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NASA NTRS2017

Playbook Data Analysis Tool: Collecting Interaction Data from Extremely Remote Users

Typically, user tests for software tools are conducted in person. At NASA, the users may be located at the bottom of the ocean in a pressurized habitat, above the atmosphere in the International Space Station, or in an isolated capsule on a simulated asteroid mission. The Playbook Data Analysis Tool (P-DAT) is a human-computer interaction (HCI) evaluation tool that the NASA Ames HCI Group has developed to record user interactions with Playbook, the group's existing planning-and-execution software application. Once the remotely collected user interaction data makes its way back to Earth, researchers can use P-DAT for in-depth analysis. Since a critical component of the Playbook project is to understand how to develop more intuitive software tools for astronauts to plan in space, P-DAT helps guide us in the development of additional easy-to-use features for Playbook, informing the design of future crew autonomy tools.P-DAT has demonstrated the capability of discreetly capturing usability data in amanner that is transparent to Playbook’s end-users. In our experience, P-DAT data hasalready shown its utility, revealing potential usability patterns, helping diagnose softwarebugs, and identifying metrics and events that are pertinent to Playbook usage aswell as spaceflight operations. As we continue to develop this analysis tool, P-DATmay yet provide a method for long-duration, unobtrusive human performance collectionand evaluation for mission controllers back on Earth and researchers investigatingthe effects and mitigations related to future human spaceflight performance.

in-flight monitoring↗
NASA NTRS2017

What Machines Need to Learn to Support Human Problem-Solving

In the development of intelligent systems that interact with humans, there is often confusion between how the system functions with respect to the humans it interacts with and how it interfaces with those humans. The former is a much deeper challenge than the latter it requires a system-level understanding of evolving human roles as well as an understanding of what humans need to know (and when) in order to perform their tasks. This talk will focus on some of the challenges in getting this right as well as on the type of research and development that results in successful human-autonomy teaming. Brief Bio: Dr. Alonso Vera is Chief of the Human Systems Integration Division at NASA Ames Research Center. His expertise is in human-computer interaction, information systems, artificial intelligence, and computational human performance modeling. He has led the design, development and deployment of mission software systems across NASA robotic and human space flight missions, including Mars Exploration Rovers, Phoenix Mars Lander, ISS, Constellation, and Exploration Systems. Dr. Vera received a Bachelor of Science with First Class Honors from McGill University in 1985 and a Ph.D. from Cornell University in 1991. He went on to a Post-Doctoral Fellowship in the School of Computer Science at Carnegie Mellon University from 1990-93.

intelligent systems↗
NASA NTRS2017

Modular Autonomous Systems Technology Framework: A Distributed Solution for System Monitoring and Control

The Modular Autonomous Systems Technology (MAST) framework is a tool for building distributed, hierarchical autonomous systems. Originally intended for the autonomous monitoring and control of spacecraft, this framework concept provides support for variable autonomy, assume-guarantee contracts, and efficient communication between subsystems and a centralized systems manager. MAST was developed at NASA's Johnson Space Center (JSC) and has been applied to an integrated spacecraft example scenario.

Badger, Julia M.↗
NASA NTRS2017

SPHERES and Astrobee: Space Station Robotic Free Flyers

Free-flying space robots can be used when humans are present to off-load routine work, to increase astronaut productivity, and to handle contingencies. The International Space Station (ISS), for example, is a continuously manned orbital laboratory the size of a large house, which contains many thousands of inventory items and hundreds of diverse payloads and experiments - all of which have to be managed by 6 person crew. To help with this, NASA is developing and testing robotic free-flyers on the ISS. SPHERES (Synchronized Position Hold, Engage, Reorient, Experimental Satellites) is an ISS facility with three nano-satellites designed to research estimation, control, and autonomy algorithms. SPHERES are volleyball-sized, have their own power, propulsion and navigation systems, and work on the ISS under astronaut supervision. For more than 10 years, NASA has made SPHERES available to other U.S. government agencies, schools, commercial companies and students as a platform for science, technology development, and education. SPHERES will soon be succeeded by the new Astrobee free-flying robot. Astrobee builds on the success of SPHERES, but in addition to research, the robot will also be used for housekeeping and monitoring duties without astronaut supervision. Astrobee makes extensive use of open-source (the complete software stack is available on GitHub) and is scheduled to be installed on the ISS in late Spring 2018.

Benavides, Jose V.↗
NASA NTRS2018

Model-Based Data Integration and Process Standardization Techniques for Fault Management: A Feasibility Study

This paper describes the theory and considerations in the application of model-based techniques to assimilate information from disjoint knowledge sources for performing NASA's Fault Management (FM)-related activities using the TEAMS® toolset. FM consists of the operational mitigation of existing and impending spacecraft failures. NASA's FM directives have both design-phase and operational-phase goals. This paper highlights recent studies by QSI and DST of the capabilities required in the TEAMS® toolset for conducting FM activities with the aim of reducing operating costs, increasing autonomy, and conforming to time schedules. These studies use and extend the analytic capabilities of QSI's TEAMS® toolset to conduct a range of FM activities within a centralized platform.

Haste, Deepak↗
NASA NTRS2018

Verification and Validation Challenges for Adaptive Flight Control of Complex Autonomous Systems

Autonomy of aerospace systems requires the ability for flight control systems to be able to adapt to complex uncertain dynamic environment. In spite of the five decades of research in adaptive control, the fact still remains that currently no adaptive control system has ever been deployed on any safety-critical or human-rated production systems such as passenger transport aircraft. The problem lies in the difficulty with the certification of adaptive control systems since existing certification methods cannot readily be used for nonlinear adaptive control systems. Research to address the notion of metrics for adaptive control began to appear in the recent years. These metrics, if accepted, could pave a path towards certification that would potentially lead to the adoption of adaptive control as a future control technology for safety-critical and human-rated production systems. Development of certifiable adaptive control systems represents a major challenge to overcome. Adaptive control systems with learning algorithms will never become part of the future unless it can be proven that they are highly safe and reliable. Rigorous methods for adaptive control software verification and validation must therefore be developed to ensure that adaptive control system software failures will not occur, to verify that the adaptive control system functions as required, to eliminate unintended functionality, and to demonstrate that certification requirements imposed by regulatory bodies such as the Federal Aviation Administration (FAA) can be satisfied. This presentation will discuss some of the technical issues with adaptive flight control and related V&V challenges.

Nguyen, Nhan T.↗
NASA NTRS2018

Robonaut 2 and Watson: Cognitive Dexterity for Future Exploration

Future exploration missions will dictate a level of autonomy never before experienced in human spaceflight. Mission plans involving the uncrewed phases of complex human spacecraft in deep space will require a coordinated autonomous capability to be able to maintain the spacecraft when ground control is not available. One promising direction involves embedding intelligence into the system design both through the employment of state-of-the-art system engineering principles as well as through the creation of a cognitive network between a smart spacecraft or habitat and embodiments of cognitive agents. The work described here details efforts to integrate IBM's Watson and other cognitive computing services into NASA Johnson Space Center (JSC)'s Robonaut 2 (R2) anthropomorphic robot. This paper also discusses future directions this work will take. A cognitive spacecraft management system that is able to seamlessly collect data from subsystems, determine corrective actions, and provide commands to enable those actions is the end goal. These commands could be to embedded spacecraft systems or to a set of robotic assets that are tied into the cognitive system. An exciting collaboration with Woodside provides a promising Earth-bound testing analog, as controlling and maintaining not normally manned off-shore platforms have similar constraints to the space missions described.

Badger, Julia M.↗
NASA NTRS2018

Aircraft Capability Management

This presentation presents an overview of work performed at NASA Ames Research Center in 2017. The work concerns the analysis of current aircraft system management displays, and the initial development of an interface for providing information about aircraft system status. The new interface proposes a shift away from current aircraft system alerting interfaces that report the status of physical components, and towards displaying the implications of degradations on mission capability. The proposed interface describes these component failures in terms of operational consequences of aircraft system degradations. The research activity was an effort to examine the utility of different representations of complex systems and operating environments to support real-time decision making of off-nominal situations. A specific focus was to develop representations that provide better integrated information to allow pilots to more easily reason about the operational consequences of the off-nominal situations. The work is also seen as a pathway to autonomy, as information is integrated and understood in a form that automated responses could be developed for the off-nominal situations in the future.

airplane capabilities↗
NASA NTRS2018

Development of a Ground Test and Analysis Protocol for NASA's NextSTEP Phase 2 Habitation Concepts

The NASA Next Space Technologies for Exploration Partnerships (NextSTEP) program is a public-private partnership model that seeks commercial development of deep space exploration capabilities to support human spaceflight missions around and beyond cislunar space. NASA first issued the Phase 1 NextSTEP Broad Agency Announcement to U.S. industries in 2014, which called for innovative cislunar habitation concepts that leveraged commercialization plans for low-Earth orbit. These habitats will be part of the Deep Space Gateway (DSG), the cislunar space station planned by NASA for construction in the 2020s. In 2016, Phase 2 of the NextSTEP program selected five commercial partners to develop ground prototypes. A team of NASA research engineers and subject matter experts (SMEs) have been tasked with developing the ground-test protocol that will serve as the primary means by which these Phase 2 prototypes will be evaluated. Since 2008, this core test team has successfully conducted multiple spaceflight analog mission evaluations utilizing a consistent set of operational tools, methods, and metrics to enable the iterative development, testing, analysis, and validation of evolving exploration architectures, operations concepts, and vehicle designs. The purpose of implementing a similar evaluation process for the Phase 2 Habitation Concepts is to consistently evaluate different commercial partner ground prototypes to provide data-driven, actionable recommendations for Phase 3. This paper describes the process by which the ground test protocol was developed and the objectives, methods, and metrics by which the NextSTEP Phase 2 Habitation Concepts will be rigorously and systematically evaluated. The protocol has been developed using both a top-down and bottom-up approach. Top-down development began with the Human Exploration and Operations Mission Directorate (HEOMD) exploration objectives and ISS Exploration Capability Study Team (IECST) candidate flight objectives. Strategic questions and associated rationales, derived from these candidate architectural objectives, provide the framework by which the ground-test protocol will address the DSG stack elements and configurations, systems and subsystems, and habitation, science, and EVA functions. From these strategic questions, high-level functional requirements for the DSG were drafted and associated ground-test objectives and analysis protocols were established. Bottom-up development incorporated objectives from NASA SMEs in autonomy, avionics and software, communication, environmental control and life support systems, exercise, extravehicular activity, exploration medical operations, guidance navigation and control, human factors and behavioral performance, human factors and habitability, logistics, Mission Control Center operations, power, radiation, robotics, safety and mission assurance, science, simulation, structures, thermal, trash management, and vehicle health. Top-down and bottom-up objectives were integrated to form overall functional requirements - ground-test objectives and analysis mapping. From this mapping, ground-test objectives were organized into those that will be evaluated through inspection, demonstration, analysis, subsystem standalone testing, and human-in-the-loop (HITL) testing. For the HITL tests, mission-like timelines, procedures, and flight rules have been developed to directly meet ground test objectives and evaluate specific functional requirements. Data collected from these assessments will be analyzed to determine the acceptability of habitation element configurations and the combinations of capabilities that will result in the best habitation platform to be recommended by the test team for Phase 3.

Gernhardt, Michael L.↗
NASA NTRS2018

Autonomous Systems Taxonomy

The purpose of this taxonomy is to provide common definitions and a functional decomposition of the technology that is required for NASA's autonomous systems. The taxonomy serves as a framework for: (1) assessing the state of NASA's autonomous systems capability (workforce, technology, etc.) and (2) assessing the state of the art in autonomy technology.

Fong, Terrence W.↗
NASA NTRS2018

Sixth NASA Glenn Research Center Propulsion Control and Diagnostics (PCD) Workshop

The Intelligent Control and Autonomy Branch at NASA Glenn Research Center hosted the Sixth Propulsion Control and Diagnostics Workshop on August 22-24, 2017. The objectives of this workshop were to disseminate information about research being performed in support of NASA Aeronautics programs; get feedback from peers on the research; and identify opportunities for collaboration. There were presentations and posters by NASA researchers, Department of Defense representatives, and engine manufacturers on aspects of turbine engine modeling, control, and diagnostics.

Collaboration↗
NASA NTRS2018

A Prognostics Framework Development for Swarm Satellite Formations

Prognostics is the science of predicting the failure(s) of a component or a system and understanding how the performance will change in the event of a failure or degradation mechanism. With accurate predictions of possible failures, autonomous mitigative actions can be taken to correct/repair any issues or alert human operators of a failure threshold exceedance requiring condition-based maintenance. Although there is extensive research on failure predictions for a component or a system, there are significantly more opportunities to foray into failure predictions and prognostics for a system of systems such as an airspace consisting of multiple aircraft, a fleet of unmanned aerial vehicles, and a swarm of intelligent satellite systems. Failure prediction and mitigation are particularly important in autonomous systems such as satellite swarm systems that need effective resource management and minimal human interactions. Based on NASA's decadal survey, there is a clear need to prioritize the development of satellite swarm technology for studies of space physics and Earth science. The science community will propose future missions that return in-situ measurements from a 3-D (three-dimensional) volume of space, with relative spacecraft motion and inter-satellite baselines controlled according to the mission objectives. For such multi-spacecraft missions, it is required that ground operations resources do not scale with the number of satellites, thus compromising the swarm or leading to inefficiencies in resource allocation. Swarms of tens or hundreds of small satellites will require autonomy in attitude control, navigation and failure. Although significant research has been conducted in the areas of autonomous formation flying algorithms, less attention has been given to the development of resilient systems robust to failures.The focus of this research paper is the integration of model-based prognostics into the swarm dynamics control and decision-making algorithms. We simulate swarm management strategies for a subsystem failure to demonstrate the importance of failure predictions by comparing two cases: (i) no health information is provided to the system and utilized in the decision-making process and (2) system health information is obtained using prognostics and employed by the control system. One example scenario presented is for the GPS (Global Positioning System) system of an individual satellite to perform off-nominally due to increasing estimated error. In this scenario, the keep-out zone for that satellite would become more conservative, thereby decreasing the risk of collision. This is achieved via tuning the individual artificial repulsive functions assigned to each satellite.This paper is structured as follows. First we provide an overview of current swarm technology development, where we specifically use the term swarm to define multiple satellites flying in formation in similar orbits, with cross-link communication and station-keeping capabilities. Second, we give an introduction to the Swarm Orbital Dynamics Advisor (SODA), a tool that accepts high-level configuration commands and provides the orbital maneuvers required to achieve the prescribed formation configuration. Third, we provide the details of the model-based prognostics algorithm implementation in SODA. Finally, we present different case studies for potential component/subsystem failures and the swarm responses based with and without failure prediction information.

prognostics↗
NASA NTRS2018

Low-Latency Teleoperations: Operational Implications for Human Space Exploration

Low-latency teleoperations (LLT) is envisioned to be an element of human exploration missions in a number of different applications. LLT can be broadly considered to encompass any remote operation of an asset with a communication delay that is less than the human response time to allow for what is effectively "real-time" or "near real-time" operations. This paper will explore motivations and operational implications for why and how LLT might be used for human exploration space missions. LLT analyses have been performed under the auspices of the NASA Human Spaceflight Architecture Team (HAT) and Evolvable Mars Campaign (EMC). The EMC created a flexible, evolvable, capability-driven architectural strategy to enable a sustainable long-term human presence at Mars. LLT is envisioned to be part of that strategy for both in-space and on-surface applications, and this paper will expand on operational considerations within that broader strategic context, as well additional contexts. Some operational implications explored in this paper, derived largely from previous work, are: (1) crew mission support, for which we will address roles for Mission Control on earth, balanced with the capability for crew and robotic assets to operate independently, (2) science operations, with a focus on "backroom" support, highly dynamic science, and enhanced science return and efficiency, and (3) operational efficiency at a deep-space destination such as Mars, including implications for communications infrastructures and how to leverage and balance system autonomy with crew operations, both of which can inform the overall operational "choreography" between crew members, multiple shifts, and exploration assets.

human spaceflight↗
NASA NTRS2018

Managing Complex Airplane System Failures Through a Structured Assessment of Airplane Capabilities

This report describes an analysis of current transport aircraft system-management displays and the initial development of a set of display concepts for providing information about aircraft system status. The new display concepts are motivated by a shift away from the current approach to aircraft system alerting that reports the status of physical components, and towards displaying the implications for mission capability. Specifically, the proposed display concepts describe transport airplane component failures in terms of operational consequences of aircraft system degradations. The research activity described in this report is an effort to examine the utility of different representations of complex systems and operating environments to support real-time decision making during off-nominal situations. A specific focus is to develop display concepts that provide more highly integrated information to allow pilots to more easily reason about the operational consequences of the off-nominal situations. The work can also serve as a foundational element to autonomy-supported decision making since we are developing ideas for integrating information from the airplane and the operational environment to support decision making.

operational decision making↗
NASA NTRS2018

Overview of MSFC Additive Electronics Capabilities

Focus: Marshall seeks to support the Agency in the development of next generation printed electronics technologies for living and working in space, with emphasis on enhanced electronics manufacturing processes and capabilities development on the ground and in-space. Near-Term: Human Habitation Elements and Life Support Systems - pursuing integrated flexible wearable air, water, vital monitoring solutions for next generation printed technologies; Complete startup printing technology demonstrations which prove basic processes and establish ISM (In-Space Manufacturing) infrastructure needed for future applications including metals based manufacturing. Medium Term: Target low-cost research and demonstration activities that support multi-material additive manufacturing, more sophisticated parts production, printed electronics and ISM; Maturation and flight demonstration of printed propulsion system components, with emphasis on infusion into small-spacecraft-based missions. Long-Term: Evolve systems capabilities to be supportive of destination (lunar or Mars) resources and requirements, increase autonomy in systems and utilize in-situ resources towards manufacturing; Support development of self-replicable systems and their infusion into future spacecraft and missions.

MacLeod, Todd C.↗
NASA NTRS2018

Coronal Imaging with the Solar Ultraviolet Imager

Coronal imaging capabilities of the Solar UltraViolet Imager (SUVI) on the Geostationary Operational Environmental Satellite-16 (GOES-16) are investigated. Launched in 2016, GOES-16 is stationed at 75.2 deg W longitude and accommodates both terrestrial and space weather instruments. SUVI is one of two instruments observing the Sun, providing solar images in six extreme ultraviolet (EUV) wavelengths: 94A, 131A, 171A, 195A, 284A and 304A. Mounted on a two axis gimballed Sun Pointed Platform (SPP), its 53 arc minute square field of view is nominally Sun-centered to provide high dynamic range images in all six wavelengths every four minutes. Analyses of on-orbit data indicated that SUVI had sufficient dynamic range and sensitivity in the 171A and 195A wavelengths to image the corona to the largest heights above the Sun to date while simultaneously imaging the Sun. The 1.6 megapixel Charge Coupled Device (CCD) detector enables the capture of the fine features of the corona. On-orbit calibration tests requiring movement of the SUVI line-of-sight off the Sun, demonstrated that the resulting dynamic disturbance didn't adversely impact the earth-pointing instrument performance. Combining these factors, we conducted a test to assess the performance of SUVI as an EUV extremely wide-field coronal imager by off-pointing the SPP around the Sun and synthesizing an image that's about 4 times the diameter of the Sun. The capability to perform coronal imaging at these two wavelengths is demonstrated by off-pointing the SUVI boresight to nine locations around the Sun and generating a composite coronal image that extends to approx. 4 solar radii. For this purpose, we exploited the satellite's ability to provide stable, fine pointing, even during the slews. Various exposure durations were considered during the first trial and a composite coronal image is obtained for each wavelength. The results are promising. The off-pointing locations and the exposure duration are being refined for the subsequent trials. The routine use of an ultraviolet solar imager as both an imager and a EUV coronagraph would be the first of its kind and is an exciting development. The on-orbit solar coronagraphs to date provide white light images with fields of view extending from 4-15 solar radii. Earlier, Seaton, et al investigated the evolution of the corona in EUV near the solar surface with the data from the Sun Watcher with Active Pixels and Image Processing (SWAP) EUV solar telescope on board the Project for On-Board Autonomy 2 (PROBA2). The presence of corona in EUV even to 4 solar radii is not known. The results of these experiments will begin to fill this void.

Tadikonda, Sivakumara K.↗
NASA NTRS2018

Coronal Imaging with the Solar Ultraviolet Imager

Coronal imaging capabilities of the Solar UltraViolet Imager (SUVI) on the Geostationary Operational Environmental Satellite-16 (GOES-16) are investigated. Launched in 2016, GOES-16 is stationed at 75.2 deg W longitude and accommodates both terrestrial and space weather instruments. SUVI is one of two instruments observing the Sun, providing solar images in six extreme ultraviolet (EUV) wavelengths: 94A, 131A, 171A, 195A, 284A and 304A. Mounted on a two axis gimballed Sun Pointed Platform (SPP), its 53 arcminute square field of view is nominally Sun-centered to provide high dynamic range images in all six wavelengths every four minutes. Analyses of on-orbit data indicated that SUVI had sufficient dynamic range and sensitivity in the 171A and 195A wavelengths to image the corona to the largest heights above the Sun to date while simultaneously imaging the Sun. The 1.6 megapixel Charge Coupled Device (CCD) detector enables the capture of the fine features of the corona. On-orbit calibration tests requiring movement of the SUVI line-of-sight off the Sun, demonstrated that the resulting dynamic disturbance didn't adversely impact the earth-pointing instrument performance. Combining these factors, we conducted a test to assess the performance of SUVI as an EUV extremely wide-field coronal imager by off-pointing the SPP around the Sun and synthesizing an image that's about 4 times the diameter of the Sun. The capability to perform coronal imaging at these two wavelengths is demonstrated by off-pointing the SUVI boresight to nine locations around the Sun and generating a composite coronal image that extends to approx. 4 solar radii. For this purpose, we exploited the satellite's ability to provide stable, fine pointing, even during the slews. Various exposure durations were considered during the first trial and a composite coronal image is obtained for each wavelength. The results are promising. The off-pointing locations and the exposure duration are being refined for the subsequent trials. The routine use of an ultraviolet solar imager as both an imager and a EUV coronagraph would be the first of its kind and is an exciting development. The on-orbit solar coronagraphs to date provide white light images with fields of view extending from 4-15 solar radii. Earlier, Seaton, et al investigated the evolution of the corona in EUV near the solar surface with the data from the Sun Watcher with Active Pixels and Image Processing (SWAP) EUV solar telescope on board the Project for On-Board Autonomy 2 (PROBA2). The presence of corona in EUV even to 4 solar radii is not known. The results of these experiments will begin to fill this void.

Tadikonda, Sivakumara K.↗
NASA NTRS2018

Simulation Evaluations of an Autonomous Urban Air Mobility Network Management and Separation Service

This paper presents an initial implementation of an autonomous Urban Air Mobility network management and aircraft separation service for urban airspace that does 1) departure and arrival scheduling across the network, 2) continuous trajectory management to ensure safe separation between aircraft, and 3) seamless integration with traditional operations. The highly-autonomous AutoResolver algorithm developed for traditional aviation was extended to provide these capabilities. An evaluation of this initial implementation was conducted in fast-time simulations using a dense, two-hour traffic scenario with Urban Air Mobility aircraft flying between a network of 20 vertiports in the Dallas-Fort Worth metroplex. When the spatial separation was reduced from 0:3nmi to 0:1nmi, the total de- lay decreased by 7:3%; when the temporal separation was reduced from 60s to 45s, the total delay decreased by 28:4%. The total number of conflict resolutions decreased by 26% and 17%, respectively. Furthermore, when a scheduling horizon greater than the duration of UAM flights was used (50min), most conflicts were resolved pre-departure producing ground delay. By comparison, when a shorter scheduling horizon was used (8min), most conflicts were resolved post-departure generating airborne delay. For all scheduling and separation constraints tested, AutoResolver prevented loss of separation from occurring. Urban Air Mobility operations have the ability to revolutionize how people and goods are transported and this paper presents initial research focusing on the high levels of autonomy required for an airspace system capable of scaling to handle significantly higher densities of aircraft.

Bosson, Christabelle S.↗