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At least 1,117 records · Page 62

How Autonomous Intelligent Systems Can Facilitate Earth-independent Medical Care: Going Beyond Telepresence

During the last decade, teleoperated robotic systems have extended humans’ sensorimotor competence to digitally fly beyond the physical barrier of distance and scale and thus transmit sensorimotor skills of the human through direct communication. Telepresence capabilities have enabled tele-physical remote access at small scales thanks to telerobotic mediums. Although the concept was initially motivated by space applications, such technologies quickly have expanded into the medical domain and resulted in teleoperated medical robots, including telerobotic surgical systems (such as the da Vinci surgical system). Effective telepresence fundamentally depends on an agile, reliable, and secure communication medium that can transmit real-time information between the operator and a remote device. However, direct telepresence may not be achievable for long-duration exploration spaceflight missions. Thus, autonomous systems and local intelligence represent potential solutions to the aforementioned issues. One example solution employs demonstration systems which enable learning from the pre-captured inputs of a skilled human operator. These will be computationally modeled and later probabilistically replicated toward the completion of remote physical tasks when direct telepresence is not viable - such as under communication blackout conditions. In other words, trained autonomous systems (e.g., robots) can perform remote operations that mimic the physical performance of experts during remote operations/training. Beyond learning the physics of the task, autonomous agents can also be used to conduct algorithmic decision-making that mimics the higher-level cognition of the expert. Thus, using an autonomous system, pre-trained cognitive and manipulation-based skills can be leveraged (acquired during pre-mission events) to produce digital twins of an intelligent operator. Such systems can be used for the real-time conduction of intricate tasks in complex and unstructured environments. Such systems will operationalize “cognitive digital twins” and can expand the reach of human cognition and manipulation through the power of data-driven learning from demonstration algorithms. This system category will be discussed as a fully autonomous operation in this talk. In addition to the above, we will also propose and discuss the possibility of partial-automation using remote intelligence and remote sensing. In contrast to full automation, partial automation can close the loop through a local operator equipped with augmented sensory awareness through wearable systems. Such technologies will allow the local operator to conduct delicate tasks while being guided using sensory augmentation and being monitored to gauge her/his level of cognitive focus and performance. The difference with the previous category is that a remote human will conduct the task. Further, rather than making a digital twin of human cognition, we will augment the control inputs of the local human to match those of the skilled expert operator who is not accessible in real-time. Going beyond classic telepresence and thus approaching intelligent telepresence, our vision is that autonomous agents will eventually enable the safe, consistent and efficient delivery of complex, remote and smart medical care during space exploration across operators in an Earth-independent fashion. We will discuss our collective vision from NASA and MERIIT@NYU lab in this talk.

Telepresence↗

Radiation Tolerance and Mitigation for Neuromorphic Processors

Neuromorphic processors are designed to execute Deep Neural Networks (DNNs) at very high speed using only a fraction of the electrical power needed to run a DNN on a traditional CPU or GPU. This unique capability makes Neuromorphic processors a prime candidate for space systems, where advanced computational tasks like image analysis, depth map reconstruction, or rover control need to be executed in a power-starved environment. In contrast to the growing number of applications of Neuromorphic processors in smart phones, the automotive and robotics domain, the space environment is unforgiving because of extreme temperatures and high levels of radiation. Any space system, operating beyond LEO requires computing hardware that is resilient against radiation effects. However, Neuromorphic processors have not yet been designed or tested for their radiation tolerance. In this report, we consider traditional methods of detection of radiation events and mitigation via redundancy and gauge their effectiveness on DNNs. In contrast to traditional flight software, however, neural networks represent a statistical algorithm, which might affect its resilience against radiation events. We will focus on the analysis of the tolerance of DNNs with respect to radiation events and discuss techniques to detect radiation hits using on-chip triple modular redundancy (TMR) on an Intel Loihi neuromorphic processor and to mitigate radiation damage. We describe an architecture for on-chip TMR for the Intel Loihi and present results of initial experiments.

Neural Networks↗

Coordinating Innovative Technology Development at NASA

Innovation requires continually using and coordinating new knowledge on the current, anticipated, and even unanticipated developments in a given area. You do not want to simply repeat what others are doing, so you must remain aware of other activities in your area of interest. You also have to maintain awareness of where the cutting edge is, because the limit of what’s possible is continually pushed and redefined. Finally, “smart” innovation involves not only seeking innovative solutions to anticipated problems but also being prepared to respond to unanticipated problems or even unanticipated solutions. As the leading U.S. agency for aeronautics development, cutting-edge scientific discovery, and human space exploration, the National Aeronautics and Space Administration (NASA) must remain at the forefront of innovation. NASA enables innovation in technology development through a coordinated effort among its internal mission directorates and its partners in government, commercial, and academic sectors. As an independent office, NASA’s Office of Technology, Policy, and Strategy (OTPS) provides the coordination effort necessary to ensure innovation. OTPS uses and coordinates inputs from a broad community of experts. With these inputs, OTPS identifies and communicates shared needs and opportunities for partnership. OTPS provides strategic advice to synergize and ultimately facilitate innovative technology development both within and outside NASA. This paper summarizes NASA’s approach to ensuring innovation through a deliberately coordinated effort. In this paper, we first provide examples of how this coordination has enabled innovative technology development. We then describe how NASA’s mission directorates use input from a broad community of experts to inform innovative technology development. Finally, we describe OTPS’s role in coordinating inputs from both internal and external experts, including standardizing communication about technology developments, facilitating conversations with external partners on shared technology needs and solutions, and advising on strategies and policies to ensure innovative technology development. This paper can help members of the aerospace community understand their role in NASA’s innovation efforts and how to engage OTPS in conversations about potential partnership.

Erica Rodgers↗

Coordinating Innovative Technology Development at NASA

Innovation requires continually using and coordinating new knowledge on the current, anticipated, and even unanticipated developments in a given area. You do not want to simply repeat what others are doing, so you must remain aware of other activities in your area of interest. You also have to maintain awareness of where the cutting edge is, because the limit of what’s possible is continually pushed and redefined. Finally, “smart” innovation involves not only seeking innovative solutions to anticipated problems but also being prepared to respond to unanticipated problems or even unanticipated solutions. As the leading U.S. agency for aeronautics development, cutting-edge scientific discovery, and human space exploration, the National Aeronautics and Space Administration (NASA) must remain at the forefront of innovation. NASA enables innovation in technology development through a coordinated effort among its internal mission directorates and its partners in government, commercial, and academic sectors. As an independent office, NASA’s Office of Technology, Policy, and Strategy (OTPS) provides the coordination effort necessary to ensure innovation. OTPS uses and coordinates inputs from a broad community of experts. With these inputs, OTPS identifies and communicates shared needs and opportunities for partnership. OTPS provides strategic advice to synergize and ultimately facilitate innovative technology development both within and outside NASA. This paper summarizes NASA’s approach to ensuring innovation through a deliberately coordinated effort. In this paper, we first provide examples of how this coordination has enabled innovative technology development. We then describe how NASA’s mission directorates use input from a broad community of experts to inform innovative technology development. Finally, we describe OTPS’s role in coordinating inputs from both internal and external experts, including standardizing communication about technology developments, facilitating conversations with external partners on shared technology needs and solutions, and advising on strategies and policies to ensure innovative technology development. This paper can help members of the aerospace community understand their role in NASA’s innovation efforts and how to engage OTPS in conversations about potential partnership.

Erica Rodgers↗

UASs Optimization - An evaluation of multiple mode vehicles in monitoring and communication

This presentation will describe the author's experience as Co-I in the NASA/USFS Project: "Strategic Tac Radio and Tac Overwatch (STRATO): Last Mile Communications and Realtime Observation Stratospheric Platforms for wildland fire". This project utilizes an uncrewed Stratospheric (> 70Kft) lighter than air Uninhabited Aerial System (UAS), carries 50 Kg of sensor and communications equipment, and remains on station over a wildfire for up to 30 days. The air vehicle is capable of in excess of 180 days on station, but while the full duration capabilities were not utilized in this project, they could, and would, be used in monitoring a feature like a volcano. In addition to duration, the ability of these Stratospheric Platforms to persistently monitor an area, while carrying "smart" sensors - generating information products, rather than just data - is game changing. Utilizing technologies such as LoRa, the platform can now be the nexus of an ad-hoc sensor web, receiving LoRa based data from low power in-situ sensors in the area being monitored, combining it with high fidelity information gained from on-board sensors, and sending the information product to a remote research facility, using Satellite communication, in real time. The author will relate the evolution of information collection, derivation and delivery mechanisms in the entire range of NASA's UAS fleet, to today's world, where information products conventionally derived in desk top computational environments, and made available to the Science Community in weeks or months, are now being generated and delivered in near real time.

UASs↗

A Structurally-Adaptive Framework for Distributed Airborne Sensing over Real-time Collaborative Information Sharing Networks

The emergence and maturation of wireless communication technologies continue to transform the aviation industry and are enabling new solutions to challenges faced by NASA’s Advanced Air Mobility (AAM) initiative. AAM is leading towards high-density autonomous aircraft operations in areas underserved by traditional aviation, such as over densely populated urban centers. In this paper, we build on concepts from distributed sensing and smart spaces - where sensing, processing, and communication are embedded in an environment, and agents are operating within the space can exploit these capabilities in real-time through collaborative information sharing networks. Building from these concepts, we propose a framework to enable a dynamic, topologically-adaptive, and distributed estimation system for man-rated aviation to address challenges faced by autonomous AAM operations. This paper presents the initial concept of operations and system design for this framework, presents a mathematical formulation for abstraction of the problem, identifies requirements and constraints for operation, and presents algorithmic constructs to demonstrate operation. The initial framework design will focus on supporting precision navigation and independent surveillance supporting conformance monitoring of aircraft in airspace corridors and vertiport airspaces. Preliminary results from this framework shows promise in addressing gaps in current technologies needed to enable future AAM concepts, while promising greater capabilities, performance, robustness, and safety over current aviation systems and operations.

Distributed sensing↗

Differences in Water Vapor Radiative Transfer Among 1D Models Can Significantly Affect the Inner Edge of the Habitable Zone

An accurate estimate of the inner edge of the habitable zone is critical for determining which exoplanets are potentially habitable and for designing future telescopes to observe them. Here, we explore differences in estimating the inner edge among seven one-dimensional radiative transfer models: two line-by-line codes (SMART and LBLRTM) as well as five band codes (CAM3, CAM4_Wolf, LMDG, SBDART, and AM2) that are currently being used in global climate models. We compare radiative fluxes and spectra in clear-sky conditions around G and M stars, with fixed moist adiabatic profiles for surface temperatures from 250 to 360 K. We find that divergences among the models arise mainly from large uncertainties in water vapor absorption in the window region (10 μm) and in the region between 0.2 and 1.5 μm. Differences in outgoing longwave radiation increase with surface temperature and reach 10–20Wm(exp −2); differences in shortwave reach up to 60 Wm(exp −2), especially at the surface and in the troposphere, and are larger for an M-dwarf spectrum than a solar spectrum. Differences between the two line-by-line models are significant, although smaller than among the band models. Our results imply that the uncertainty in estimating the insolation threshold of the inner edge (the runaway greenhouse limit) due only to clear-sky radiative transfer is ≈10% of modern Earth’s solar constant (i.e., ≈34 Wm(exp −2) in global mean) among band models and ≈3% between the two line-by-line models. These comparisons show that future work is needed that focuses on improving water vapor absorption coefficients in both shortwave and longwave, as well as on increasing the resolution of stellar spectra in broadband models.

Numerical methods↗

Electronic Nose Development and Preliminary Human Breath Testing for Rapid, Non-Invasive COVID-19 Detection

We adapted an existing, spaceflight-proven, robust “electronic nose” (E-Nose) that uses an array of electrical resistivity-based nanosensors mimicking aspects of mammalian olfaction to conduct on-site, rapid screening for COVID-19 infection by measuring the pattern of sensor responses to volatile organic compounds (VOCs) in exhaled human breath. We built and tested multiple copies of a hand-held prototype E-Nose sensor system, composed of 64 chemically sensitive nanomaterial sensing elements tailored to COVID-19 VOC detection; data acquisition electronics; a smart tablet with software (App) for sensor control, data acquisition and display; and a sampling fixture to capture exhaled breath samples and deliver them to the sensor array inside the E-Nose. The sensing elements detect the combination of VOCs typical in breath at parts-per-billion (ppb) levels, with repeatability of 0.02% and reproducibility of 1.2%; the measurement electronics in the E-Nose provide measurement accuracy and signal-to-noise ratios comparable to benchtop instrumentation. Preliminary clinical testing at Stanford Medicine with 63 participants, their COVID-19-positive or COVID-19-negative status determined by concomitant RT-PCR, discriminated between these two categories of human breath with a 79% correct identification rate using “leave-one-out” training-and-analysis methods. Analyzing the E-Nose response in conjunction with body temperature and other non-invasive symptom screening using advanced machine learning methods, with a much larger database of responses from a wider swath of the population, is expected to provide more accurate on-the-spot answers. Additional clinical testing, design refinement, and a mass manufacturing approach are the main steps toward deploying this technology to rapidly screen for active infection in clinics and hospitals, public and commercial venues, or at home.

COVID-19↗

Deployable Greenhouse for Food Production

The X-HAB team in the CU Graduate Projects course was tasked with developing a Distributed Robotic Plant Production System (DRoPPS) that provided remote food production capability for long-duration space missions. The system consists of a Remotely Operated Gardening Robot (ROGR) and a Smart Pot (SPOT). SPOT was designed to grow a plant in a hydroponic system, while also providing multiple sensor data feedback to a remote user. ROGR was designed to give a remote user a method of moving SPOT and caring for the plant. A User Interface (UI) was created to provide a means by which a remote user could interact with the system. At the conclusion of the semester, ROGR was 95% complete, and only required minor mechanical fixes. SPOT was 85% complete, and required additional machining of mechanical parts. The UI was 75% complete, and required additional functionality and user testing. The project was not fully completed due to the scope of the project. Regardless, multiple team members have agreed to continue working on the project to finish by the June 1st presentation day at NASA.

Nikolaus Correll↗

Additively Manufactured Ink-based Cell Batteries for Flexible Conformational Applications

With the rise of additively manufactured electronics, the importance of thin, flexible printed batteries has increased for shape-conformal electronics. Traditionally, battery cells such as industry-standard lithium-ion batteries, have been rigid and therefore unable to conform to time-variant surfaces such as smart cards/RFID devices, disposable battery-powered skin patches, foldable electronic devices, and IoT nodes. Aligned with the effort to advance technologies on printed electronics for habitat systems and advanced manufacturing, this proof-of-concept will focus on printing a 50 mAh Zn-Ag battery implementing a DIW Technique. After prototype manufacturing, electrical and environmental testing on the flexible battery cell will be conducted to the evaluate energy and power density over 50 charge cycles under standard atmosphere conditions. The project aims to provide PCB designers, fabricators, and assemblers an integrated battery package to eliminate external batteries, external wiring and connectors, and conformational complexity. Potential end uses of the printed battery process will be within Martian and lunar habitat systems, Gateway, and surface vehicles, or aerial robots where space and geometry is a restriction.

Printed Electronics↗

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

From May 8th to June 9th, 2023, I had the opportunity to participate in an experiential learning experience at Johnson Space Center in Houston, TX with Exploration Medical Capability (ExMC), an element of the NASA Human Research Program. During this research experience, I was not only able to work on the above titled research project, but also gain an immense exposure to the field of aerospace medicine, make numerous connections within the field, tour NASA facilities, as well as travel to the Aerospace Medical Association Annual Conference (AsMA) in New Orleans. To briefly introduce my project, it is well understood that the medical capabilities available to crew medical officers (CMOs) on the International Space Station will be different than the capabilities available and needed during deep space exploration missions to the Moon, Mars, and beyond. Ground support is particularly limited due to distance, communication delays (or lack of communication), and lack of resupply. Therefore, to support medical care by CMOs on these missions, robust clinical decision support systems (CDSSs) must be designed. The recent publication and public launch of generative artificial intelligence (AI) tools based upon large language models (LLM) such as ChatGPT provides the opportunity to create a smart assistant for onboard triage, diagnosis, and treatment of medical conditions. Ultimately, the overall purpose of the project was to research what AI tools currently exist or are in development, and to see how they might be implemented onboard during exploration class spaceflights of the future. The ExMC element is actively developing several tools to be used in preparation for and during deep space exploration missions. One of those tools, known as IMPACT, is a probabilistic risk assessment model which can be used to propose a desired medical system (based on mass and volume) and suggest the clinical outcomes likely to occur for a design reference mission (DRM). The group recently presented the IMPACT model and a DRM of interest titled “Modified Long Duration Lunar Orbital and Lunar Surface” (mLDLOLS) at the recent AsMA conference. The mLDLOLS mock mission is a 9 month and 6-day deep space exploration mission consisting of time in Moon’s orbit (3 months on the Gateway space station), on the lunar surface (3 months within habitat), and another 3 months on Gateway before return to Earth. For this DRM, IMPACT ultimately outlined a preferred medical system that was then associated with medical conditions considered to be most likely based on frequency, most likely to cause astronaut task time loss (TTL), most likely to cause return to definitive care (RTDC), and most likely cause loss of crew life (LOCL). IMPACT also highlighted the medical capabilities/skills that would be required to care for those medical conditions, such as performing a history of present illness or musculoskeletal exam with ultrasound. The primary objective of the project was to perform a survey of the AI tools and systems applicable to the conditions outlined for the proposed mLDLOLS mission. Using PubMed (including most relevant MeSH terms) and Google Scholar, we then created a robust annotated bibliography organized by condition. The 56-page and over 500 reference annotated bibliography was subsequently used to create a review outline that would become the basis for drafting of a future publication. For the review outline, we took those medical conditions researched within the annotated bibliography (condition-based approach) and deployed a systems-based approach, combining those medical conditions and related tools into ten categories. These categories included general/all-purpose CDSSs, tools to diagnose or manage respiratory, dermatologic, neurologic, auditory and vestibular, ophthalmic, musculoskeletal, infection-associated, and gynecologic conditions, as well as tools that could be deployed in the setting of trauma/emergency. With the completion of the 30-page outline, we then began drafting the review paper. To conclude the research experience, I presented the findings from our survey to the ExMC Clinical and Science team. With these objectives, I ultimately learned about the number of AI tools that exist today to assist medical professionals with the triage, diagnosis, and management of several medical conditions. These tools can span from chatbot assistants to help triage knee pain to vision transformer models that can identify ophthalmic conditions based on ocular surface images captured with a cell phone. We also highlighted the current gaps that exist in the literature alongside the advancements that are needed to make the desired CDSS for deep space exploration missions. With this experience, I certainly confirmed an existing career goal and identified several additional skills needed to become an aerospace medical doctor including knowledge of critical care in an extreme medicine setting, aerospace engineering and human integration systems, artificial intelligence, machine learning, and risk models. I also identified numerous transferable skills for this career goal including the basic knowledge of medicine (MD), deployment of the scientific method for critical thought about new scientific questions (PhD), review of published literature, including creating an annotated bibliography (PhD), as well as detailed scientific writing (PhD). The results of my research will likely guide the design of an all-encompassing onboard medical assistant for use during deep space exploration missions of the future. I plan on sharing the outcomes from this experience with my peers at a student seminar in the Fall semester on August 30th. During the seminar, I will detail the project, my experience at NASA and AsMA, as well as offer best practice guidelines for students entertaining similar experiences or careers. In conclusion, I would like to thank the WVU School of Medicine, Research and Graduate Education office, as well as NASA ExMC for the unwavering support of this life-changing experience.

Ryan A. Lacinski↗

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

BACKGROUND The medical capabilities necessary for long-duration exploration missions (LDEMs) will differ tremendously from those currently available to crew medical officers (CMOs) on the International Space Station (ISS). Ground support will be more challenging due to distance-related communication delays and data transmission, and resource utilization must be optimized given limited ability for resupply. Clinical decision support systems (CDSSs) can help mitigate these limitations. The recent launch of generative artificial intelligence (AI) tools based upon large language models (LLM) support the creation of a smart assistant for onboard triage, diagnosis, and guided treatment of medical conditions during these missions. The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) tool can help predict which clinical problems and outcomes are likely to occur for a design reference mission (DRM) and assist Medical Operations and systems engineering teams in creating a medical system that may optimally mitigate the predicted risks. The purpose of this study was to identify AI tools currently available or in development for the assistive diagnosis and care of medical conditions predicted for an extended duration Lunar mission. METHODS The 119 medical conditions currently built into the IMPACT suite were categorized into systems, and these diagnoses were used as keywords for our literature search. Using PubMed and Google Scholar, we performed a literature survey of AI tools applicable to these conditions. Article inclusion criteria included publication between the years 2017-2023, as the sentinel paper discussing the “selective attention” driving ChatGPT and other generative transformer models was published in June 2017. Where applicable, we reviewed only the top 1000 research articles (based on relevance) for each of the keywords/phrases. AI tools whose training sets were exclusive to a pediatric patient population were excluded. We also excluded any medical diagnostic tools (such as CT, MRI, mass spectrometry) or procedures (such as endoscopy, surgery) that are unlikely to be available during LDEMs due to mass and volume constraints, CMO knowledge, skills, and abilities, and/or inherent procedural risks. RESULTS Our survey highlighted several AI-driven tools for the triage, diagnosis, and management of those medical conditions highlighted by IMPACT. Selected publications for each medical condition were then screened for inclusion within ten systems-based categories including: general diagnostic tools (25), tools to diagnose and manage respiratory (40), dermatologic (34), neurologic (28), auditory and vestibular (30), ophthalmic (34), musculoskeletal (104), infection-associated (92), and gynecologic (19) conditions, as well as tools that could be deployed in the setting of trauma and emergency (34). CONCLUSIONS Numerous AI-driven tools were highlighted within this literature survey, ranging from chatbot assistants that triage knee pain to vision transformer models for diagnosis of ophthalmic conditions using ocular surface images captured with a mobile phone. Remaining challenges include optimizing connectivity and integration of existing and developing systems into the vehicles or habitats. Notably, findings from this survey could help guide the initial design of an all-encompassing, onboard medical AI assistant for use during future LDEMs.

R A Lacinski↗

NASA’s Astrophysics Cross-Observatory Science Support (ACROSS) Initiative

The Astro2020 Decadal Survey recommended an investment in Time Domain and Multi-Messenger Astrophysics (TDAMM) as the top-priority sustaining activity in space for the coming decade. One aspect of NASA’s response to this recommendation is a pilot project, the Astrophysics Cross-Observatory Science Support (ACROSS) Initiative, designed to provide support to both missions and observers as they pursue TDAMM science. Here, we present the results of our study and initial plans for ACROSS activities, including services to facilitate and improve cross-mission follow-up planning and execution; a multi-messenger web portal with links to existing mission resources, community tools, and information targeted for TDAMM General Observers; development of “Smart Target of Opportunity submission page” proof-of-concepts; and ongoing development of a potential TDAMM general observing competitive grant solicitation. We invite further discussion with both scientists and mission representatives to better understand their needs and concerns as ACROSS begins its pilot phase.

Brian Humensky↗

Flight Test Configuration of the Sensor Payload and the Ground Nodes in Distributed Sensing Frameworks

The realization of the Urban Air Mobility (UAM) vision, entailing the deployment of high-density autonomous flights over urban areas, necessitates methodologies transcending contemporary airborne sensing techniques. An instrumental innovation in this realm is the advent of distributed sensing. In this paradigm, environmental sensors function as active agents, engaging in the triad of sensing, processing, and communication either amongst themselves, with ground stations, or both. This concerted effort creates a dynamic and responsive "smart space," facilitating real-time autonomous control. NASA Ames Research Center is actively engaged in a series of comprehensive indoor and outdoor flight tests to garner requisite data and insights essential for the actualization of this ambitious vision. This paper delineates the holistic configuration of the flight payload and the distributed ground nodes pivotal to the NASA flight test campaign. Furthermore, it expounds upon the intricacies of the sensor node communication framework. The narrative extends to provide a detailed overview of the strategic placement of distributed sensors and a comprehensive account of the varied indoor and outdoor flight tests orchestrated in pursuit of UAM objectives.

urban air mobility↗

Cyber Resiliency and the Implementation of a Host-Based Intrusion Detection System in an Urban Air Mobility Environment

With the growth in urban air mobility systems and the increasing reliance on interconnected technologies, ensuring the security of these complex infrastructures has become critical. As cities evolve into smart urban centers, the vulnerability to cyber threats escalates, possibly endangering citizens’ safety and the efficiency of transportation networks. In response to these challenges, this paper presents a study on implementing a Host-Based Intrusion Detection System (HIDS) tailored explicitly to urban mobility environments’ unique demands. This study explores the design, development, and deployment of the proposed HIDS, focusing on its adaptability to monitor the hybrid nature of the urban mobility environment. Leveraging machine learning algorithms and anomaly detection techniques, the HIDS is equipped to continuously monitor and analyze the behavior of individual host systems, vehicles, and devices, thereby providing a proactive approach to threat detection. Implementing a HIDS is a pivotal strategy for enhancing cyber resiliency, as it gives an organization granular visibility into internal system activities, enables rapid detection and response to anomalous behavior and cyber threats, and fortifies the organization’s overall cybersecurity posture. In conclusion, this paper highlights the significance of host-based intrusion detection in urban mobility environments and underscores the necessity of tailored security solutions to safeguard against emerging cyber threats.

UAM↗

Flight Test Configuration of the Sensor Payload and the Ground Nodes in Distributed Sensing Frameworks

The realization of the Urban Air Mobility (UAM) vision, entailing the deployment of high-density autonomous flights over urban areas, necessitates methodologies transcending contemporary airborne sensing techniques. An instrumental innovation in this realm is the advent of distributed sensing. In this paradigm, environmental sensors function as active agents, engaging in the triad of sensing, processing, and communication either amongst themselves, with ground stations, or both. This concerted effort creates a dynamic and responsive "smart space," facilitating real-time autonomous control. NASA Ames Research Center is actively engaged in a series of comprehensive indoor and outdoor flight tests to garner requisite data and insights essential for the actualization of this ambitious vision. This paper delineates the holistic configuration of the flight payload and the distributed ground nodes pivotal to the NASA flight test campaign. Furthermore, it expounds upon the intricacies of the sensor node communication framework. The narrative extends to provide a detailed overview of the strategic placement of distributed sensors and a comprehensive account of the varied indoor and outdoor flight tests orchestrated in pursuit of UAM objectives.

urban air mobility↗

An Integrated Architecture Study for Autonomous Lunar Construction

Lunar construction is an expanding field within NASA’s Moon to Mars objectives that presents many challenges and requires innovative and reliable forms of autonomous operations on the surface of the Moon to further the technologies needed for human space exploration. Marshal Space Flight Center’s (MSFC) Advanced Concepts Office (ACO) addressed Lunar Infrastructure Objective LI-4 l by developing a Pre-Phase A, integrated architecture to inform a demonstration for lunar construction operations. The ACO study traded three architectures that would survey and prepare a construction area to build a landing pad out of lunar regolith using MMPACT (Moon-to-Mars Planetary Autonomous Construction Technology) platforms, rovers, and navigation outposts. The main trades examined navigation for the system/architecture, options for rover navigation, battery vs continuous tether power for the MMPACT robotic arm, and assigning site prep functionality to the rovers vs the platforms. Results of the study determined that the best options for the scenario provided would be local navigation (more accurate and continuous), a combination of Light Detection and Ranging (LiDAR) for initial site mapping with subsequent Smart Video Guidance Sensors (SVGS) to save power for construction, and using tethered power to decrease mission duration. The team also concluded that assigning site prep functionality to either the rovers or the platforms has benefits and challenges; future studies could explore having that functionality on both the rovers and platforms. Lastly, the team provided a Concept of Operations (ConOps) timeline that can be used in real-time ground demonstrations to explore the mission timeline, construction processes, autonomous operations, and communication systems that can be tested using MSFC’s lunar regolith field and Lunar Utilization Control Area (LUCA).

Sarah Triana↗

Simulating Secure Data Exchange and Storage for Urban Air Mobility Environments

Urban Air Mobility (UAM) defines an environment for managing operations of vertical takeoff and landing (VTOL) and short takeoff and landing (STOL) vehicles in an urban environment. Within a UAM environment, UAM operators manage fleets of vehicles, relying on Providers of Services for UAM (PSUs) for managing flights in a region of airspace. Flight plan deconfliction is primarily performed by the Discovery and Synchronization Service (DSS), and the Federal Aviation Administration (FAA) maintains control over the UAM space via the FAA-Industry Exchange Protocol (FIDXP). UAM is a federated environment with many different entities owning and operating vehicles, PSUs, and other services. These entities often need to interoperate or access data generated by other organizations. This paper demonstrates the feasibility of using blockchain to facilitate a secure data exchange and storage for this flight information in a UAM environment. In particular, this paper is focused on flight plans and telemetry data. A blockchain network was developed with a set of smart contracts for managing relevant flight data. Hyperledger Fabric was chosen as it is performent, scalable, and allows organizations to reuse existing public key infrastructure (PKI) for identity management. A set of simulated UAM services were also developed. These services propose flight plans and negotiate with other UAM services for airspace access. All interactions between UAM services, as well as vehicle telemetry data, is recorded onto the blockchain. Vehicle telemetry data is generated by a vehicle flight simulation service. This paper successfully demonstrates the feasibility of using blockchain as a secure data exchange and storage mechanism in a UAM environment.

UAM↗