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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 NTRS2020

Design of Autonomous Medical Response Agent (AMRA) Aggregate Information Dashboard (AID)

Future astronauts in deep space missions will rely on tools and technologies empowering them to self-diagnose and self-treat medical conditions. Given communications delays and limited bandwidth in future long-duration exploration missions (LDEMs), medical decision support technologies must empower the crew to manage routine medical activities, acute medical incidents, as well as emergency medical scenarios independently from ground support.The Autonomous Medical Response Agent (AMRA) is envisioned as a digital tool enabling crew to issue medical complaints and interact with a medical decision support algorithm which develops a differential diagnosis and recommends a treatment protocol for the condition. AMRA will draw from individual crew medical history in addition to crew symptoms to more efficiently identify high-risk medical conditions. A new symptom could be indicative of a chronic condition or a normal adaptation to long-duration spaceflight, but could just as easily be indicative of an adverse vehicle condition affecting the entire crew.While real-time communication with a flight surgeon may not possible, the crew will nonetheless require a means to communicate and document both routine and emergency medical incidents to ground support. Conversely, flight surgeons and medical specialists on the ground will need to understand information such as crew vitals or responses to medical check-ups and examinations within the larger context of crew schedule, mission activities, and vehicle performance. A user interface which establishes communication protocols between an individual crew member and AMRA, as well as ground support to the crew is a significant area of research demanding input and consideration.The design of AMRA AID is intended to: a) represent routine medical activities as well as new (unplanned) medical incidents within the larger context of crew schedule and mission activities, and b) increase confidence between ground support and crew members over the course of LDEMs. Maintaining situation awareness of unplanned medical incidents between ground and crew will be a critical element within LDEMs. Two medical incidents headache and difficulty breathing are being explored within a user interface prototype which captures communications protocols between crew members and mission control, human health monitoring, vehicle or environmental monitoring, as well as crew schedule and mission activities holistically.

Yashar, M.↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

Jan Stupl↗

NASA NTRS1993

Multi-beam range imager for autonomous operations

For space operations from the Space Station Freedom the real time range imager will be very valuable in terms of refuelling, docking as well as space exploration operations. For these applications as well as many other robotics and remote ranging applications, a small potable, power efficient, robust range imager capable of a few tens of km ranging with 10 cm accuracy is needed. The system developed is based on a well known pseudo-random modulation technique applied to a laser transmitter combined with a novel range resolution enhancement technique. In this technique, the transmitter is modulated by a relatively low frequency of an order of a few MHz to enhance the signal to noise ratio and to ease the stringent systems engineering requirements while accomplishing a very high resolution. The desired resolution cannot easily be attained by other conventional approaches. The engineering model of the system is being designed to obtain better than 10 cm range accuracy simply by implementing a high precision clock circuit. In this paper we present the principle of the pseudo-random noise (PN) lidar system and the results of the proof of experiment.

Marzwell, Neville I.↗

NASA NTRS2011

Lunabotics Mining Competition: Inspiration Through Accomplishment

NASA's Lunabotics Mining Competition is designed to promote the development of interest in space activities and STEM (Science, Technology, Engineering, and Mathematics) fields. The competition uses excavation, a necessary first step towards extracting resources from the regolith and building bases on the moon. The unique physical properties of lunar regolith and the reduced 1/6th gravity, vacuum environment make excavation a difficult technical challenge. Advances in lunar regolith mining have the potential to significantly contribute to our nation's space vision and NASA space exploration operations. The competition is conducted annually by NASA at the Kennedy Space Center Visitor Complex. The teams that can use telerobotic or autonomous operation to excavate a lunar regolith geotechnical simulant, herein after referred to as Black Point-1 (or BP-1) and score the most points (calculated as an average of two separate 10-minute timed competition attempts) will eam points towards the Joe Kosmo Award for Excellence and the scores will reflect ranking in the on-site mining category of the competition. The minimum excavation requirement is 10.0 kg during each competition attempt and the robotic excavator, referred to as the "Lunabot", must meet all specifications. This paper will review the achievements of the Lunabotics Mining Competition in 2010 and 2011, and present the new rules for 2012. By providing a framework for robotic design and fabrication, which culminates in a live competition event, university students have been able to produce sophisticated lunabots which are tele-operated. Multi-disciplinary teams are encouraged and the extreme sense of accomplishment provides a unique source of inspiration to the participating students, which has been shown to translate into increased interest in STEM careers. Our industrial sponsors (Caterpillar, Newmont Mining, Harris, Honeybee Robotics) have all stated that there is a strong need for skills in the workforce related to robotics and automated machines. In 2010, 22 United States (US) universities competed, and in May 2011 the competition was opened to international participation, with 46 Universities attending. There were 12 international teams and 34 US teams. This combined total directly inspired an estimated 544 university students. More students and the public were engaged via internet broadcasting and social networking media. This is expected to be of value for actual future space missions, as knowledge is gained from testing many innovative prototypes in simulated lunar regolith. More information is available at www.nasa.gov/lunabotics/.

Mueller, Robert P.↗

NASA NTRS2016

Risk of Performance Decrements and Adverse Health Outcomes Resulting from Sleep Loss, Circadian Desynchronization, and Work Overload

Sleep loss, circadian desynchronization, and work overload occur to some extent for ground and flight crews, prior to and during spaceflight missions. Ground evidence indicates that such risk factors may lead to performance decrements and adverse health outcomes, which could potentially compromise mission objectives. Efforts are needed to identify the environmental and mission conditions that interfere with sleep and circadian alignment, as well as individual differences in vulnerability and resiliency to sleep loss and circadian desynchronization. Specifically, this report highlights a collection of new evidence to better characterize the risk and reveals new gaps in this risk as follows: Sleep loss is apparent during spaceflight. Astronauts consistently average less sleep during spaceflight relative to on the ground. The causes of this sleep loss remain unknown, however ground-based evidence suggests that the sleep duration of astronauts is likely to lead to performance impairment and short and long-term health consequences. Further research is needed in this area in order to develop screening tools to assess individual astronaut sleep need in order to quantify the magnitude of sleep loss during spaceflight; current and planned efforts in BHP's research portfolio address this need. In addition, it is still unclear whether the conditions of spaceflight environment lead to sleep loss or whether other factors, such as work overload lead to the reduced sleep duration. Future data mining efforts and continued data collection on the ISS will help to further characterize factors contributing to sleep loss. Sleep inertia has not been evaluated during spaceflight. Ground-based studies confirm that it takes two to four hours to achieve optimal performance after waking from a sleep episode. Sleep inertia has been associated with increased accidents and reduced performance in operational environments. Sleep inertia poses considerable risk during spaceflight when emergency situations necessitate that crewmembers wake from sleep and make quick decisions. A recently completed BHP investigation assesses the effects of sleep inertia upon abrupt awakening, with and without hypnotics currently used in spaceflight; results from this investigation will help to inform strategies relative to sleep inertia effects on performance. Circadian desynchrony has been observed during spaceflight. Circadian desynchrony during spaceflight develops due to schedule constraints requiring non-24 operations or 'slam-shifts' and due to insufficient or mis-timed light exposure. In addition, circadian misalignment has been associated with reduced sleep duration and increased medication use. In ground-based studies, circadian desynchrony has been associated with significant performance impairment and increased risk of accidents when operations coincide with the circadian nadir. There is a great deal of information available on how to manage circadian misalignment, however, there are currently no easily collected biomarkers that can be used during spaceflight to determine circadian phase. Current research efforts are addressing this gap. Work overload has been documented during current spaceflight operations. NASA has established work hour guidelines that limit shift duration, however, schedule creep, where duty requirements necessitate working beyond scheduled work hours, has been reported. This observation warrants the documentation of actual work hours in order to improve planning and in order to ensure that astronauts receive adequate down time. In addition to concerns about work overload, ground based evidence suggests that work underload may be a concern during deep space missions, where torpor may develop and physically demanding workload will be exchanged for monitoring of autonomous systems. Given that increased automation is anticipated for exploration vehicles, fatigue effects in the context of such systems needs to be further understood. Performance metrics are needed to evaluate fitness-for-duty during spaceflight. Although ground-based evidence supports the notion that sleep loss, circadian desynchronization and work overload lead to performance impairment, inconsistency in the measures used to evaluate performance during spaceflight make it difficult to evaluate the magnitude of performance impairment during spaceflight. Work is underway to standardize measures of performance evaluation during spaceflight. Once established, such performance indicators need to be correlated with operational performance. Individual differences in sleep need and circadian preference, phase shifting ability and period have been documented in ground-based studies. Individual differences in response to sleep loss and circadian misalignment have also been documented and are presumed to be associated with genetic polymorphisms. No studies have systematically reported individual differences in sleep or circadian-related outcomes during spaceflight. More work is needed in this area in order to identify genetic or phenotypic biomarkers that predict resilience or vulnerability to sleep loss in order to personalize countermeasure strategies and mitigate performance impairment during spaceflight. Two laboratory and field investigations specific to this topic are currently ongoing; additional efforts, including an effort to mine existing biological data from spaceflight relative to sleep and circadian outcomes, are planned. Sex differences in sleep need and circadian period and phase have been reported in ground-based studies. The impact of these sex differences on performance is unclear. Sex differences in sleep need and circadian rhythms have not been systematically studied during spaceflight, presumably due to the small number of women that have flown in space. More research is needed in this area to evaluate whether any of the observed sex differences in physiology lead to altered performance in spaceflight and on the ground.

Flynn-Evans, Erin↗

NASA NTRSDate not supplied

Predicting Team Functioning in Long Term Space Missions Using Acoustic and Linguistic Measures

Maintaining optimal team functioning is critical for long-duration space exploration missions, yet traditional monitoring methods, such as self-reports and wearable sensors, often impose operational burdens or suffer from bias. This paper investigates a non-intrusive speech-based artificial intelligence (AI) framework to predict degradations in team functioning using data from the Human Exploration Research Analog (HERA) of the U.S. National Aeronautics and Space Administration (NASA). Using acoustic features, linguistic descriptors, and semantic embeddings, we evaluate static non-linear and temporal machine learning models to predict both objective (task accuracy) and subjective (self-reported efficacy and cohesion) team functioning outcomes. Results indicate that temporal models outperform static approaches, with prediction of objective task accuracy in Team Interaction Battery (TIB) improving from near chance to 71%. Self-reported outcomes, including team efficacy and cohesion, are predicted more reliably than task performance, achieving balanced accuracies of up to 85.56% and 78.12%, respectively, and are found to be most strongly associated with acoustic features. In a second interdependent task, the MMSEV–EVA, accuracies of up to 78% are achieved using temporal models with acoustic features. Furthermore, incorporating just 1–2 days of team-specific historical data systematically improved performance, and acoustic markers from informal pre-task interactions provided modest predictive gains. Finally, while automated preprocessing yielded viable accuracy, humancorrected data provided moderate performance gains, though transcription error rates did not significantly correlate with model performance. These findings highlight the potential of speech as a passive, high-fidelity monitoring tool for autonomous habitats.

Temporal modeling↗

NASA NTRSDate not supplied

Autonomous Navigation of a Lunar Relay Using GNSS and Other Measurements

Many of the highest priority destinations at the Moon lack a continuous view of Earth, such as the lunar poles or lunar far side. Exploration of these sites will require spacecraft in cislunar space to relay communications and provide position, navigation, and timing (PNT) services. Accurate knowledge of relay position, velocity, and time is essential to these services. This paper describes a concept for a PNT Instrument being developed for the Lunar Communications Relay and Navigation Systems (LCRNS) Project. The instrument is intended as a payload that would enable autonomous, on-board, real-time navigation and timing using Global Navigation Satellite System (GNSS), optical navigation, and one-way measurements from Earth-based ground stations. Hardware-in-the-loop simulations using flight software are used to realistically characterize performance on hardware platforms with a path to flight. These results provide preliminary validation of the proposed PNT Instrument, demonstrate the benefits of augmenting GNSS with other measurements, and serve as an insightful reference for the design of future lunar missions, including those that will operate within the LunaNet framework of standards. This instrument concept relies on several technologies developed at NASA Goddard Space Flight Center (GSFC). For GNSS observables, the instrument relies on the high-altitude NavCube 3 mini (NC3m) GNSS receiver specifically designed for cislunar applications. The autoNGC system, which consists of flight software and a hardware platform, is responsible for fusing the observables using its extended Kalman filter, the Goddard Enhanced Onboard Navigation System (GEONS). Optical navigation observables are processed within autoNGC (“autonomous Navigation, Guidance, and Control”) using the Goddard Image Analysis & Navigation Tool (GIANT) which is also responsible for simulating high-fidelity images for test and analysis. In addition to describing the PNT Instrument and its components, the paper will present predicted performance based on simulation results. As a baseline, it will present GNSS-only hardware-in-the loop results using a NC3m test unit to process Spirent-simulated GPS signals in a potential lunar relay trajectory: a 12-hour elliptical frozen lunar orbit (ELFO). GEONS then processes the GPS pseudorange and time differenced carrier phase measurements to estimate and propagate the relay state (position, velocity, and time). These results extend previously published work that showed preliminary ELFO performance. Previous work has shown the importance of other measurement types, so additional simulations are performed which augment GNSS with ground station observables and several methods of optical navigation, including celestial navigation, limb-finding (e.g., observations of the lunar horizon), and terrain relative navigation (TRN). TRN involves correlating simulated predicted images of the lunar surface with actual imagery; misalignments of landmarks identified in each image are translated into relay state updates. TRN is valuable as a measurement of the relay’s state relative to the Moon, especially during GNSS outages or after maneuvers. One-way Pseudorange and Doppler measurements from Earth-based ground stations are also simulated. The full set of observables is processed using autoNGC. These simulations make use of autoNGC and NC3m test units, a lab atomic clock, and a pulse-per-second (PPS) generation and distribution system. This combination of subsystems, and the hardware platforms used in this analysis, represents a PNT Instrument that could be flown on a lunar relay. Results from the hardware-in-the-loop simulations presented in this paper provide a preliminary assessment of the achievable navigation performance of this instrument concept. PNT Instrument performance is compared to the GPS-only performance, and a discussion is provided on the apparent merits and challenges of each measurement type.

Ben Ashman↗

NASA NTRS2007

The Autonomous Precision Landing and Hazard Detection and Avoidance Technology (ALHAT)

As NASA plans to send humans back to the Moon and develop a lunar outpost, technologies must be developed to place humans and cargo safely, precisely, repeatedly, on the lunar surface with the capability to avoid surface hazards. Exploration Space Architecture Study requirements include the need for global lunar surface access with safe, precise landing without lighting constraints on terrain that may have landing hazards for human scale landing vehicles. Landing accuracies of perhaps 1,000 meters for sortie crew missions to 10 s of meters for Outpost class missions are required. The Autonomous precision Landing Hazard Avoidance Technology (ALHAT) project will develop the new and unique descent and landing Guidance, Navigation and Control (GNC) hardware and software technologies necessary for these capabilities. The ALHAT project will qualify a lunar descent and landing GNC system to a Technology Readiness Level (TRL) of 6 capable of supporting lunar crewed, cargo, and robotic missions. The (ALHAT) development project was chartered by NASA Headquarters in October 2006. The initial effort to write a project plan and define an ALHAT Team was followed by a fairly aggressive research and analysis effort to determine what technologies existed that could be developed and applied to the lunar landing problems indicated above. This paper describes the project development, research, analysis and concept evolution that has occurred since the assignment of the project. This includes the areas of systems engineering, GNC, sensors, sensor algorithms, simulations, fielding testing, laboratory testing, Hardware-In-The-Loop testing, system avionics and system certification concepts.

Epp, Chirold D.↗

NASA NTRS1992

Multi-Beam Range Imager for Autonomous Operations

For space operations using Space Station Freedom, the real-time range imager will be very valuable in terms of refueling and docking, as well as space exploration.

robotics↗

NASA NTRS1993

Application of the CIRSSE cooperating robot path planner to the NASA Langley truss assembly problem

A method for autonomously planning collision free paths for two cooperating robots in a static environment was developed at the Center for Intelligent Robotic Systems for Space Exploration (CIRSSE). The method utilizes a divide-and-conquer type of heuristic and involves non-exhaustive mapping of configuration space. While there is no guarantee of finding a solution, the planner was successfully applied to a variety of problems including two cooperating 9 degrees of freedom (dof) robots. Although developed primarily for cooperating robots the method is also applicable to single robot path planning problems. A single 6 dof version of the planner was implemented for the truss assembly east, at NASA Langley's Automated Structural Assembly Lab (ASAL). The results indicate that the planner could be very useful in addressing the ASAL path planning problem and that further work along these lines is warranted.

Weaver, Jonathan M.↗

NASA NTRS2004

Autonomous Sciencecraft Experiment (ASE) Operations on EO-1 in 2004

The Autonomous Sciencecraft Experiment (ASE) has been selected for flight demonstration by NASAs New Millennium Program (NMP) as part of the Space Technology 6 (ST6) mission. NASA has identified the development of an autonomously operating spacecraft as a necessity for an expanded program of missions exploring the Solar System. The versatile ASE spacecraft command and control software, image formation software, and science processing software will be uploaded to the Earth Observer 1 (EO-1) spacecraft in early 2004 to detect surface modification related to volcanism, ice formation and retreat, and flooding.

Davies, A. G.↗

NASA NTRS2007

In-Space Crew-Collaborative Task Scheduling

For all past and current human space missions, the final scheduling of tasks to be done in space has been devoid of crew control, flexibility, and insight. Ground controllers, with minimal input from the crew, schedule the tasks and uplink the timeline to the crew or uplink the command sequences to the hardware. Prior to the International Space Station (ISS), the crew could make requests about tomorrow s timeline, they could omit a task, or they could request that something in the timeline be delayed. This lack of control over one's own schedule has had negative consequences. There is anecdotal consensus among astronauts that control over their own schedules will mitigate the stresses of long duration missions. On ISS, a modicum of crew control is provided by the job jar. Ground controllers prepare a task list (a.k.a. "job jar") of non-conflicting tasks from which jobs can be chosen by the in space crew. Because there is little free time and few interesting non-conflicting activities, the task-list approach provides little relief from the tedium of being micro-managed by the timeline. Scheduling for space missions is a complex and laborious undertaking which usually requires a large cadre of trained specialists and suites of complex software tools. It is a giant leap from today s ground prepared timeline (with a job jar) to full crew control of the timeline. However, technological advances, currently in-work or proposed, make it reasonable to consider scheduling a collaborative effort by the ground-based teams and the in-space crew. Collaboration would allow the crew to make minor adjustments, add tasks according to their preferences, understand the reasons for the placement of tasks on the timeline, and provide them a sense of control. In foreseeable but extraordinary situations, such as a quick response to anomalies and extended or unexpected loss of signal, the crew should have the autonomous ability to make appropriate modifications to the timeline, extend the timeline, or even start over with a new timeline. The Vision for Space Exploration (VSE), currently being pursued by the National Aeronautics and Space Administration (NASA), will send humans to Mars in a few decades. Stresses on the human mind will be exacerbated by the longer durations and greater distances, and it will be imperative to implement stress-reducing innovations such as giving the crew control of their daily activities.

Jaap, John↗

NASA NTRS2020

Assemblers: A Modular, Reconfigurable Manipulator for Autonomous in-Space Assembly

This paper presents a modular, reconfigurable robotic manipulator for autonomous in-space assembly. On-orbit and planetary surface assembly is a challenging domain that encompasses various technological thrusts to support human exploration, science, and technology demonstration missions. The system architecture presented here addresses the need for robotic assembly of structures in locations that prohibit constant human oversight and/or teleoperation. This system is capable of scheduling, reconfiguring itself, and executing structural assembly tasks with the ability to assess construction and correct errors during the assembly as needed. An example concept of operations is presented. Details of the mechanical and electrical design of the robotic modules as well as the dynamic modeling approach, control algorithms, and sensing and perception systems are also provided.

robotics↗

NASA NTRS2015

The ISECG* Global Exploration Roadmap as Context for Robotic and Human Exploration Operations

The International Space Exploration Coordination Group (ISECG) Global Exploration Roadmap (GER) provides a broad international context for understanding how robotic missions and robotic assets can enable future human exploration of multiple destinations. This presentation will provide a brief high-level review of the GER with a focus on key robotic missions and robotic assets that can provide enabling technology advancements and that also raise interesting operational challenges in both the near-term and long-term. The GER presently features a variety of robotic missions and robotic assets that can provide important technology advancements as well as operational challenges and improvements, in areas ranging from: (a) leveraging the International Space Station, (b) planetary science robotic missions to potential human destinations, (c) micro-g body proximity operations (e.g. asteroids), (d) autonomous operations, (e) high and low-latency telerobotics, (f) human assisted sample return, and (g) contamination control. This presentation will highlight operational and technology challenges in these areas that have feed forward implications for human exploration.

telerobotics↗

NASA NTRS2017

Spacecraft in the Shot: A Platform for Deep-Space Cinematography

Spacecraft In The Shot (SITS) is a breakthrough cross-disciplinary initiative that stands at the intersection of advanced technology and strategic storytelling. It aims to enhance humanity’s connection to robotic space exploration by capturing high-definition footage of heroic spacecraft along their journey through deep space. The SITS Imaging Instrument, a self-contained, highly autonomous instrument package can be included in any typical mission payload. The Instrument stores and deploys SITS imagers, or camera-carrying nanosats. As a mission approaches a key milestone in the trajectory, the SITS Instrument deploys one or more imagers that fly pre-programmed, autonomous trajectories around the host spacecraft. After the footage has been collected and downlinked to the host, the Imagers get decommissioned and left behind as the host spacecraft continues into the depths of space until the next photo opportunity.

Smith, Jonathon↗

NASA NTRS1999

Autonomous Command Operation of the WIRE Spacecraft

This paper presents the end-to-end design architecture for an autonomous commanding capability to be used on the Wide Field Infrared Explorer (WIRE) mission for the uplink of command loads during unattended station contacts. The WIRE mission is the fifth and final mission of NASA's Goddard Space Flight Center Small Explorer (SMEX) series to be launched in March of 1999. Its primary mission is the targeting of deep space fields using an ultra-cooled infrared telescope. Due to its mission design WIRE command loads are large (approximately 40 Kbytes per 24 hours) and must be performed daily. To reduce the cost of mission operations support that would be required in order to uplink command loads, the WIRE Flight Operations Team has implemented an autonomous command loading capability. This capability allows completely unattended operations over a typical two-day weekend period.

Prior, Mike↗