Results for “mobile resources” — Page 18

NASA NTRS1991

Use of low orbital satellite communications systems for humanitarian programs

Communication and information exchange play a decisive role in progress and social development. However, in many parts of the world the communication infrastructure is inadequate and the capacity for on-line exchange of information may not exist. This is true of underdeveloped countries, remote and relatively inaccessible regions, sites of natural disasters, and of all cases where the resources needed to create complex communication systems are limited. The creation of an inexpensive space communications system to service such areas is therefore a high priority task. In addition to a relatively low-cost space segment, an inexpensive space communications systems requires a large number of ground terminals, which must be relatively inexpensive, energy efficient (using power generated by storage batteries, or solar arrays, etc.), small in size, and must not require highly expert maintenance. The ground terminals must be portable, and readily deployable. Communications satellites in geostationary orbit at altitudes of about 36,000 km are very expensive and require complex and expensive ground stations and launch vehicles. Given current technology, it is categorically impossible to develop inexpensive satellite systems with portable ground terminals using such satellites. To solve the problem of developing an inexpensive satellite communications system that can operate with relatively small ground stations, including portable terminals, we propose to use a system with satellites in low Earth orbit, at an altitude of 900-1500 km. Because low orbital satellites are much closer to the Earth than geostationary ones and require vastly less energy expenditure by the satellite and ground terminals for transmission of messages, a system using them is relatively inexpensive. Such a system could use portable ground terminals no more complex than ordinary mobile police radios.

Vlasov, Vladimir N.↗

NASA NTRSDate not supplied

The Kinematic Navigation and Cartography Knapsack (KNaCK): Demonstrating SlLAM (Simultaneous Localization and Mapping) LiDAR as a Tool for Exploration and Mapping of Lunar Pits and Caves

KNaCK (Kinematic Navigation and Cartography Knapsack) is a backpack-mounted mobile LiDAR (Light Detection and Ranging) system. It can map its surroundings in 3 dimensions and localize itself in space. The project is exploring how LiDAR can advance terrain mapping and navigation at the lunar south pole. KNaCK is lead by Dr. Michael Zanetti of NASA MSFC’s Heliophysics and Planetary Science Branch. - KNaCK serves as - A test article for GPS denied mapping and navigation. - A test bed for SLAM (Simultaneous Localization and Mapping) algorithms. - A test bed for commercial LiDAR units. - A tool for terrestrial science.

LiDAR↗

NASA NTRSDate not supplied

Science Capabilities of the Common Habitat

The Common Habitat is the primary habitable element in a conceptual architecture feasibility study for long-duration space exploration with an eight-person crew size. On lunar or planetary surfaces, the Common Habitat forms the core of a Surface Base Camp. In microgravity, the Common Habitat is the core of the Deep Space Exploration Vehicle (DSEV), an in-space transportation spacecraft. The Common Habitat employs a horizontal orientation that is divided internally into a lower deck, mid deck, and upper deck, roughly separating outfitting into individual, work, and group functions. On a planetary surface, the habitat is incorporated into a base camp, located near the south pole in the case of the Moon. The Mars base camp is currently location agnostic. Each base camp is divided into habitation, landing, resource production, and power zones. In microgravity, the habitat is incorporated into the Deep Space Exploration Vehicle, a vessel capable of transporting the crew within the inner solar system. In addition to crew and teleoperated control of external science assets, the Common Habitat employs a suite of life and physical science laboratory systems to enable it to support science investigations across a variety of destination environments, primarily featuring the Moon and Mars, along with the intervening interplanetary space. Other potential destinations include Near Earth Asteroids and Venus and Earth orbits. Located in the aft starboard section of the Common Habitat mid deck, the life science laboratory supports primarily biology and human research. In its baseline configuration, the laboratory includes horizontal work surfaces, freezers, multiple gloveboxes, sample transfer/exposure capability, large instruments, and reconfigurable ISPR-compatible payload bays. The Medical Care Facility and exercise facility can also support life science research. In the aft port section of the mid deck, the physical science laboratory supports physics, chemistry, materials science, geology, and remote sensing (including astrophysics, heliophysics, Earth science, planetary science, and meteorology). It provides a baseline of similar ISPR-compatible payload bays and adds additional freezers, including those for cryogenic sample storage, a remote sensing workstation, more gloveboxes, also with sample transfer/exposure, combustion chambers, fluid mechanics chambers, and a gas chromatograph. It leverages the Command & Control Center for teleoperations of mobile science assets. Both laboratories are highly modular, with the ability to swap out both payloads and instruments on an as-needed basis. The integrated science outfitting of the Common Habitat positions its crew to contribute to all of NASA’s Moon to Mars science objectives and extend human understanding into the inner solar system.

Lunar Suface↗

NASA NTRS2023

Vertiport Dynamic Density

Advanced Air Mobility (AAM) is envisioned to be another spoke in a region’s transportation system, supplementing ground-based travel with air-based travel. Eventually, air taxis will be pervasive, convenient, and affordable. As demand and operations tempo increase, congestion may arise. Several characteristics of AAM reduce the applicability of techniques used in today’s National Airspace System (NAS) to manage congestion. AAM will include both scheduled and non-scheduled, on-demand operations, challenging strategic deconfliction algorithms. Flights will be fairly short, traversing an urban area, not hundreds or thousands of miles, with corresponding low energy reserves, prohibiting excessive delays. Flight operators will need flexibility in operations, scheduling a flight only minutes before departure, or diverting to an alternate vertiport if it becomes advantageous, further adding to trajectory uncertainty that would challenge strategic deconfliction. Finally, operators will desire privacy to protect sensitive information or preserve competitive advantage, limiting early access to intent information. In this paper, we present an approach for managing congestion at vertiports by providing insight into the traffic situation to support operationally-advantageous and safe land or divert decisions. We propose a metric designed with usefulness and usability in AAM operations in mind. The metric uses the sociology concept of dynamic density (DD) that takes into consideration not only number of flights, but also the interaction of those flights with the vertiport’s limited resources, namely the landing pads and the parking spots. DD supports a Pilot in Command (PIC) with decisions about whether to proceed, expedite, delay, or divert; and supports air traffic control (ATC) and vertiport operators in airspace management and vertiport usage. We demonstrate the metric on a notional vertiport scenario. We also show that DD provides better insight into congestion and resulting flight delays than an aircraft count metric used in traditional air traffic management.

Lilly Spirkovska↗

NASA NTRS2016

A Basic Robotic Excavator (the Glenn Digger): Description, Design, and Initial Operation

This paper describes the design, commercial part selections, fabrication, assembly, installation, and initial operation of a two degree of freedom robotic excavator. Colloquially referred to as "the NASA Glenn Digger," it was designed specifically to be mounted onto, and to operate with, the then newly developed Centaur 2 robotic mobility base. The excavator, when mounted to Centaur 2, is designed to scoop loose regolith from the terrain, raise its loaded bucket up and dump the load into a hopper of at least a 1-m-height. The hopper represents the input to a machine that would process the raw material, such as to produce oxygen from lunar regolith as would be required for long-term lunar habitation. This equipment debuted at the annual Research and Technology Studies ("Desert RATS", Ref. 1) event held north of Flagstaff, Arizona, in September of 2010, when the Digger was successfully joined to Centaur 2 and the shoveling articulation was demonstrated. During 2011, the hardware was modified for added strength, strain gauges were added to measure loads, and the controls were improved in preparation for the 2011 Desert RATS event, where additional "field operations" experience was gained.

backhoe↗

NASA NTRS2022

In-space Servicing, Assembly, and Manufacturing (ISAM) State of Play 2022 Edition

The future of spaceflight will yield increasingly more ambitious missions to support civil, national security, and commercial space sectors. Achieving these ambitious missions is not feasible using the traditional paradigm of launching an integrated, fully functioning system on a single launch vehicle. For example, science and human exploration missions will desire payloads that are larger than any foreseeable launch vehicle fairing, national security missions will require persistent assets that are mobile and resilient, and commercial space missions will require cost-effective ways to update to the latest technology on orbit. In-space Servicing, Assembly, and Manufacturing (ISAM) can vastly expand the performance, availability, and lifetime of space systems compared to the traditional paradigm of launching an asset with no intent to ever touch it again. ISAM capabilities foster an ecosystem that changes the space operations paradigm, creating the foundation for sustainable exploration and serving as a multiplier for other capabilities like space logistics, power generation, and reusability. Previous achievements in ISAM have enabled ambitious human and robotic space missions. NASA’s International Space Station (ISS) operations and maintenance, servicing missions of the Hubble Space Telescope (HST), and Northrop Grumman’s Mission Extension Vehicle (MEV) demonstrate the dramatic operational missions that can be achieved using ISAM capabilities. Many current and upcoming flight demonstrations are advancing areas that will enable the next generation of civil, national security, and commercial space missions. This document describes the current state of ISAM missions, capabilities, and developments. Compiling and organizing the available ISAM capabilities will help mission designers incorporate ISAM technologies into their concepts, create the starting point for technology development plans and roadmaps, and provide technologists a survey of the field they are developing. This document divides the ISAM capabilities into 11 capability areas that describe the functions or activities that would be performed in space using ISAM. This version of the ISAM State of Play is part of a continuing journey to encourage the use of ISAM capabilities in space. Compiling and organizing the current state of ISAM provides a simple resource for those working in the ISAM ecosystem to ensure that the advancements being made build upon the investments of the past. The state of play is ever changing as new capabilities are developed, and this document will be periodically updated to ensure that it is relevant to those who need it in the future.

ISAM↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRS2016

Mars Surface System Common Capabilities and Challenges for Human Missions

NASA has begun a process to identify and evaluate candidate locations where humans could land, live and work on the martian surface. These locations are referred to as Exploration Zones (EZs). Given current mission concepts, an EZ is a collection of Regions of Interest (ROIs) that are located within approximately 100 kilometers of a centralized landing site. ROIs are areas that are relevant for scientific investigation and/or development/maturation of capabilities and resources necessary for a sustainable human presence. The EZ also contains a landing site and a habitation site that will be used by multiple human crews during missions to explore and utilize the ROIs within the EZ. In parallel with this process, NASA continues to make progress on the Evolvable Mars Campaign examining alternatives that can pioneer an extended human presence on Mars that is Earth independent. This involves ongoing assessments of surface systems and operations to enable a permanent, sustainable human presence. Because of the difficulty in getting equipment and supplies to the surface of Mars, part of these assessments involve identifying those systems and processes that can perform in multiple, sometimes completely unrelated, situations. These assessments have been performed in a very generic surface mission carried out at a very generic surface location. As specific candidate EZs are identified it becomes important to evaluate the current suite of surface systems and operations as they are likely to perform for the specific locations and for the types of operations - both scientific and development - that are proposed for these EZs. It is also important to evaluate the proposed EZs for their suitability to be explored or developed given the range of capabilities and constraints for the types of surface systems and operations being considered within the EMC. This means looking at setting up and operating a field station at a central location within the EZ as well as traversing to and exploring the scientific ROIs within the boundaries of the EZ. The proposed paper will describe the current status of common systems and operations as they can be applied to actual EZ locations on Mars. Initially these EZs will be the four locations identified MEPAG's Human Exploration of Mars Science Analysis Group (HEM‐SAG) that will be used as representative of the EZs that will emerge from the process that NASA has initiated. An example process that could be commonly applied is an approach for developing a field station site plan that would be demonstrated by applying this process to each of the four HEM‐SAG sites. Examples of common systems include (a) mobility systems that can be used to off‐load and move payloads to specific locations at the central field station location that could also be used to traverse long distances to reach some of the more remote ROIs or (b) robotic systems that can support various activities (such as system set up and maintenance) at the field station that could also be used to explore scientific ROIs and used to support site‐specific ISRU production activities.

Hoffman, Stephen J.↗

NASA NTRS2021

Mass Inferencing Model Creation And Deployment To Lunar Excavation Robot, RASSOR

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a teleoperated mobile robotic platform with a unique space regolith excavation capability. This research project developed functionality for inferencing regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete ISRU missions. Radio wave propagation time to the Moon and back is ~2.56 seconds. Though teleoperation is possible with this delay, autonomous capability that enables RASSOR to plan and execute excavation missions intelligently and efficiently is preferred. To teleoperate or run autonomously, it is crucial for the quantity of regolith mass ingested by RASSOR to be available as a system state for efficient operation (e.g. knowledge of whether drums are full informs the task of highest priority, whether it be continuing to dig, or returning to a processing plant to offload regolith). A configurable data reduction and analysis pipeline was created to allow for straightforward incorporation of new data, such as that from lunar excavation, to improve model performance in new environments. Four distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR. All four models take in system states and output a mass prediction for each set of the robot’s bucket drums. Initial results from deployment to RASSOR and testing in a simulated lunar environment show that the models have <10% mean error during robot operation. Future work includes refinement of a model that estimates regolith mass in real-time during excavation as well as further testing of the developed models on the hardware.

ROS↗

NASA NTRSDate not supplied

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↗