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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.

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NASA NTRS1994

Customizing the JPL Multimission Ground Data System: Lessons Learned

This paper will describe lessons learned in adapting JPL's Multimission Ground Data System (MGDS) to fly the Voyager, Galileo, and Mars Observer missions. We will explain how powerful, existing ground data systems can be adapted and packaged in a cost effective way for operations of small and large planetary missions. We will also describe how the MGDS was adapted to support operations within the Galileo Spacecraft Testbed. The Galileo testbed provided a unique opportunity to adapt MGDS to support command and control operations for a small autonomous operations team with a handful of engineers flying the Galileo Spacecraft flight system model.

ground↗
NASA NTRS2024

Search Technology for Optimal Rescue Missions (STORM)

Natural disasters, such as earthquakes, hurricanes, and wildfires are responsible for the deaths of 60,000 to 90,000 people per year. Today, search and rescue (SAR) operations heavily rely on humans to find and deliver life-saving supplies to those affected by these disasters. However, these operations have limits in visibility, navigation, communication systems, and data availability in the area affected, as well as endangering the SAR personnel. Search Technology for Optimal Rescue Missions (STORM) discusses a new system for SAR teams using autonomous drones able to find and deliver supplies to people, without risking more lives in the process. The concept includes the use of two drone types, STORM Search and STORM Rescue, which will survey and locate survivors and be able to drop equipment to the survivors identified, respectively. These two types of drones were optimized in drone design and durability (such as the use of dihedral wings and a toroidal propeller), detection and navigation systems (sturdy thermal and Light Detection and Ranging [LiDAR] cameras), automation and system design (Machine Learning and Computer Vision), server-drone communication (Meshnets), weight, and cost. Once implemented, the STORM concept is expected to improve, ease, and speed up SAR operations, and most important of all, rescue lives that would have never been currently possible to find.

Astha Ingole↗
NASA NTRS2018

Trusted Communication: Utilizing Speech Communication to Enhance Human-Machine Teaming Success

An area of increasing interest for the next generation of aircraft is autonomy and the integration of increasingly autonomous systems into the national airspace. Such an integration requires humans to work closely with autonomous systems, forming teams. Our hypothesis is that a team composed of both humans and autonomous systems will operate better than either entity alone. We have existing procedures for certifying pilots to operate in the national airspace and are currently working on methods for validating the function of autonomous systems, however we have no method in place for assessing the interaction of these two disparate systems. Communication is one avenue. This paper will examine the use of language as a metric for ascertaining human-machine teaming effectiveness. A proof-of-concept of the application of two communication-based analysis techniques, Linguistic Inquiry and Word Count (LIWC) and Latent Semantic Analysis (LSA), for the prediction of success in human/chatbot teaming was conducted. By running these analyses over data from the 2014 and 2015 Loebner Prize competitions of human/chatbot teaming, numerical scores were obtained that can be associated with scores provided by human judges during the competition. Correlating their LIWC and LSA data with the scores provided by the judges, and using linear regression over this correlation, formulae were obtained that predict the score of human/chatbot interaction. These formulae were tested over the 2013 Loebner Prize transcripts, determining that, though there was strong correlation between predicted and actual scores, the predictive success of this method was not strong. However, with specialized topic spaces and lexica, as well as larger data sets, the predictive power of these metrics will improve. Given the importance of providing metrics for human-machine system team success and given the promise shown by the communication-basedLIWCand LSAmethods, continuing research in this area is necessary. After examining the potential for using communication and spoken language as a metric for the success of human/autonomous system teaming, this paper then examines aspects inherent to communication systems that may contribute to unreliability and reduced trust. Modern natural language processing tools rely on deep learning algorithms to create language rules that produce accurate results, but these rules are uninterpretable. The resulting blackbox system lacks transparency necessary for full validation and complete trust. Additionally, speech-based interfaces pose other difficulties to developing coordinated teamwork between humans and autonomous systems. Human communication is infrequently limited to speech only, instead usually relying on a combination of verbal, gestural, and general body language communication. Reducing an analysis of team effectiveness to a study of spoken language alone is problematic as it leaves these other equally important forms of communication out. This paper will examine these problems and the general deficiencies in speech-based metrics for human-machine teaming.

E L Meszaros↗
NASA NTRS2012

System Engineering Paper

The Iowa State University team, Team LunaCY, is composed of the following sub-teams: the main student organization, the Lunabotics Club; a senior mechanical engineering design course, ME 415; a senior multidisciplinary design course, ENGR 466; and a senior design course from Wartburg College in Waverly, Iowa. Team LunaCY designed and fabricated ART-E III, Astra Robotic Tractor- Excavator the Third, for the team's third appearance in the NASA Lunabotic Mining competition. While designing ART-E III, the team had four main goals for this year's competition:to reduce the total weight of the robot, to increase the amount of regolith simulant mined, to reduce dust, and to make ART-E III autonomous. After many designs and research, a final robot design was chosen that obtained all four goals of Team LunaCY. A few changes Team LunaCY made this year was to go to the electrical, computer, and software engineering club fest at Iowa State University to recruit engineering students to accomplish the task of making ART-E III autonomous. Team LunaCY chose to use LabView to program the robot and various sensors were installed to measure the distance between the robot and the surroundings to allow ART-E III to maneuver autonomously. Team LunaCY also built a testing arena to test prototypes and ART-E III in. To best replicate the competition arena at the Kennedy Space Center, a regolith simulant was made from sand, QuickCrete, and fly ash to cover the floor of the arena. Team LunaCY also installed fans to allow ventilation in the arena and used proper safety attire when working in the arena . With the additional practice in the testing arena and innovative robot design, Team LunaCY expects to make a strong appearance at the 2012 NASA Lunabotic Mining Competition. .

Heise, James↗
NASA NTRSDate not supplied

NASA DEVELOP’s Approach to Experiential Learning

The DEVELOP Program, part of NASA’s Applied Sciences’ Capacity Building Program, connects decision makers and early career individuals through rapid 10-week feasibility studies. These projects utilize Earth observation assets to address environmental issues and are conducted by small, interdisciplinary teams who work autonomously under the guidance of a cohort of science advisors. The teams tailor their research to create end products that support partner decision making processes. The connection to real-world decision making and direct communication with partner organizations drives meaningful experiences for DEVELOP participants that are directly applicable to future work in the geosciences. This poster will share DEVELOP’s model for offering meaningful experiences to its participants, address lessons learned over the past 20+ years, and highlight a variety of testimonials.

Capacity Building↗
NASA NTRS2006

Reconfigurable Software for Controlling Formation Flying

Software for a system to control the trajectories of multiple spacecraft flying in formation is being developed to reflect underlying concepts of (1) a decentralized approach to guidance and control and (2) reconfigurability of the control system, including reconfigurability of the software and of control laws. The software is organized as a modular network of software tasks. The computational load for both determining relative trajectories and planning maneuvers is shared equally among all spacecraft in a cluster. The flexibility and robustness of the software are apparent in the fact that tasks can be added, removed, or replaced during flight. In a computational simulation of a representative formation-flying scenario, it was demonstrated that the following are among the services performed by the software: Uploading of commands from a ground station and distribution of the commands among the spacecraft, Autonomous initiation and reconfiguration of formations, Autonomous formation of teams through negotiations among the spacecraft, Working out details of high-level commands (e.g., shapes and sizes of geometrically complex formations), Implementation of a distributed guidance law providing autonomous optimization and assignment of target states, and Implementation of a decentralized, fuel-optimal, impulsive control law for planning maneuvers.

Mueller, Joseph B.↗
NASA NTRS2018

NASA Autonomous Systems

This presentation provides an overview of autonomous systems at NASA, including definitions, description of the Autonomous Systems SCLT (Systems Capability Leadership Team), and a taxonomy of autonomous systems.

Fong, Terry↗
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 NTRS2004

From Present Surveying to Future Prospecting of the Asteroid Belt

We have applied a future mission architecture, the Autonomous Nano-Technology Swarm (ANTS), to a proposed mission for in situ survey, or prospecting, of the asteroid belt, the Prospecting Asteroid Mission (PAM) as part of a NASA 2003 Revolutionary Aerospace Concept (RASC) study. ANTS architecture builds on and advances recent trends in robotics, artificial intelligence, and materials processing to minimize costs and maximize effectiveness of space operations. PAM and other applications have been proposed for the survey of inaccessible, high surface area populations of great interest from the standpoint of resources and/or solar system origin. The ANTS architecture is inspired by the success of social insect colonies, a success based on the division of labor within the colonies in two key ways: 1) within their specialties, individual specialists generally outperform generalists, and 2) with sufficiently efficient social interaction and coordination, the group of specialists generally outperforms the group of generalists. Thus systems designed as ANTS are built from potentially very large numbers of highly autonomous, yet socially interactive, elements. The architecture is self-similar in that elements and sub-elements of the system may also be recursively structured as ANTS on scales ranging from microscopic to interplanetary distances. Here, we analyze requirements for the mission application at the low gravity target end of the spectrum, the Prospecting Asteroid Mission (PAM), and for specialized autonomous operations which would support this mission. ANTS as applied to PAM involves the activities of hundreds of individual specialist 'sciencecraft'. Most of them, called Workers, carry and operate eight to nine different scientific instruments, as listed in the table, including spectrometers, ranging and radio science devices, and imagers. The remaining specialists, Messenger/Rulers, provide communication and coordination functions among specialists operating autonomously as individuals, team members, and subswarms.

Clark, P. E.↗
NASA NTRS2012

Advancing Robotic Control for Space Exploration Using Robonaut 2

Robonaut 2, or R2, arrived on the International Space Station (ISS) in February 2011 and is currently being tested in preparation for its role initially as an Intra-Vehicular Activity (IVA) tool and eventually as a robot that performs Extra-Vehicular Activities (EVA). Robonaut 2, is a state of the art dexterous anthropomorphic robotic torso designed for assisting astronauts. R2 features increased force sensing, greater range of motion, higher bandwidth, and improved dexterity over its predecessor. Robonaut 2 is unique in its ability to safely allow humans in its workspace and to perform significant tasks in a workspace designed for humans. The current operational paradigm involves either the crew or the ground control team running semi-autonomous scripts on the robot as both the astronaut and the ground team monitor R2 and the data it produces. While this is appropriate for the check-out phase of operations, the future plans for R2 will stress the current operational framework. The approach described here will outline a suite of operational modes that will be developed for Robonaut 2. These operational modes include teleoperation, shared control, directed autonomy, and supervised autonomy, and they cover a spectrum of human involvement in controlling R2.

Badger, Julia↗
NASA NTRS2017

Machine Learning for Slow but Steady Interplanetary Construction

For prolonged manned missions to destinations such as the moon and Mars, there is a need for significant infrastructure construction ahead of time, such as habitats and landing pads. Unfortunately we have little experience in remote construction and using conventional methods is likely to be expensive, cumbersome and unreliable. Fortunately these challenges may be overcome by taking advantage of the long lead time for such missions and using teams of small and slow construction robots. We propose using teams of simple autonomous robots for this purpose that would perform continuous construction over a period of many years or even decades. While individual robot reliability will be low over such long time frames, system reliability will be maintained by using machine learning over simulations to achieve coordination and reconfigurations in the event of lost robots.

Tensegrity robots↗
NASA NTRS2024

Vertiport Management from Simulation to Flight: Continued Human Factors Assessment of Vertiport Operations

The Urban Air Mobility concept envisions a new future for transportation in urban environments, primarily focused around vertiport operations. A vertiport refers to an identifiable ground or elevated area used for the vertical takeoff and landing of an aircraft. One critical role within vertiport operations that has been proposed but is under-researched is the vertiport manager. The vertiport manager is expected to manage the ground-to-air operations at the vertiport, but their precise responsibilities, tasks, and challenges are currently undefined. The High Density Vertiplex Subproject at NASA is responsible for developing and testing technologies, concepts, and architectures that will support the infrastructure needed for terminal environments around vertiports. Our previous work investigated ground control station operators performing simulated and live flight operations within a remote operations environment. In the current study, we extended from our previous work to explore the role of a vertiport manager. Specifically, we investigated five participants serving as a vertiport manager across both simulated and live flight operations. We performed multiple knowledge elicitation techniques, a thematic content analysis of qualitative data, and naturalistic observations to create a list of insights, design and training recommendations, and a simplified cognitive task diagram of the vertiport manager’s primary task. Findings from this study are discussed within the context of the current debate on the degree to which this role should be automated. A third alternative is suggested to allow a human vertiport manager to be successful by teaming with increasingly autonomous systems, designing the role to improve safety and efficiency of Urban Air Mobility operations.

AAM↗
NASA NTRS2024

Vertiport Management from Simulation to Flight: Continued Human Factors Assessment of Vertiport Operations

The Urban Air Mobility concept envisions a new future for transportation in urban environments, primarily focused around vertiport operations. A vertiport refers to an identifiable ground or elevated area used for the vertical takeoff and landing of an aircraft. One critical role within vertiport operations that has been proposed but is under-researched is the vertiport manager. The vertiport manager is expected to manage the ground-to-air operations at the vertiport, but their precise responsibilities, tasks, and challenges are currently undefined. The High Density Vertiplex Subproject at NASA is responsible for developing and testing technologies, concepts, and architectures that will support the infrastructure needed for terminal environments around vertiports. Our previous work investigated ground control station operators performing simulated and live flight operations within a remote operations environment. In the current study, we extended from our previous work to explore the role of a vertiport manager. Specifically, we investigated five participants serving as a vertiport manager across both simulated and live flight operations. We performed multiple knowledge elicitation techniques, a thematic content analysis of qualitative data, and naturalistic observations to create a list of insights, design and training recommendations, and a simplified cognitive task diagram of the vertiport manager’s primary task. Findings from this study are discussed within the context of the current debate on the degree to which this role should be automated. A third alternative is suggested to allow a human vertiport manager to be successful by teaming with increasingly autonomous systems, designing the role to improve safety and efficiency of Urban Air Mobility operations.

AAM↗
NASA NTRS2017

Application of Human-Autonomy Teaming to an Advanced Ground Station for Reduced Crew Operations

Within human factors there is burgeoning interest in the "human-autonomy teaming" (HAT) concept as a way to address the challenges of interacting with complex, increasingly autonomous systems. The HAT concept comes out of an aspiration to interact with increasingly autonomous systems as a team member, rather than simply use automation as a tool. The authors, and others, have proposed core tenets for HAT that include bi-directional communication, automation and system transparency, and advanced coordination between human and automated teammates via predefined, dynamic task sequences known as "plays." It is believed that, with proper implementation, HAT should foster appropriate teamwork, thus increasing trust and reliance on the system, which in turn will reduce workload, increase situation awareness, and improve performance. To this end, HAT has been demonstrated and/or studied in multiple applications including search and rescue operations, healthcare and medicine, autonomous vehicles, photography, and aviation. The current paper presents one such effort to apply HAT. It details the design of a HAT agent, developed by Human Automation Teaming Solutions, Inc., to facilitate teamwork between the automation and the human operator of an advanced ground dispatch station. This dispatch station was developed to support a NASA project investigating a concept called Reduced Crew Operations (RCO); consequently, we have named the agent R-HATS. Part of the RCO concept involves a ground operator providing enhanced support to a large number of aircraft with a single pilot on the flight deck. When assisted by R-HATS, operators can monitor and support or manage a large number of aircraft and use plays to respond in real-time to complicated, workload-intensive events (e.g., an airport closure). A play is a plan that encapsulates goals, tasks, and a task allocation strategy appropriate for a particular situation. In the current implementation, when a play is initiated by a user, R-HATS determines what tasks need to be completed and has the ability to autonomously execute them (e.g., determining diversion options and uplinking new routes to aircraft) when it is safe and appropriate. R-HATS has been designed to both support end users and researchers in RCO and HAT. Additionally, R-HATS and its underlying architecture were developed with generalizability in mind as a modular software applicable outside of RCO/aviation domains. This paper will also discuss future further development and testing of RHATS.

automation↗
NASA NTRS2017

Flight Deck Surface Trajectory-Based Operations (STBO): A Four-Dimensional Trajectory (4DT) Simulation

Within human factors there is burgeoning interest in the Human-Autonomy Teaming (HAT) concept as away to address the challenges of interacting with complex, increasingly autonomous systems. The HAT concept comes out of an aspiration to interact with increasingly autonomous automation as a team member, rather than simply use automation as a tool. The authors, and others, have proposed core tenets for HAT that include bi-directional communication, automation and system transparency, and advanced coordination between human and automated teammates via predefined, dynamic task sequences known as plays (Shively et al., 2017). It is believed that, with proper implementation, HAT should foster appropriate teamwork, thus increasing trust and reliance on the system, which in turn will reduce workload, increase situation awareness, and improve performance. To this end, HAT has been demonstrated and/or studied in multiple applications including search and rescue operations (Nourbakhsh et al., 2005), healthcare and medicine (Tsui Yanco, 2007), autonomous vehicles (Parasuraman, Barnes, Cosenzo, Mulgund, 2007), photography (Lachter, Brandt, Sadler, Shively, in press), and aviation (Shively et al., in press). The current paper presents one such effort to apply HAT. It details the design of a R-HAT Agent developed as part of a NASA Research Agreement awarded to Human-Autonomy Teaming Solutions Inc. (HATS Inc), and developed in collaboration with the Human-Autonomy Teaming Laboratory at NASA Ames Research Center. The role of this Agent is to mediate interaction between the automation and the human operator of an advanced ground dispatch station, with this mediation based upon previously mentioned core tenets for HAT and the many lessons learned from the HAT research literature. This dispatch station was developed to support a NASA project investigating a concept called Reduced Crew Operations (RCO; Lachter, Brandt, Battiste, Matessa, Johnson, in press). Part of the RCO concept involves a ground operator providing enhanced support to a large number of aircraft with a single pilot on the flight deck. When assisted by the Agent, operators can monitor and support or manage a large number of aircraft and use plays to respond in real-time to complicated, workload-intensive events (e.g., an airport closure). A play is a plan that encapsulates goals, tasks, and a task allocation strategy appropriate for a particular situation. In the current implementation, when a play is initiated by a user, the Agent determines what tasks need to be done and has the ability to autonomously execute them (e.g., determining diversion options and uplinking new routes to aircraft) when it is safe and appropriate. The R-HAT Agent has been designed to both support end users and research in RCO and HAT. Additionally, the Agent and its underlying architecture were developed with generalizability in mind as a modular piece of software applicable outside of RCO aviation in domains such as those mentioned above. This paper will also discuss future further development and testing of the R-HAT Agent.

Bakowski, Deborah L.↗