Search NASA⌕ Search

SEARCH · Search NASA

Results for “collective autonomy”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

139 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Coordinating Multiple Spacecraft Assets for Joint Science Campaigns

This paper describes technology to support a new paradigm of space science campaigns. These campaigns enable opportunistic science observations to be autonomously coordinated between multiple spacecraft. Coordinated spacecraft can consist of multiple orbiters, landers, rovers, or other in-situ vehicles (such as an aerobot). In this paradigm, opportunistic science detections can be cued by any of these assets where additional spacecraft are requested to take further observations characterizing the identified event or surface feature. Such coordination will enable a number of science campaigns not possible with present spacecraft technology. Examples from Mars include enabling rapid data collection from multiple craft on dynamic events such as new Mars dark slope streaks, dust-devils or trace gases. Technology to support the identification of opportunistic science events and/or the re-tasking of a spacecraft to take new measurements of the event is already in place on several individual missions such as the Mars Exploration Rover (MER) Mission and the Earth Observing One (EO1) Mission. This technology includes onboard data analysis techniques as well as capabilities for planning and scheduling. This paper describes how these techniques can be cue and coordinate multiple spacecraft in observing the same science event from their different vantage points.

automated commanding↗
NASA NTRS1998

The Athena Mars Rover Science Payload

The Mars Surveyor missions that will be launched in April of 2001 will include a highly capable rover that is a successor to the Mars Pathfinder mission's Sojourner rover. The design goals for this rover are a total traverse distance of at least 10 km and a total lifetime of at least one Earth year. The rover's job will be to explore a site in Mars' ancient terrain, searching for materials likely to preserve a record of ancient martian water, climate, and possibly biology. The rover will collect rock and soil samples, and will store them for return to Earth by a subsequent Mars Surveyor mission in 2005. The Athena Mars rover science payload is the suite of scientific instruments and sample collection tools that will be used to perform this job. The specific science objectives that NASA has identified for the '01 rover payload are to: (1) Provide color stereo imaging of martian surface environments, and remotely-sensed point discrimination of mineralogical composition. (2) Determine the elemental and mineralogical composition of martian surface materials. (3) Determine the fine-scale textural properties of these materials. (4) Collect and store samples. The Athena payload has been designed to meet these objectives. The focus of the design is on field operations: making sure the rover can locate, characterize, and collect scientifically important samples in a dusty, dirty, real-world environment. The topography, morphology, and mineralogy of the scene around the rover will be revealed by Pancam/Mini-TES, an integrated imager and IR spectrometer. Pancam views the surface around the rover in stereo and color. It uses two high-resolution cameras that are identical in most respects to the rover's navigation cameras. The detectors are low-power, low-mass active pixel sensors with on-chip 12-bit analog-to-digital conversion. Filters provide 8-12 color spectral bandpasses over the spectral region from 0.4 to 1.1 micron Narrow-angle optics provide an angular resolution of 0.28 mrad/pixel, nearly a factor of four higher than that of the Mars Pathfinder and Mars Surveyor '98 cameras. Image compression will be performed using a wavelet compression algorithm. The Mini-Thermal Emission Spectrometer (Mini-TES) is a point spectrometer operating in -the thermal IR. It produces high spectral resolution (5 /cm) image cubes with a wavelength range of 5-40 gm, a nominal signal/noise ratio of 500:1, and a maximum angular resolution of 7 mrad (7 cm at a distance of 10 in). The wavelength region over which it operates samples the diagnostic fundamental absorption features of rockforming minerals, and also provides some capability to see through dust coatings that could tend to obscure spectral features. The mineralogical information that Mini-TES provides will be used to select from a distance the rocks and soils that will be investigated in more detail and ultimately sampled. Mini-TES is derived from the MO/MGS TES instrument, but is significantly smaller and simpler. The instrument uses an 8-cm Cassegrain telescope, a Michelson interferometer, and uncooled pyroelectric detectors. Along with its mineralogical capabilities, Mini-TES can provide information on the thermophysical properties of rocks and soils. Viewing upward, it can also provide temperature profiles through the martian atmospheric boundary layer. Elemental and Mineralogical Composition: Once promising samples have been identified from a distance using Pancam/Mini-TES, they will be studied in detail using up to three compositional sensors that can be placed directly against them by an Instrument Arm. The two compositional sensors, presently on the payload are an Alpha-Proton-X-Ray Spectrometer (APXS), and a Mossbauer Spectrometer. The APXS is derived closely from the instrument that flew on Mars Pathfinder. Radioactive alpha sources and three detection modes (alpha, proton, and x-ray) provide elemental abundances of rocks and soils to complement and constrain mineralogical data. The Athena APXS will have a revised mechanical design that will cut down significantly on backscattering of alpha particles from martian atmospheric carbon. It will also include a target of known elemental composition that will be used for calibration purposes. The Athena Mossbauer Spectrometer is a diagnostic instrument for the mineralogy and oxidation state of Fe-bearing phases, which are particularly important on Mars. The instrument measures the resonant absorption of gamma rays produced by a Co-57 source to determine splitting of nuclear energy levels in Fe atoms that is related to the electronic environment surrounding them. It has been under development for space flight for many years at the Technical University of Darmstadt. The Mossbauer Spectrometer (and the other arm instruments) will be able to view a small permanent magnet array that will attract magnetic particles in the martian soil. The payload may also include a Raman Spectrometer. If included, the Raman Spectrometer will provide precise identification of major and minor mineral phases. It requires no sample preparation, and is also sensitive to organics. Fine-Scale Texture: The Instrument Arm a also carries a Microscopic Imager that will obtain high-resolution monochromatic images of the same materials for which compositional data will be obtained. Its spatial resolution is 20 micron/pixel over a 1 cm depth of field, and 40 micron/pixel over a 1-cm depth of field. Like Pancam, it uses the same active pixel sensor detectors and electronics as the rover's navigation cameras. The Instrument Arm is a three degree-of-freedom arm that uses designs and components from the Mars Pathfinder and Mars Surveyor '98 projects. Its primary function is instrument positioning. Along with the instruments noted above, it also carries a brush that can be used to remove dust and other loose coatings from rocks. Sample Collection and Storage: Martian rock and soil samples will be collected using a low-power rotary coring drill called the Mini-Corer. An important characteristic of this device is that it can obtain intact samples of rock from up to 5 cm within strong boulders and bedrock, Nominal core dimensions are 8xl7 mm. The Mini-Corer drills a core to the commanded depth in a rock, shears it off, retains it, and extracts it. It can also acquire samples of loose soil, using soil sample cups that are pressed downward into loose material. The Mini-Corer can drill at angles from vertical to 45' off vertical. It has six interchangeable bits for long life. Mechanical damage to the sample during drilling is minimal, and heating is negligible. After acquisition, the sample may be viewed by the arm instruments, and/or placed in one of 104 compartments in the Sample Container. A subset of the acquired samples may be replaced with other samples obtained later if desired. The Sample Container has no moving parts, and is mounted external to the rover for easy removal by the Mars Surveyor 2005 flight system. Operation of the rover will make extensive use of automated onboard navigation and hazard avoidance capabilities. Otherwise, use of onboard autonomy is minimal. Data downlink capability is about 40 Mbit/sol, and the use of the Mars Surveyor '01 orbiter for data relay imposes a limit of at most two command cycles per sol. Because of the significant amount of time available between command cycles, all payload elements will be operated sequentially, rather than in parallel.; this approach also significantly simplifies operations and minimizes peak power usage. The landing site for the '01 rover has not been selected yet. Site selection will make as full use as possible of Mars Global Surveyor data, and will involve substantial input from the broad Mars science community. Summary: The following table describes the mass, power, providers, and key scientific objectives of all the major elements of the Athena payload. Additional Athena payload information may be found at: http://astrosun.tn.cornell.edu/athena/index.html. Additional information contained in the original.

Squyes, S. W.↗
NASA NTRS2004

Robotic Subsurface Analyzer and Sample Handler for Resource Reconnaissance and Preliminary Site Assessment for ISRU Activities at the Lunar Cold Traps

Since the 1960s, claims have been made that water ice deposits should exist in permanently shadowed craters near both lunar poles. Recent interpretations of data from the Lunar Prospector-Neutron Spectrometer (LP- NS) confirm that significant concentrations of hydrogen exist, probably in the form of water ice, in the permanently shadowed polar cold traps. Yet, due to the large spatial resolution (45-60 Ian) of the LP-NS measurements relative to these shadowed craters (approx.5-25 km), these data offer little certainty regarding the precise location, form or distribution of these deposits. Even less is known about how such deposits of water ice might effect lunar regolith physical properties relevant to mining, excavation, water extraction and construction. These uncertainties will need to be addressed in order to validate fundamental lunar In Situ Resource Utilization (ISRU) precepts by 2011. Given the importance of the in situ utilization of water and other resources to the future of space exploration a need arises for the advanced deployment of a robotic and reconfigurable system for physical properties and resource reconnaissance. Based on a collection of high-TRL. designs, the Subsurface Analyzer and Sample Handler (SASH) addresses these needs, particularly determining the location and form of water ice and the physical properties of regolith. SASH would be capable of: (1) subsurface access via drilling, on the order of 3-10 meters into both competent targets (ice, rock) and regolith, (2) down-hole analysis through drill string embedded instrumentation and sensors (Neutron Spectrometer and Microscopic Imager), enabling water ice identification and physical properties measurements; (3) core and unconsolidated sample acquisition from rock and regolith; (4) sample handling and processing, with minimized contamination, sample containerization and delivery to a modular instrument payload. This system would be designed with three mission enabling goals, including: (1) a self-contained, low power, low mass, "black box'' configuration for operations from a lander, various classes of rovers or a surface-based platform with human assistance or robotic anchoring mechanisms; (2) reconfigurable and scalable sample handling for delivery to various types of instrumentation, depending on mission requirements; and (3) the use of advanced automation control and diagnostic techniques that will afford local human deployed, remote teleoperation and fully autonomous intelligent operations. Though a great deal of technology has been advanced toward these objectives, the SASH system faces significant design challenges, including the low gravity environment, various levels of autonomy in operations, radiation exposure, dust contamination, and temperature extremes and deltas. Significant input from the scientific and engineering communities, as well as a significant environmental testing program, will be required to guide the design process.

Gorevan, S. P.↗
NASA NTRS2014

Exploration Medical System Demonstration

BACKGROUND: Exploration class missions will present significant new challenges and hazards to the health of the astronauts. Regardless of the intended destination, beyond low Earth orbit a greater degree of crew autonomy will be required to diagnose medical conditions, develop treatment plans, and implement procedures due to limited communications with ground-based personnel. SCOPE: The Exploration Medical System Demonstration (EMSD) project will act as a test bed on the International Space Station (ISS) to demonstrate to crew and ground personnel that an end-to-end medical system can assist clinician and non-clinician crew members in optimizing medical care delivery and data management during an exploration mission. Challenges facing exploration mission medical care include limited resources, inability to evacuate to Earth during many mission phases, and potential rendering of medical care by non-clinicians. This system demonstrates the integration of medical devices and informatics tools for managing evidence and decision making and can be designed to assist crewmembers in nominal, non-emergent situations and in emergent situations when they may be suffering from performance decrements due to environmental, physiological or other factors. PROJECT OBJECTIVES: The objectives of the EMSD project are to: a. Reduce or eliminate the time required of an on-orbit crew and ground personnel to access, transfer, and manipulate medical data. b. Demonstrate that the on-orbit crew has the ability to access medical data/information via an intuitive and crew-friendly solution to aid in the treatment of a medical condition. c. Develop a common data management framework that can be ubiquitously used to automate repetitive data collection, management, and communications tasks for all activities pertaining to crew health and life sciences. d. Ensure crew access to medical data during periods of restricted ground communication. e. Develop a common data management framework that allows for scalability, extensibility, and interoperability of data sources and data users. f. Lower total cost of ownership for development and sustainment of peripheral hardware and software that use EMSD for data management. g. Provide a better standard of healthcare for crew members through reductions in the time required by crew and ground personnel to provide medical treatment and the number of crew errors experienced during treatment.

Rubin, D. A.↗
NASA NTRSDate not supplied

Reinforcement Learning for Spacecraft Navigation & Environment Characterization in the Planar-Restricted Two-Body Problem

As science, exploration, and commercial space missions become increasingly complex, so does the need for efficient, autonomous, and integrated spacecraft navigation and operations techniques. Key operational functions, including data collection and transmission, environment characterization, systems constraints, human factors, and navigation, often are intertwined and conflicted. Deep Reinforcement Learning (DRL) offers a framework for addressing integrated spacecraft navigation and planning in an uncertain dynamical environment. The goal of this study is to evaluate the utility of DRL for integrated spacecraft navigation and planning. This is achieved by developing a simple environmental characterization training environment in the Planar-Restricted 2-Body Problem (PR2BP), establishing benchmarks and heuristic baselines, and designing a previously unstudied Markov Decision Process (MDP) formulation. This MDP formulation enables the spacecraft DRL agents to appropriately balance navigation and actuation capabilities. The resulting DRL-derived policy exceeds a random or untrained policy and meets or exceeds the level of performance of a heuristic without actuation. In the process, valuable intuition is gained about the problem with insight into how DRL methods could scale to increasingly more realistic scenarios, including net-work design and training architectures, efficient state space representations, and methods for encouraging exploration in a parametric action space, among others.

navigation↗
NASA NTRSDate not supplied

Machine Learning based Aircraft Performance Model Estimation for Trajectory Prediction

The accurate prediction of aircraft trajectory by ground-based decision support tools is a critical component of air traffic management in the US National Airspace System (NAS). Accurate predictions of where the aircraft will be in the future or when they will arrive at specific locations (e.g., fixes) is a key enabler for sequencing and efficient arrival management of flights. Traditional physics based aircraft trajectory prediction relies on a simplified point-mass total energy model whose parameters are referred to as Aircraft Performance Model (APM) parameters. Even though the performance coefficients and weight of an aircraft are a vital part of the aircraft performance model’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to decision-support tools. Current approaches freeze some coefficients to default base of aircraft data (BADA) values and optimize others. However, the APM parameters are highly coupled by the flight dynamics and prioritizing one parameter over others leads to bias and skewed predictions. To alleviate this problem, we provide a combined optimization framework to predict all the critical (thrust, drag and weight) APM parameters. This paper is focused on training Machine Learning (ML) models that map historical flights to optimized APM parameters that provide the best fit (in terms of prediction error). Our dataset obtained from NASA’s Sherlock data warehouse is comprised of thousands of historical flights and includes weather and track data collected from 2019. Using different subsets of relevant features (e.g., aircraft type), we trained several ML models to estimate the aircraft’s take off weight, drag polar coefficients (both parasitic and lift induced), and thrust settings (multiplier applied to the maximum engine thrust). The chosen flights are from three of the most common aircraft types (B738, B737, and A320) arriving at four airports (LAX, DEN, MSP, and DFW). Our ML approach is comprised of two different solutions: 1- using a subset of features that are known prior to the flight departure and do not change during flight (such as engine type, current temperature at departure & destination airports, aircraft type) and 2 - using a subset of temporal features of the flight trajectory (such as cruise altitude, Mach, airspeed, and rate of climb) in addition to the pre-departure features from the first solution. The labels or target variables are the APM parameters that were obtained by an optimized ordinary differential equations (ODE) fitting process (applied to individual flights). The ODE-fitting is very time intensive and is therefore performed offline. Thus, training an ML model to learn the relationship between the flight features and ODE-generated labels enables faster estimation of the APM parameters and is therefore amenable to real-time prediction. Various ML models including linear regression, random forest, XGBoost, and neural network were trained, and the results are compared. After model validation and hyperparameter-tuning, we observed that the Random Forest model outperformed the other three models by the overall mean square error (MSE) of 2% for the first solution and 1.5% for the second solution. Finally, the ML-derived parameters are compared against default BADA APM parameters using NASA’s Autonomy Development toolkit (ADK) simulation software. The simulation results for one of each aircraft type is shown and discussed.

Aida Sharif Rohani↗
NASA NTRSDate not supplied

A Remote, Human-in-the-Loop Evaluation of a Multiple-Drone Delivery Operation

Over time, advances in unmanned aircraft systems (UAS) have enabled a shift in the operational paradigm from one operator managing one aircraft to that of multiple operators working together to manage multiple aircraft. This shift has highlighted the need for effective human-autonomy teaming methods to maintain manageable workload levels for operators as well as high standards of system performance and safety. This paper presents a study aimed at evaluating whether automation can help operators manage workload during small UAS (sUAS) package delivery scenarios featuring contingency situations. These contingency situations, resulting from unplanned UAS Volume Reservations (UVRs), required flight path reroutes for multiple aircraft simultaneously. The study manipulated the number of aircraft affected by the UVRs and the level of automation support. The presence of terrain conflicts was also controlled within each scenario. Due to the COVID-19 pandemic, subjects were not able to gain direct access to the Ground Control System (GCS). Therefore, the study was conducted using a subject-surrogate paradigm that required subjects to relay commands through a verbal protocol from remote locations outside of the lab to a researcher surrogate who had direct control of the GCS interfaces at the lab location. Results show that the automated support condition was associated with faster reroute response times, more efficient reroute maneuvers, and significantly lower levels of perceived workload than the manual reroute condition. However, the automation support level did not significantly impact pilots’ ability to avoid the UVR successfully; pilots were overwhelmingly capable of avoiding the UVR in all conditions. The presence of terrain conflicts primarily impacted pilot performance by leading to multiple uploads per vehicle, which was not typically required when pilots only needed to maneuver laterally. Although subjects did not have direct control over the GCS, subjective ratings indicate that the displays under test provided them with sufficient information to manage their aircraft and promptly respond to the unplanned UVRs. Overall, the objective and subjective data strongly suggest that the verbal protocol and subject-surrogate paradigm were effective methods for collecting data remotely amid the COVID-19 pandemic.

multi-UAS↗
NASA NTRSDate not supplied

Monitoring Human Performance on Future Deep Space Missions Abstract

NASA and the commercial spacecraft community are working diligently to put the first woman on the moon in the 2024 timeframe. At the same time, NASA researchers are thinking about how to solve the even larger challenges that future deep space missions will bring. Space travel itself is difficult, but astronauts on deep space missions will face obstacles and unknowns never before experienced. In addition to the altered gravity and hostile/closed environment of a spacecraft, deep space crews will face increased radiation, isolation, and distance from Earth. During Extravehicular Activity (EVA, or “spacewalk”) operations, crew will experience increased physical and cognitive workload due to extended types, frequencies and durations of tasks performed on exploration missions in partial gravity environments. All of these stressors will impact crew physical and mental health and performance in difficult-to-anticipate ways. Crew autonomy may be one of the biggest challenges faced. Communication delays and blackouts will occur, and in those situations, the crew may not have access to the Mission Control Center (MCC). They may be forced to be solely dependent on each other and the available information onboard to stay alive, healthy, and achieve the mission. The only conceivable way to meet the challenges of Earth independence is to enable the crew to monitor their own health and performance -- preferably unobtrusively as they perform their duties. Technologies and techniques must be developed to aid the crew in these assessments. A deep space mission is expected to have relatively short periods of high cognitive demand, stress, and fatigue, alongside potentially long periods of cognitive underload during the transit, where boredom, loneliness, and depression can set in. Both ends of this spectrum are dangerous. Crew must be made aware when their task performance drops significantly, when their cognitive workload is too high, when they have lost situation awareness, or when they are too stressed or too fatigued to perform well. They must be able to identify these risks, and then mitigate them with countermeasures available onboard. A number of self-monitoring technologies are presently being explored by NASA to advance crew state determination capabilities. These range from real-time, physiological workload and situation awareness assessments, to crew health measurements determining physical and mental fitness for duty, to task performance metrics such as suit resource expenditures. For EVA tasks during surface exploration missions, biomedical information such as metabolic rate may be provided to crewmembers for situational awareness related to task performance efficiency. In addition, translation distances, hydration, nutrition, inspired CO2 exposure and other consumables usage rates may be useful input metrics for modeling individualized performance during tasks to inform crew or provide estimates of work efficiency. Oculomotor metrics such as gaze dwell time, pupilometry, and eye tracking collected in advanced helmet mounted displays could potentially be used to characterize crew situation awareness. This paper highlights some of these projects, and provides broader discussion about the need for advanced monitoring and smart technologies, as NASA takes the leap into the next generation of space exploration.

Kritina Holden↗
NASA NTRSDate not supplied

Future Homes in Space: Development of Concepts for Exploration Space Habitats

NASA’s Artemis campaign seeks to return humans to the moon and establish a sustained presence on the lunar surface. This session will emphasize how habitation capabilities on the moon and in cislunar space can potentially contribute to the sustainability objectives of Artemis. Habitable elements represent opportunities to enable longer duration stays, increase the number of crew members present, enhance science and utilization activities, drive technology development for future Mars exploration, perform analog missions, and fuel economic opportunities for US industry. Panelists include Paul Kessler (NASA Marshall Space Flight Center, deputy lead for lunar surface habitation); Andrew Choate (NASA Marshall Space Flight Center, Mars habitation lead); Krystofer Dudzinski (NASA Marshall Space Flight Center, a space architect within the MSFC Advanced Concepts Office); and Larry Toups (retired from NASA Johnson Space Center, currently an adjunct professor at University of Houston in space architecture). The panel is moderated by Tracie Prater (NASA Marshall Space Flight Center, Habitation Systems Development Office). The panel will begin with an overview of the history of habitation concepts and an academic perspective on general considerations in space habitat design (Larry Toups). Paul Kessler and Andrew Choate will introduce NASA’s principle of “architecting from the right” to help define objectives for Artemis missions, needs/characteristics, use cases, and functions (as published in the agency’s Architecture Definition Document) and provide perspective on how this principle informs habitation concept development work. NASA panelists will discuss key engineering challenges identified for developing, deploying, and operating habitable assets on the lunar surface and/or in deep space. These may include dust mitigation, outfitting of inflatable softgoods (for concepts which may use softgoods as a primary structural material), survival in lunar darkness, human health and performance considerations, maintenance/repair/sparing, and autonomy. These identified challenges represent risks for habitation systems development and relate closely to capability gaps identified by the agency. While the work of NASA Marshall Space Flight Center’s habitation development office is primarily focused on habitats which are launched from earth pre-integrated (referred to as Class I in the framework previously developed by NASA space architects Kennedy/Cohen) or launched from earth and deployed at the point of use (Class II), there is also extensive work in NASA, academia, and companies on constructed habitats, which would be built on a planetary surface using indigenous resources (Class III habitats). Panelist Krystopher Dudzinski will discuss potential evolutionary pathways from Class I and Class II habitats to Class III habitats, unique and common architectural challenges within each habitat class, and key gaps in implementing Class III habitats from an architectural perspective. NASA panelists and the moderator will also provide an overview of partnership opportunities and avenues for further engagement to advance habitation systems for the SpaceCom audience. NASA is currently developing notional concepts for a lunar surface habitat and Mars transit habitat, which will be discussed during this panel session and used as examples. These concepts represent options for habitation system design and are a point of departure. They do not represent a final plan or formal recommendation on the part of the agency. Based on the most recent analysis cycle, NASA’s lunar surface habitat (SH) concept nominally supports two crew members for 30 days, with the capacity to support four crew during a surge period where crew will swap between the SH and another surface asset, such as a pressurized rover. This example design has a metallic airlock for ingress/egress and the upper portion is an inflatable material system which serves as the habitation module. The notional interior of the habitat is a three-deck layout/configuration which supports all crew mission functions, including exercise, stowage, extravehicular activity (EVA), sleep, hygiene waste collection, maintenance and repair, and meal preparation. Under analysis assumptions for habitation, the Mars Transit Habitat (TH) concept would support four crew on an up to 1,200 day Mars mission. One option for the concept is to initially dock Transit Habitat at Gateway, where it can be used to increase the duration of crew stays in cislunar space and perform shakedown and analog missions prior to a Mars departure. One challenge in longer duration missions which involve both surface exploration and transit is understanding crew adaptation when transitioning between partial gravity and microgravity environments. TH at Gateway offers an opportunity to study this transition and in doing so reduce risks associated with future Mars exploration. Like lunar SH, the most recent analysis cycle concept of a Mars TH is a hybrid structure design, with a metallic section supporting EVAs, axial/radial docking, and Safe Haven capabilities, and an inflatable softgoods structure for the primary habitation function. Interior layouts to optimize crew usability and livability are currently under trade. The panel will include presentation material, but also seeks to engage the audience in a highly interactive conversation regarding the potential role for habitation in future exploration initiatives. Potential topics for discussion include the influence of the crew experience on habitation systems design and livability/usability considerations, the benefits of space habitation development in terrestrial applications, and challenges and opportunities in “feeding forward” lunar surface habitation systems development to Mars exploration.

space habitats↗
NASA NTRSDate not supplied

Flight Testing of In-Time Safety Assurance Technologies for UAS Operations

Ongoing research at NASA is driven by a strategic plan defined by the Aeronautics Research Mission Directorate and a vision for future In-Time Aviation Safety Management Systems (IASMS) as described by the National Academies. In both visions, system safety awareness and provision are expanded through increased access to relevant data; integrated analysis and predictive capabilities; improved real-time detection and alerting of domain-specific hazards; decision support, and in some cases, automated risk mitigation strategies. One primary research focus is to develop means by which more timely (i.e., “in-time”) actions may be taken to mitigate precursors, anomalies, or trends that are observed during operations. In this paper, we describe such means as a collection of Services, Functions, and Capabilities (SFCs) that are supported by an underlying information system. For example, an integrated risk assessment capability is envisioned that continuously monitors safety-related metrics and margins and recommends timely operational changes. Assessment functions and/or services can be based on data analytics and predictive models derived from heterogeneous data sets that span relevant indicator metrics and their time histories. Likewise, on-board functions can identify and reduce susceptibility to precursor conditions that have led (and can lead) to aircraft loss-of-control or out-of-control accidents. This paper summarizes development and testing of such an information system tailored to hazards anticipated for future highly autonomous flight missions near and over densely populated areas. Testing is accomplished via simulation and by using small, unmanned aircraft operating over a test range at NASA’s Langley Research Center. Flight plans and test scenarios are defined to emulate several use-cases, including package delivery; reconnaissance; fire management; and urban air taxi vertiport operations. Two test phases are summarized with Phase 1 occurring in (2019-2020) and Phase 2 ongoing (2021-present). Results focus on SFC performance, technology readiness level assessment, and requirements discovery/validation. Companion papers are cited throughout for additional details on the recent testing.

safety management↗
NASA NTRS2015

Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team

A team is defined as: "two or more individuals who interact socially and adaptively, have shared or common goals, and hold meaningful task interdependences; it is hierarchically structured and has a limited life span; in it expertise and roles are distributed; and it is embedded within an organization/environmental context that influences and is influenced by ongoing processes and performance outcomes" (Salas, Stagl, Burke, & Goodwin, 2007, p. 189). From the NASA perspective, a team is commonly understood to be a collection of individuals that is assigned to support and achieve a particular mission. Thus, depending on context, this definition can encompass both the spaceflight crew and the individuals and teams in the larger multi-team system who are assigned to support that crew during a mission. The Team Risk outcomes of interest are predominantly performance related, with a secondary emphasis on long-term health; this is somewhat unique in the NASA HRP in that most Risk areas are medically related and primarily focused on long-term health consequences. In many operational environments (e.g., aviation), performance is assessed as the avoidance of errors. However, the research on performance errors is ambiguous. It implies that actions may be dichotomized into "correct" or "incorrect" responses, where incorrect responses or errors are always undesirable. Researchers have argued that this dichotomy is a harmful oversimplification, and it would be more productive to focus on the variability of human performance and how organizations can manage that variability (Hollnagel, Woods, & Leveson, 2006) (Category III1). Two problems occur when focusing on performance errors: 1) the errors are infrequent and, therefore, difficult to observe and record; and 2) the errors do not directly correspond to failure. Research reveals that humans are fairly adept at correcting or compensating for performance errors before such errors result in recognizable or recordable failures. Astronauts are notably adept high performers. Most failures are recorded only when multiple, small errors occur and humans are unable to recognize and correct or compensate for these errors in time to prevent a failure (Dismukes, Berman, Loukopoulos, 2007) (Category III). More commonly, observers record variability in levels of performance. Some teams commit no observable errors but fail to achieve performance objectives or perform only adequately, while other teams commit some errors but perform spectacularly. Successful performance, therefore, cannot be viewed as simply the absence of errors or the avoidance of failure Johnson Space Center (JSC) Joint Leadership Team, 2008). While failure is commonly attributed to making a major error, focusing solely on the elimination of error(s) does not significantly reduce the risk of failure. Failure may also occur when performance is simply insufficient or an effort is incapable of adjusting sufficiently to a contextual change (e.g., changing levels of autonomy).

Landon, Lauren Blackwell↗
NASA NTRSDate not supplied

A Science-Focused Artificial Intelligence (AI) Responding in Real-Time to New Information: Capability Demonstration for Ocean World Missions

Introduction: Artificial intelligence (AI) has long been considered a potential mechanism to explore increasingly challenging environments, including those with extreme temperatures and pressures, limited communication capabilities, or those with demanding terrain. We posit that missions in extreme environments could deploy an onboard AI focused on science observations and goals in order to augment a traditional concept(s) of operations (ConOps). An onboard AI capability could perform functions such as data analysis in order to make high-level decisions, including prioritized data transmission for analysis by ground-based teams or autonomously-guided follow-on analyses that maximize science return. Such a capability would empower missions to respond to scientific data of interest in real-time; a mission could make observations and perform a preliminary analysis to alert ground-based scientists to an observation of interest, enabling an informed, rapid response from Earth-based teams. Enceladus Case Study for Onboard AI: We are developing an onboard AI capability for real-time telemetry response that formulates and carries-out informed decisions in service to established mission goals, enabling increased science return of a mission. We focus our AI development for use on a constellation of SmallSats orbiting Enceladus. Our Enceladus case study tests autonomous decision-making capabilities in scenarios with complex orbital dynamics, plume ejecta, extreme cold environments, power restrictions, and a requirement to maximize science return for a potential positive detection of life, while critically evaluating the potential for false positives. Telemetry includes simulated scientific data, spacecraft onboard operational data (e.g., position, velocity, and rotation), and engineering hardware performance data. Enceladus SmallSat Constellation. Our constellation includes eight SmallSat spacecraft in an 8:35 resonant orbit-based formation, leveraging Saturn’s gravitational forces to maintain stable orbits with global coverage around Enceladus. To our knowledge, we simulate the first stable configuration of multiple spacecraft in closed orbits around Enceladus, using a full ephemeris force model (Russell and Lara, 2009). Each spacecraft’s orbit will precess, causing an eastward ground track shift (from an orbiter’s perspective) of each spacecraft for each orbit. However, all spacecraft return to their original positions relative to Enceladus after eight Enceladus revolutions around Saturn. We model communication pathways between SmallSats to understand how information would need to be transmitted across the constellation to enable AI-driven decision-making and resource allocation across the fleet. Capability Demonstration. Our simulated capability demonstration inputs position, velocity, and rotation telemetry from our Enceladus-focused constellation simulations, and mass spectrometry data collected from abiotic and biotic laboratory-analog ocean world experiments (Theiling et al., 2018; Theiling, 2021; Da Poian et al., 2023). Data from these experiments are used to simulate MS measurements and different scenarios of science observations for onboard analysis performed on each of the eight spacecraft. For these demonstrations, we integrate 24 machine learning (ML) algorithms into an onboard intelligence as a ‘knowledge base’, including algorithms evaluating data quality and those predicting (with % confidence) gas composition, ocean aqueous chemistry, and whether the sample was influenced by microbial life. The onboard AI capability is designed to use the knowledge base to come to a consensus-based decision in the interpretation of the observed data in order to request additional action outside of a pre-defined ConOps. Requested actions could include e.g., prioritized downlink to Earth (for analysis by ground-based teams) or follow-on analyses performed across the constellation. The spacecraft’s intelligent onboard planner must then determine whether sufficient resources (e.g., time, power, etc.) are available and weigh the request with mission priorities. In our simulation, the constellation is able to identify potential biosignatures using onboard ML algorithms, evaluate the confidence of that prediction, and perform follow-on analyses across the fleet to confirm the detection, in order to best prepare a transmission of these data to Earth-based teams.

astrobiology↗
NASA NTRS2012

Science Benefits of Onboard Spacecraft Navigation

Primitive bodies (asteroids and comets), which have remained relatively unaltered since their formation, are important targets for scientific missions that seek to understand the evolution of the solar system. Often the first step is to fly by these bodies with robotic spacecraft. The key to maximizing data returns from these flybys is to determine the spacecraft trajectory relative to the target body-in short, navigate the spacecraft- with sufficient accuracy so that the target is guaranteed to be in the instruments' field of view. The most powerful navigation data in these scenarios are images taken by the spacecraft of the target against a known star field (onboard astrometry). Traditionally, the relative trajectory of the spacecraft must be estimated hours to days in advance using images collected by the spacecraft. This is because of (1)!the long round-trip light times between the spacecraft and the Earth and (2)!the time needed to downlink and process navigation data on the ground, make decisions based on the result, and build and uplink instrument pointing sequences from the results. The light time and processing time compromise navigation accuracy considerably, because there is not enough time to use more accurate data collected closer to the target-such data are more accurate because the angular capability of the onboard astrometry is essentially constant as the distance to the target decreases, resulting in better "plane-of- sky" knowledge of the target. Excellent examples of these timing limitations are high-speed comet encounters. Comets are difficult to observe up close; their orbits often limit scientists to brief, rapid flybys, and their coma further restricts viewers from seeing the nucleus in any detail, unless they can view the nucleus at close range. Comet nuclei details are typically discernable for much shorter durations than the roundtrip light time to Earth, so robotic spacecraft must be able to perform onboard navigation. This onboard navigation can be accomplished through a self- contained system that by eliminating light time restrictions dramatically improves the relative trajectory knowledge and control and subsequently increases the amount of quality data collected. Flybys are one-time events, so the system's underlying algorithms and software must be extremely robust. The autonomous software must also be able to cope with the unknown size, shape, and orientation of the previously unseen comet nucleus. Furthermore, algorithms must be reliable in the presence of imperfections and/or damage to onboard cameras accrued after many years of deep-space operations. The AutoNav operational flight software packages, developed by scientists at the Jet Propulsion Laboratory (JPL) under contract with NASA, meet all these requirements. They have been directly responsible for the successful encounters on all of NASA's close-up comet-imaging missions (see Figure !1). AutoNav is the only system to date that has autonomously tracked comet nuclei during encounters and performed autonomous interplanetary navigation. AutoNav has enabled five cometary flyby missions (Table!1) residing on four NASA spacecraft provided by three different spacecraft builders. Using this software, missions were able to process a combined total of nearly 1000 images previously unseen by humans. By eliminating the need to navigate spacecraft from Earth, the accuracy gained by AutoNav during flybys compared to ground-based navigation is about 1!order of magnitude in targeting and 2!orders of magnitude in time of flight. These benefits ensure that pointing errors do not compromise data gathered during flybys. In addition, these benefits can be applied to flybys of other solar system objects, flybys at much slower relative velocities, mosaic imaging campaigns, and other proximity activities (e.g., orbiting, hovering, and descent/ascent).

Autonomy↗