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All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Space Station crew workload - Station operations and customer accommodations

The features of the Space Station which permit crew members to utilize work time for payload operations are discussed. The user orientation, modular design, nonstressful flight regime, in space construction, on board control, automation and robotics, and maintenance and servicing of the Space Station are examined. The proposed crew size, skills, and functions as station operator and mission specialists are described. Mission objectives and crew functions, which include performing material processing, life science and astronomy experiments, satellite and payload equipment servicing, systems monitoring and control, maintenance and repair, Orbital Maneuvering Vehicle and Mobile Remote Manipulator System operations, on board planning, housekeeping, and health maintenance and recreation, are studied.

Shinkle, G. L.↗
NASA NTRSDate not supplied

A Flight Replanning Tool for Terminal Area Urban Air Mobility Operations

In this presentation we describe and evaluate a flight replanning tool, called the trial planner, for terminal area air transport applications. The trial planner employs predefined airspace structures to generate rerouting options between vertiports. In a simplified definition, vertiports are facilities that provide services for managing the take-off and landing of autonomous or manned electric vertical take-off or landing (eVTOL) aircraft. The airspace structures involved in our test cases are arrival routes that consist of predefined entry points to which a transitional path is computed dynamically from the current position of the aircraft. Rerouting options include the transitional path and approach segment along the arrival route; these rerouting options are not vetted for potential conflicts with other operations until a human operator commits to a choice and forwards the flight plan modification for approval from an automated airspace management service. A selected rerouting option is executed by loading a flight plan file to the aircraft ground control station. We conducted a qualitative evaluation of the trial planner using ratings provided by flight crews and air transportation human factors experts with relevant experience from air traffic control. Evaluation criteria included trust in the route recommendations and adequate explanation for the route options. We detail the logic and implementation of the trial planner, as well as report the results of the evaluation of the implementation herein.

Trial planning↗
NASA NTRS2020

Intelligent Contingency Management for Urban Air Mobility

The third aviation revolution is seeking to enable transportation where users have access to immediate and flexible air travel; the users dictate trip origin, destination and timing. One of the major components of this vision is urban air mobility (UAM) for the masses. UAM means a safe and efficient system for vehicles to move passengers and cargo within a city. In order to reach UAM’s full market potential the vehicle will have to be autonomous. One of the primary challenges of autonomous flight is dealing with off-nominal events, both common and unforeseen; thus, intelligent contingency management (ICM) is one of the enabling technologies. In this context, the vehicle has to be aware of its internal state and external environment at all times, ascertain its capability and make decisions about mission completion or modification. All of these functions require data to model and assess the environment and then take actions based on these models. Necessarily, there is uncertainty associated with the data and the models generated from it. Since we are dealing with safety-critical systems, one of the main challenges of ICM is to generate sufficient data and to minimize its uncertainty to enable practical and safe decision making. We propose an overall architecture that incorporates deterministic and learning algorithms together to assess vehicle capabilities, project these into the future and make decisions on mission management level. A layered approach allows for mature parts and technologies to be integrated into early highly automated vehicles before the final state of autonomy is reached.

data-driven systems↗
NASA NTRS1994

Task automation in a successful industrial telerobot

In this paper, we discuss cooperative work by Oak Ridge National Laboratory and Remotec, Inc., to automate components of the operator's workload using Remotec's Andros telerobot, thereby providing an enhanced user interface which can be retrofit to existing fielded units as well as being incorporated into new production units. Remotec's Andros robots are presently used by numerous electric utilities to perform tasks in reactors where substantial exposure to radiation exists, as well as by the armed forces and numerous law enforcement agencies. The automation of task components, as well as the video graphics display of the robot's position in the environment, will enhance all tasks performed by these users, as well as enabling performance in terrain where the robots cannot presently perform due to lack of knowledge about, for instance, the degree of tilt of the robot. Enhanced performance of a successful industrial mobile robot leads to increased safety and efficiency of performance in hazardous environments. The addition of these capabilities will greatly enhance the utility of the robot, as well as its marketability.

Spelt, Philip F.↗
NASA NTRS2007

Robotics

Lunar robotic functions include: 1. Transport of crew and payloads on the surface of the moon; 2. Offloading payloads from a lunar lander; 3. Handling the deployment of surface systems; with 4. Human commanding of these functions from inside a lunar vehicle, habitat, or extravehicular (space walk), with Earth-based supervision. The systems that will perform these functions may not look like robots from science fiction. In fact, robotic functions may be automated trucks, cranes and winches. Use of this equipment prior to the crew s arrival or in the potentially long periods without crews on the surface, will require that these systems be computer controlled machines. The public release of NASA's Exploration plans at the 2nd Space Exploration Conference (Houston, December 2006) included a lunar outpost with as many as four unique mobility chassis designs. The sequence of lander offloading tasks involved as many as ten payloads, each with a unique set of geometry, mass and interface requirements. This plan was refined during a second phase study concluded in August 2007. Among the many improvements to the exploration plan were a reduction in the number of unique mobility chassis designs and a reduction in unique payload specifications. As the lunar surface system payloads have matured, so have the mobility and offloading functional requirements. While the architecture work continues, the community can expect to see functional requirements in the areas of surface mobility, surface handling, and human-systems interaction as follows: Surface Mobility 1. Transport crew on the lunar surface, accelerating construction tasks, expanding the crew s sphere of influence for scientific exploration, and providing a rapid return to an ascent module in an emergency. The crew transport can be with an un-pressurized rover, a small pressurized rover, or a larger mobile habitat. 2. Transport Extra-Vehicular Activity (EVA) equipment and construction payloads. 3. Transport habitats and power modules over long distances, pre-positioning them for the arrival of crew on a subsequent lander. Surface Handling 1. Offload surface system payloads from the lander, breaking launch restraints and power/data connections. Payloads may be offloaded to a wheeled vehicle for transport. 2. Deploy payloads from a wheeled vehicle at a field site, placing the payloads in their final use site on the ground or mating them with existing surface systems. 3. Support regolith collection, site preparation, berm construction, or other civil engineering tasks using tools and implements attached to rovers. Human-Systems Interaction 1. Provide a safe command and control interface for suited EVA to ride on and drive the vehicles, making sure that the systems are also safe for working near dismounted crew. 2. Provide an effective control system for IV crew to tele-operate vehicles, cranes and other equipment from inside the surface habitats with evolving independence from Earth. .. Provide a supervisory system that allows machines to be commanded from the ground, working across the Earth-Lunar time delays on the order of 5-10 seconds (round trip) to support operations when crew are not resident on the surface. Technology Development Needs 1. Surface vehicles that can dock, align and mate with outpost equipment such as landers, habitats and fluid/power interfaces. 2. Long life motors, drive trains, seals, motor electronics, sensors, processors, cable harnesses, and dash board displays. 3. Active suspension control, localization, high speed obstacle avoidance, and safety systems for operating near dismounted crew. 4. High specific energy and specific power batteries that are safe, rechargeable, and long lived.

Ambrose, Robert O.↗
NASA NTRSDate not supplied

Urban Air Mobility Airspace Dynamic Density

Airspace safety must be assured for Urban Air Mobility (UAM) to become reality. Emerging operations need to safely integrate with existing and future air traffic. UAM is anticipated to evolve in stages. Early stages, characterized by low-density operations, may be accommodated by traditional air traffic management approaches. However, to enable the scale of UAM operations necessary to reach ubiquitous integration into daily life, new paradigms that utilize collaborative and automated systems are being considered. Correspondingly, traditional approaches for determining a safe number of simultaneous operations in an airspace must be adapted to suit evolving traffic management paradigms. In this paper, we examine dynamic density as an approach to determine when an airspace is excessively populated, and safety may become compromised. We review previous work on dynamic density as it relates to air traffic controller workload and propose factors that may be more fitting for UAMs. Rather than considering controller workload, we propose factors that suggest increased likelihood of loss of separation and may lead to conflicts that require tactical avoidance maneuvers. In addition to monitoring dynamic density for tactical decision making, we endeavor to predict airspace dynamic density with adequate look-ahead to allow for strategic route selection.

UAM↗
NASA NTRSDate not supplied

Flight Dynamics Modeling for a Rapid Conceptual Development Environment

This paper presents the integration of flight dynamics modeling (FDM) within an aircraft conceptual development framework. Within this framework, FDM and simulation are used for the preliminary analysis of the takeoff, landing, and critical loss of thrust performance of early-stage aircraft concepts. The aerodynamic and propulsive models are built using the XML-based DAVE-ML model exchange. Automated integration of these models is achieved using the Simulink® model generation available through the DAVEtools Java package in addition to Simulink®-based flight simulation blocks. Results demonstrating the established methods are presented for the analysis of a twin-engine light transport example aircraft. This work establishes methods that will be used to perform trade studies of aircraft designed for Regional Air Mobility operations. These aircraft will require short takeoff and landing distances in addition to steep climb and descent gradients, each of which can be estimated and applied as design space constraints using the current methods.

Flight Dynamics↗
NASA NTRS2011

Integrating Machine Learning into a Crowdsourced Model for Earthquake-Induced Damage Assessment

On January 12th, 2010, a catastrophic 7.0M earthquake devastated the country of Haiti. In the aftermath of an earthquake, it is important to rapidly assess damaged areas in order to mobilize the appropriate resources. The Haiti damage assessment effort introduced a promising model that uses crowdsourcing to map damaged areas in freely available remotely-sensed data. This paper proposes the application of machine learning methods to improve this model. Specifically, we apply work on learning from multiple, imperfect experts to the assessment of volunteer reliability, and propose the use of image segmentation to automate the detection of damaged areas. We wrap both tasks in an active learning framework in order to shift volunteer effort from mapping a full catalog of images to the generation of high-quality training data. We hypothesize that the integration of machine learning into this model improves its reliability, maintains the speed of damage assessment, and allows the model to scale to higher data volumes.

crowdsourcing↗
NASA NTRSDate not supplied

A Human-In-The-Loop Simulation for Urban Air Mobility in the Terminal Area

In this presentation we propose a human-in-the-loop experiment to study the potential impact of human engagement in tactical mitigation of delay in terminal area air taxi operations. The air taxi operations being considered herein is an advanced air transportation concept called Urban Air Mobility (UAM). The UAM concept aims to support not only air taxi operations, but also package delivery and emergency response among other use cases. The key innovation over current air transportation lies with the introduction of autonomous aircraft and autonomous air traffic management systems. Development of the UAM system will include transitional midterm phases where some operational services will be provided by a mixture of automation and human actors. Midterm operations present a unique challenge, since the scope of responsibility of automated systems is largely undefined, suggesting the need for direct human participation with little to inform how much human intervention is necessary. Here we assume that traffic management responsibilities require coordination between human actors and automated systems and focus on arrival flows for midterm operations. In the proposed human-in-the-loop simulation, virtual UAM traffic is strategically deconflicted by a Provider of Services for UAM at departure, then tactically managed by a human at the arrival facility. Generated traffic consists of UAM participants flying in UAM exclusive airspace structures, thus isolated from traditional traffic. The human operator is tasked with managing spacing of arrival traffic and executing speed adjustments as deemed necessary. We propose the investigation of three levels of automation assistance: 1) no assistance; 2) spacing violation detection; 3) spacing violation detection and speed adjustment recommendations. Quantitative measures like throughput and delay are used to assess the human's capacity for accommodating airborne delays. Qualitative evaluations such as surveys and open-ended feedback are used to gain insight into human factors. These factors could introduce additional capacity constraints on traffic, independent of physical or technical constraints. Although findings for this study will not be reported as the study has not yet been executed, we conclude with potential outcomes informed by previous simulations in the literature and suggestions for the structure and procedures of midterm human-automation air traffic management.

UAM↗
NASA NTRS2022

UAM Research – X4: Introduction to Community Based Rules (CBRs)

Recent advances in technology have enabled industry development of new and innovative vehicle types, offering lower operating costs and highly automated functionality that facilitates the introduction of new types of operations. These include low-altitude airspace operations with small Unmanned Aircraft Systems (UASs), short distance urban and intercity operations, and high- altitude Upper Class E operations. These and other new operations are expected to result in a much higher operational tempo than is currently experienced across the National Airspace System (NAS). The projected increase in operations, as well as the introduction of new aircraft form factors and supporting technologies—including increasing autonomy—will present challenges to the existing Air Traffic Management (ATM) system, which is currently unable to cost-effectively scale and deliver needed services. In response to these challenges and opportunities, a highly automated, cooperative environment incorporating a federated network has been envisioned and described through multiple operational concepts, depicting the future operating environment as part of the NAS. Foundational to the success of this future operating environment is the establishment of common business rules and understandings across relevant stakeholders, referred to as Community Based Rules (CBRs). Development, adoption, and implementation of CBRs will require collaboration across multiple stakeholders, including operators, support services (industry), and the Federal Aviation Administration (FAA), to identify and resolve a broad range of questions and challenges. Examples of these questions include “what rules are needed?”, “how are they expressed?”, and “how will they be managed?” This document identifies and describes an initial set of questions and considerations to be examined as efforts begin to create the innovative, automated, cooperative operating environment of the future. The goal is to establish a common frame of reference to support discussions and decisions regarding the future implementation of CBRs as part of the NAS.

Community Based Rules↗
NASA NTRSDate not supplied

A Concept of Operations (ConOps) of an In-time Aviation Safety Management System (IASMS) for Advanced Air Mobility (AAM)

The growth of new emerging operations involving Advanced Air Mobility (AAM) necessitates developing a perspective for an In-time Aviation Safety Management System (IASMS). This perspective advances from the National Academies report on IASMS and its recommendation for developing a Concept of Operations (ConOps) for IASMS. A ConOps has been developed for In-time System-Wide Safety Assurance (ISSA) from which the IASMS ConOps pivots to provide a robust scope commensurate with the broad vision defined by the National Academies. The IASMS ConOps focuses on emerging operations and spans innovations in Unmanned Aircraft System (UAS) and an increasingly complex ecosystem comprised of a widening mix of vehicles and technologies, Urban Air Mobility (UAM) with industry-federated services, traditional operations, as well as new supersonic aircraft and space launch systems.The challenge for the IASMS ConOps is to be broad to encompass innovations in the coming years and decades while agile to ensure levels of safety compatible with operational and certification requirements of the National Airspace System (NAS). The IASMS ConOps interweaves increasing complexity of operational safety capabilities and unlocking UAS Maturity Levels (UMLs). The relationships between increased complexity of automation and automated systems, fewer operators who are not as traditionally higher skilled, more complex operational environments, and aviation operations management with mixed aircraft and equipage pose a multi-dimensional space for IASMS capabilities essential for safety assurance and risk management. Instantiating IASMS capabilities and how they would be integral to AAM operations and increasing maturity of UAM could be accomplished through a series of Safety Demonstrators. These Safety Demonstrators could provide increased understanding and insight into use of controls for risk mitigation, means of compliance for certification, and operational experience with safety services such as in relation to contingency management. The IASMS capabilities can be viewed as initially residing with the vehicle, airspace, and Supplemental Data Service Provider (SDSP). For example, vehicle capabilities include communications including the command and control link, Remote Identification (ID), conflict advisory/alerting, and UAS system monitoring. These capabilities monitor and assess data such as battery health, aircraft state, and human performance. Complexity of ISAMS capabilities depends on a number of factors. These factors are intendedonly as a notional categorization with the purpose being to reflect the complexity of the AAM ecosystem that would drive up the complexity of ISSA capabilities including systems, sensors, models, standards, and controls. Factors could include the Vehicle Flight Management, Environment, Airspace, and Contingency Management. Each of these factors can be comprised of multiple sub-factors that contribute to increasing complexity. For example, Airspace at a lower level of complexity could be dedicated to UTM operations that are unmonitored, and at a higher level of complexity could involve mixed UTM and ATM operations. The IASMS concept includes safety services that provide data and information to different participants in AAM. The roles and responsibilities of participants can be defined using the Responsible-Accountable-Consulted-Informed (RACI) analysis. For example, for the safety service involving the Remote ID, the Operator would be accountable for providing the data, the Vehicle would be responsible for transmitting it, and the USS, SDSP, Vertiports, FIMS (FAA), and Public Entities such as safety services would be informed by receiving the data. The IASMS ConOps identifies the capabilities needed for risk mitigation and safety assurance in the increasingly complex national airspace. The ConOps serves as a pathway for engaging with industry to gain operational experience including through the Safety Demonstrator series, the RACI analysis, and operational complexity factors. The ConOps serves to integrate these different perspectives to build a cohesive and cogent approach to an AAM safety management system.

In-Time Aviation Safety Management System↗
NASA NTRS2014

Improving Early Adaptation Following Long Duration Spaceflight by Enhancing Vestibular Information

Crewmember adapted to the microgravity state may need to egress the vehicle within a few minutes for safety and operational reasons after g-transitions. The transition from one sensorimotor state to another consists of two main mechanisms: strategic and plastic-adaptive and have been demonstrated in astronauts returning after long duration space flight. Strategic modifications represent "early adaptation" -immediate and transitory changes in control that are employed to deal with short-term changes in the environment. If these modifications are prolonged then plastic-adaptive changes are evoked that modify central nervous system function, automating new behavioral responses. More importantly, this longer term adaptive recovery mechanism was significantly associated with their strategic ability to recover on the first day after return to Earth G. We are developing a method based on stochastic resonance (SR) to enhance information transfer by improving the brain's ability to detect vestibular signals especially when combined with balance training exercises for rapid improvement in functional skill, for standing and mobility. The countermeasure to improve post-flight balance and locomotor disturbances is a stimulus delivery system that is wearable/portable providing low imperceptible levels of white noise based binaural bipolar electrical stimulation of the vestibular system (stochastic vestibular stimulation, SVS). The techniques for improving signal detection using SVS may thus provide additional information to improve such strategic abilities and thus help in significantly reducing the number of days required to recover functional performance to preflight levels after long duration space flight. We have conducted a series of studies to document the efficacy of SVS stimulation on balance/locomotion tasks on unstable surfaces and motion tracking tasks during intra-vestibular system conflicts. In an initial study, we showed that SVS improved overall balance performance while standing on an unstable surface indicating that SVS may be sufficient to provide a comprehensive countermeasure approach for improving postural stability. In a second study, we showed that SVS improved locomotor performance on a treadmill mounted on an oscillating platform indicating that SVS may also be used to maximize locomotor performance during walking in unstable environments. In a third study, SVS was evaluated during an otolith-canal conflict scenario in a variable radius centrifuge at low frequency of oscillation (0.1 Hz) on both eye movements and perceptual responses (using a joystick) to track imposed oscillations. The variable radius centrifuge provides a selective tilting sensation that is detectable only by the otolith organs providing conflicting information from the canal organs of the vestibular system (intra-vestibular conflict). Results show that SVS significantly reduced the timing difference between both the eye movement responses as well as the perceptual tracking responses with respect to the imposed tilt sensations. These results indicate that SVS can improve performance in sensory conflict scenarios like that experienced during space flight. Such a SR countermeasure will act synergistically along with the pre-and in-flight adaptability training protocols providing an integrated, multi-disciplinary countermeasure capable of fulfilling multiple requirements making it a comprehensive and cost effective countermeasure approach to enhance sensorimotor capabilities following long-duration space flight.

Mulavara, Ajitkumar↗
NASA NTRSDate not supplied

National Campaign Partner Demonstration Team Annual Review April 2023

- NASA developed the Advanced Air Mobility Project (AAM) and the National Campaign (NC) series to identify and address challenges ahead for advanced air mobility concepts. - NC seeks to challenge industry as follows; - Execute progressively more difficult ecosystem-wide system-level safety and integration scenarios - Demonstrate practical and scalable system concepts - Build a knowledge base for development of requirements and standards - There are currently three focus areas within the NASA AAM NC Portfolio - Vehicle Development and Operations: test and inform capabilities that are critical enablers for AAM such as electric aircraft propulsion and increasing levels of automation - Airspace Design and Operations: develop and validate an operational concept to integrate and manage AAM traffic safely and efficiently - Community Integration: understand and address critical barriers to community integration, such as public acceptance (noise, security, privacy, etc.), supporting infrastructure, and local regulation

Eric N Becker↗
NASA NTRSDate not supplied

Hazard Perception and Avoidance (HPA) Overview

This short presentation is intended to provide RTCA Special Committee 147 (SC-147) members an overview of Advanced Air Mobility's Hazard Perception and Avoidance (HPA) technical area. This talk includes background on HPA's high-level objectives, as well as quick view of their upcoming simulation and flight test work. The presentation ends with plans for how HPA will coordinate with RTCA going forward.

automation↗
NASA NTRS2003

First Post-Flight Status Report for the Microgravity Science Glovebox

The Microgravity Science Glovebox (MSG) was launched to the International Space Station (ISS) this year on the second Utilization Flight (UF2). After successful on-orbit activation, the facility began supporting an active microgravity research program. The inaugural NASA experiments operated in the unit were the Solidification Using a Baffle in Sealed Ampoules (SUBSA, A. Ostrogorski, PI), and the Pore Formation and Mobility (PFMI, R. Grugel, PI) experiments. Both of these materials science investigations demonstrated the versatility of the facility through extensive use of telescience. The facility afforded the investigators with the capability of monitoring and operating the experiments in real-time and provided several instances in which the unique combination of scientists and flight crew were able to salvage situations which would have otherwise led to the loss of a science experiment in an unmanned, or automated, environment. The European Space Agency (ESA) also made use of the facility to perform a series of four experiments that were carried to the ISS via a Russian Soyuz and subsequently operated by a Belgium astronaut during a ten day Station visit. This imaginative approach demonstrated the ability of the MSG integration team to handle a rapid integration schedule (approximately seven months) and an intensive operations interval. Interestingly, and thanks to aggressive attention from the crew, the primary limitation to experiment thru-put in these early operational phases is proving to be the restrictions on the up-mass to the Station, rather than the availability of science operations.

Baugher, Charles R., III↗
NASA NTRSDate not supplied

Airspace Research and Development Portfolio Assessment of Urban Air Mobility using Knowledge Graph Data Science

National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of Urban Air Mobility (UAM) operations. The UAM vision is one in which advanced technologies and new operational procedures enable practical and cost-effective air transport as an integrated mode of movement of people and goods throughout metropolitan areas. To safely support UAM operations at scale in the National Airspace System (NAS), NASA’s Air Traffic Management-Exploration (ATM-X) project has been conducting research that evolves the UAM air traffic management system towards a highly automated and operationally flexible system of the future. The complexity of UAM airspace evolution to accommodate the increasing tempo of UAM operations over time is managed through the UAM airspace research roadmap, which is a system engineering approach to the R&D of complex system-of-systems, where system’s interdependencies make it nearly impossible to define requirements for individual elements of the system in isolation. These interdependencies form a knowledge graph (node-link network) with a highly complex structure far beyond the human user’s ability to extract insights for project management’s research portfolio assessment. This study applies advanced data analytics in knowledge graph to the UAM knowledge graph to facilitate the portfolio assessment.

ATM↗
NASA NTRSDate not supplied

Airspace Research and Development Portfolio Assessment of Urban Air Mobility using Knowledge Graph Data Science

National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of Urban Air Mobility (UAM) operations. The UAM vision is one in which advanced technologies and new operational procedures enable practical and cost-effective air transport as an integrated mode of movement of people and goods throughout metropolitan areas. To safely support UAM operations at scale in the National Airspace System (NAS), NASA’s Air Traffic Management-Exploration (ATM-X) project has been conducting research that evolves the UAM air traffic management system towards a highly automated and operationally flexible system of the future. The complexity of UAM airspace evolution to accommodate the increasing tempo of UAM operations over time is managed through the UAM airspace research roadmap, which is a system engineering approach to the R&D of complex system-of-systems, where system’s interdependencies make it nearly impossible to define requirements for individual elements of the system in isolation. These interdependencies form a knowledge graph (node-link network) with a highly complex structure far beyond the human user’s ability to extract insights for project management’s research portfolio assessment. This study applies advanced data analytics in knowledge graph to the UAM knowledge graph to facilitate the portfolio assessment.

ATM↗