autonomy research, and its current implementation.

Kitts, Christopher A.↗

Restoration of Apollo Data by the Lunar Data Project/PDS Lunar Data Node: An Update

The Apollo 11, 12, and 14 through 17 missions orbited and landed on the Moon, carrying scientific instruments that returned data from all phases of the missions, included long-lived Apollo Lunar Surface Experiments Packages (ALSEPs) deployed by the astronauts on the lunar surface. Much of these data were never archived, and some of the archived data were on media and in formats that are outmoded, or were deposited with little or no useful documentation to aid outside users. This is particularly true of the ALSEP data returned autonomously for many years after the Apollo missions ended. The purpose of the Lunar Data Project and the Planetary Data System (PDS) Lunar Data Node is to take data collections already archived at the NASA Space Science Data Coordinated Archive (NSSDCA) and prepare them for archiving through PDS, and to locate lunar data that were never archived, bring them into NSSDCA, and then archive them through PDS. Preparing these data for archiving involves reading the data from the original media, be it magnetic tape, microfilm, microfiche, or hard-copy document, converting the outmoded, often binary, formats when necessary, putting them into a standard digital form accepted by PDS, collecting the necessary ancillary data and documentation (metadata) to ensure that the data are usable and well-described, summarizing the metadata in documentation to be included in the data set, adding other information such as references, mission and instrument descriptions, contact information, and related documentation, and packaging the results in a PDS-compliant data set. The data set is then validated and reviewed by a group of external scientists as part of the PDS final archive process. We present a status report on some of the data sets that we are processing.

Lunar↗

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↗

Human-Automation Allocations for Current Robotic Space Operations

Within the Human Research Program, one risk delineates the uncertainty surrounding crew working with automation and robotics in spaceflight. The Risk of Inadequate Design of Human and Automation/Robotic Integration (HARI) is concerned with the detrimental effects on crew performance due to ineffective user interfaces, system designs and/or functional task allocation, potentially compromising mission success and safety. Risk arises because we have limited experience with complex automation and robotics. One key gap within HARI, is the gap related to functional allocation. The gap states: We need to evaluate, develop, and validate methods and guidelines for identifying human-automation/robot task information needs, function allocation, and team composition for future long duration, long distance space missions. Allocations determine the human-system performance as it identifies the functions and performance levels required by the automation/robotic system, and in turn, what work the crew is expected to perform and the necessary human performance requirements. Allocations must take into account each of the human, automation, and robotic systems capabilities and limitations. Some functions may be intuitively assigned to the human versus the robot, but to optimize efficiency and effectiveness, purposeful role assignments will be required. The role of automation and robotics will significantly change in future exploration missions, particularly as crew becomes more autonomous from ground controllers. Thus, we must understand the suitability of existing function allocation methods within NASA as well as the existing allocations established by the few robotic systems that are operational in spaceflight. In order to evaluate future methods of robotic allocations, we must first benchmark the allocations and allocation methods that have been used. We will present 1) documentation of human-automation-robotic allocations in existing, operational spaceflight systems; and 2) To gather existing lessons learned and best practices in these role assignments, from spaceflight operational experience of crew and ground teams that may be used to guide development for future systems. NASA and other space agencies have operational spaceflight experience with two key Human-Automation-Robotic (HAR) systems: heavy lift robotic arms and planetary robotic explorers. Additionally, NASA has invested in high-fidelity rover systems that can carry crew, building beyond Apollo's lunar rover. The heavy lift robotic arms reviewed are: Space Station Remote Manipulator System (SSRMS), Japanese Remote Manipulator System (JEMRMS), and the European Robotic Arm (ERA, designed but not deployed in space). The robotic rover systems reviewed are: Mars Exploration Rovers, Mars Science Laboratory rover, and the high-fidelity K10 rovers. Much of the design and operational feedback for these systems have been communicated to flight controllers and robotic design teams. As part of the mitigating the HARI risk for future human spaceflight operations, we must document function allocations between robots and humans that have worked well in practice.

robotic allocation↗

Navigating the Path to Autonomy: Real-World Lessons from an Air-Free Self-Driving Laboratory

While autonomous experimentation has promise to accelerate discovery in physcial sciences, the real-world integration of predictive models and experimentation is non-trivial. Here we describe the genesis of a self-driving laboratory (SDL) for air-sensitive chemistry at Argonne National Laboratory and demonstrate the experimental design considerations needed for high-throughput experiments before predictive models can lead to scientific discovery. Our SDL was designed to explore battery electrolyte stability. Our final SDL utilized plate readers in a glovebox with a nitrogen atmosphere to perform kinetic assays and screen hundreds of battery-relevant solvents. However, the roadmap to autonomy and airfree-friendly experimentation required the complex evaluation of several spectroscopic and chromatographic methods. The greatest experimental challenges were (a) developing long-term sampling methods that remained air-free; (b) accelerating kinetics to advance reactivity projections; and (c) ensuring labware compatibility with nonaqueous solvents used in battery chemistry. Our experiences highlight the practical gap between closed-loop aspirations and the realities of chemical discovery, offering lessons on the challenges of transferring every day laboratory workflows to autonomy. These results suggest a more realistic blueprint for autonomy in chemistry—one that balances thoughtful and realistic experimental formulation.

Robertson, Lily A.↗

Autonomous sputter synthesis of thin film nitrides with composition controlled by Bayesian optimization of optical plasma emission

Autonomous experimentation has emerged as an efficient approach to accelerate the pace of material discovery. Although instruments for autonomous synthesis have become popular in molecular and polymer science, solution processing of hybrid materials, and nanoparticles, examples of autonomous tools for physical vapor deposition are scarce yet important for the semiconductor industry. Here, we report the design and implementation of an autonomous workflow for sputter deposition of thin films with controlled composition, leveraging a highly automated sputtering reactor custom-controlled by Python, optical emission spectroscopy (OES), and a Bayesian optimization algorithm. We modeled film composition, measured by x-ray fluorescence, as a linear function of plasma emission lines monitored during co-sputtering from elemental Zn and Ti targets in an N 2 and Ar atmosphere. A Bayesian control algorithm, informed by OES, navigates the space of sputtering power to fabricate films with user-defined compositions by minimizing the absolute error between desired and measured optical emission signals. We validated our approach by autonomously fabricating Zn x Ti 1-x N y films that deviate from the targeted cation composition by a relative ±3.5%, even for 15 nm thin films, demonstrating that the proposed approach can reliably synthesize thin films with a specific composition and minimal human interference. Moreover, the proposed method can be extended to more difficult synthesis experiments where plasma intensity lines depend non-linearly on pressure, or the elemental sticking coefficients strongly depend on the substrate temperature.

36 MATERIALS SCIENCE↗

Skeletides: A Modular, Simplified Physical Model of Protein Secondary Structure

Three-dimensional (3D) models are essential for visualization and conceptual understanding of complex architectures such as protein structure. Although there is a plethora of software platforms that allow digital depictions of protein structure at an atomic level in silico, physical models are needed to convey an intuitive understanding of biomolecular architecture. However, it is a challenge to represent all the relevant features of proteins in a single physical model due to their sheer structural complexity. Here, we describe a modular protein model that focuses only on representation of the secondary structure—the underlying structural skeleton. The simplified model consists of amino acid units, which can be linked together to reproduce the two most fundamental structural features of protein secondary structure: the relative positions of the amino acid alpha carbon atoms, and the intra-main chain hydrogen bonding pattern. We use 3D printing and magnets to create a set of three modular amino acid building blocks, which when linked together into a chain, can faithfully represent alpha helices, beta sheets, and turns. These simple to make models can be used to quickly assemble the alpha carbon trace of an entire protein domain, which conveys a tactile experience of the complexity of protein skeletal architecture. These models highlight the modularity of protein structure: where a single structural unit, the amino acid, can be linked together to form a larger, regular secondary structure. These models also have a propensity to spontaneously organize into alpha helices and beta sheets, as demonstrated by their ability to autonomously assemble when placed in a circulating water tank. These models provide a missing educational tool to expand knowledge of protein structure, foster deeper insight into protein folding, and inspire greater interest in biomacromolecular architecture.

36 MATERIALS SCIENCE↗

Radiometric responsivity determination for Feature Identification and Location Experiment (FILE) flown on space shuttle mission

A procedure was developed to obtain the radiometric (radiance) responsivity of the Feature Identification and Local Experiment (FILE) instrument in preparation for its flight on Space Shuttle Mission 41-G (November 1984). This instrument was designed to obtain Earth feature radiance data in spectral bands centered at 0.65 and 0.85 microns, along with corroborative color and color-infrared photographs, and to collect data to evaluate a technique for in-orbit autonomous classification of the Earth's primary features. The calibration process incorporated both solar radiance measurements and radiative transfer model predictions in estimating expected radiance inputs to the FILE on the Shuttle. The measured data are compared with the model predictions, and the differences observed are discussed. Application of the calibration procedure to the FILE over an 18-month period indicated a constant responsivity characteristic. This report documents the calibration procedure and the associated radiometric measurements and predictions that were part of the instrument preparation for flight.

Wilson, R. G.↗

KATE: From the lab to the firing room

For the past 9 years, the AI group at NASA's Kennedy Space Center (KSC) has been developing a real-time model-based reasoning (MBR) system that can diagnose faults and provide control advisories. The system resulting from this effort is called KATE (Knowledge-based Autonomous Test Engineer). This article describes the transition of KATE from the applied research lab to the firing room (the firing room is the main control and monitoring center for all Shuttle ground support and launch processing). Because the problems that were countered along the way are not unique, we decided to share some of our experiences. This article also includes a brief overview of the latest application of KATE.

Pepe, Charles↗

Station Keeping with an Autonomous Underwater Glider Using a Predictive Model of Ocean Currents

We investigate the use of an autonomous underwater glider as a platform for a virtual mooring. Our approach uses a simple vehicle motion model, a predictive model of ocean currents, and a greedy search algorithm in order to simulate possible actions available to the vehicle and select an action to minimize the distance from the target point. Results from a 19 day experiment in October 2016 near Monterey Bay are presented where we test our control algorithm as well as investigate the effect of a glider’s dive profile on its ability to act as a virtual mooring.

Chien, Steve↗