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NASA NTRSDate not supplied

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight frameworks is paramount for establishing an effective architecture for autonomous systems. Hardware test flights are time-consuming and cost prohibitive during early system design and development. Simulation environments can be useful tools to accelerate algorithm development and testing. However, transitions from simulation to flight (sim-to-flight) can be challenging, unless systems are designed with this transition in mind and with the necessary capabilities built into the architecture and framework. One of the objectives of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. ATTRACTOR’s objective was to construct computational concepts of trustworthiness and justifiable trust in multi-agent autonomous teams, to inform future certification of safety-critical and time-critical autonomous systems in aviation. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation (ModSim) environment for test and evaluation of autonomous systems. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single-and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. The Autonomous Entity Operational Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications, enabling sim-to-flight with minimal configuration changes. Using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗

NASA NTRSDate not supplied

Bootstrapping Multi-Agent Unmanned Aerial Vehicle (UAV) System Integration Using Ground-Based Assets: Lessons Learned

The highly dynamic nature of UAVs imposes significant challenges when conducting initial testing ranging from safety risks posed by high-capacity lithium batteries and spinning propellers to rigorous timing demands on controllers and the consequences of failures mid-air. Flight testing of a single vehicle is time and labor intensive due to these challenges and more, and the complexity increases exponentially with the number of vehicles. While simulations and hardware-in-the-loop bench testing can provide adequate environments for preliminary validation, differences in system deployment architecture, software interfaces, and hardware infrastructure between simulation and a fleet of real UAVs create a sizable gap that must be navigated carefully during system integration. In support of the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project, which had the goal of establishing a basis of certification of trust and trustworthiness in multi-agent autonomous systems, this gap was tackled from two directions. First, a novel mixed-reality simulation environment was engineered to blur the transition from simulation to flight hardware. Second, a fleet of Unmanned Surface Vehicles (USVs) was developed as a test and evaluation platform that more closely represented the final aerial fleet while eliminating many of the risks associated with air vehicles. This paper delves into the second element, analyzing the efficacy of the USV platform in performing system integration testing for the UAV system. In this paper we present the USV fleet and its role in reducing the aforementioned gaps in deployment architecture, software interfaces, and hardware infrastructure when moving from simulation to flight. An overview of the hardware and software onboard the vehicles will be provided along with supporting infrastructure. The system integration process will be documented including results in supporting both the overarching design reference mission (DRM) of ATTRACTOR and individual research efforts conducted during the project. Finally, we will discuss some of the practical lessons learned regarding the testing, deployment, and operation of multi-agent autonomous systems.

Matthew P Vaughan↗

NASA NTRS2020

NASA SpaceCube Intelligent Multi-Purpose System for Enabling Remote Sensing, Communication, and Navigation in Mission Architectures

New, innovative CubeSat mission concepts demand modern capabilities such as artificial intelligence and autonomy, constellation coordination, fault mitigation, and robotic servicing –all of which require vastly more processing resources than legacy systems are capable of providing. Enabling these domains within a scalable, configurable processing architecture is advantageous because it also allows for the flexibility to address varying mission roles, such as a command and data-handling system, a high-performance application processor extension, a guidance and navigation solution, or an instrument/sensor interface. This paper describes the NASA SpaceCube Intelligent Multi-Purpose System (IMPS), which allows mission developers to mix-and-match 1U (10 cm × 10 cm) CubeSat payloads configured for mission-specific needs. The central enabling component of the system architecture to address these concerns is the SpaceCube v3.0 Mini Processor. This single-board computer features the 20nm Xilinx Kintex UltraScale FPGA combined with a radiation-hardened FPGA monitor, and extensive IO to integrate and interconnect varying cards within the system. To unify the re-usable designs within this architecture, the CubeSat Card Standard was developed to guide design of 1U cards. This standard define spinout configurations, mechanical, and electrical specifications for 1U CubeSat cards, allowing the backplane and mechanical enclosure to be easily extended. NASA has developed several cards adhering to the standard (System-on-Chip, power card, etc.), which allows the flexibility to configure a payload from a common catalog of cards.

Advanced Avionics, Hybrid Processing, Data Process↗

NASA NTRSDate not supplied

Integrated GNC Modifications and Performance Assessments for the Mars Ascent Vehicle, Spin-Stabilized Upper Stage Configuration

The objective of the Mars Ascent Vehicle (MAV), an element of the Mars Sample Return (MSR) campaign, is to successfully transport Martian surface samples from the Martian surface to a stable Low-Martian Orbit. Complete autonomy is required throughout ascent, and orbital insertion is constrained by tight dispersion boundaries. Given mass requirements to merely transport the MAV onto the Martian surface, reducing the total mass of the MAV reduces stress on all other campaign elements. An unguided, spin-stabilized second stage MAV allows for lower overall vehicle mass, at the cost of reduced GNC capability. This paper presents GNC modifications developed and employed to improve the performance of an unguided, spin-stabilized second stage MAV configuration. These updates include a predictive calculation scheme to solve the spin-stabilized attitude necessary for successful orbital insertion, and a spin-stabilized control law for a preliminary low-rate first stage spin-up. Trades were conducted focusing on first and second stage low-rate and high-rate spin rates (and other factors) in MSFC's MANTIS toolkit through 6-degree-of-freedom simulation and Monte Carlo analysis, and are presented here.

Dane Erickson↗

NASA NTRS2020

Simulation of a Representative Future Trajectory-Based Operations Environment

Trajectory-Based Operations in the National Airspace System is a key aspect of advanced air traffic management research. Trajectory-Based Operations focuses on modernizing the current operating paradigm to increase efficiency, predictability, resilience, and flexibility while migrating toward greater operational autonomy across the airspace. Research conducted at the National Aeronautics and Space Administration supports the transition from current airspace operations to Trajectory-Based Operations targeting a 2035-2045 implementation timeframe. Simulation scenarios that demonstrate a representative Trajectory-Based Operations environment in that timeframe, and enable the evaluation of advanced airborne tools such as strategic airborne trajectory management services, are necessary to support this research effort. This report describes characteristics and assumptions made about the future operating environment that were applied to a scenario development methodology to study a representative 2040 Trajectory-Based Operations environment in a simulation use case. This report also includes descriptions of the study design and analysis approach, discussion of the simulation results, and application of these scenarios to future research activities.

Trajectory Based Operations↗

NASA NTRSDate not supplied

Lagrange-Based Options for Relay Satellites to Eliminate Earth-Mars Communications Outages During Solar Superior Conjunctions

Recent conjunction class Mars human mission plans have generally assumed that there will inevitably be periods of communications outages between Earth and Mars. This has significant cost and risk implications for not only Mars missions, but also precursor missions in the lunar environment. But are there ways to avoid these outages? The potential exists for communications to be interrupted by Solar Superior Conjunctions (SSCs). Depending on the communications system, used, there can be communications outages up to as many as 78 days. The higher bandwidth systems experience the greatest outage. If these communications channels are interrupted due to an SSC, there are of course resulting challenges to mission operations. An outage of a few days to a few weeks could allow minor disturbances to become major concerns or even trigger subsystems failures. An inability to consult with the ground at the wrong time could result in loss of mission, loss of an element, or loss of life. Each planet in the solar system has a set of five Lagrange points associated with it and the Sun. At any given point in time, a planet or either its L4 or L5 point is visible to any other planet in the solar system, regardless of the position of the sun relative to the two. Thus, L4 and L5 have high value as relay systems to prevent communications outages. There are four sets of solar Lagrange points that may be of potential use in this study: Mars-Sun L4 and L5 points, Earth-Sun L4 and L5, Venus-Sun L4 and L5, and Mercury-Sun L4 and L5. A candidate relay satellite system will be identified, with consideration of both new technology developments and existing telecommunications satellites. This system may have implications for not only the Mars human mission architectures, but also Gateway and human lunar surface architectures as these other studies are tasked with paving the way to human Mars missions. If Mars communications outages can be eliminated, then the degree of autonomy necessary in Mars and precursor systems may be reduced.

Relay Satellite↗

NASA NTRS2020

Preparing for Human Missions to Mars: The role of ISS and Artemis as Analogs for Research and Technology Testing

The hazards of spaceflight to the human system are present in in varying degrees on different spaceflight platforms: altered gravity, isolation and confinement, distance from Earth, radiation, and hostile closed environments. A strategic view of the fidelity of hazards experienced on different platforms can shape the testing plans for human research and technology demonstration related to crew health and performance. Working across the international partnership, the International Space Station (ISS) is planning periods of modified operations to improve the fidelity of Mars simulations. To understand transit durations as an independent variable, a series of 1-year, 6-month and 30-45 day missions are being planned on ISS. Standard measurements across ISS missions of different durations, lunar missions and ground analogs offers the opportunity to distinguish different hazards and their effects in the context of the relevance to future mission concepts. The ISS partnership is planning for additional test cases that are aligned with Mars mission plans. (1) Evaluating crew performance capabilities when transitioning to gravity after long durations in microgravity representative of a Mars transit. (2) Simulating crew medical care under Mars-like autonomous operations. (3) Identification and testing of operations under communications delay and autonomy expected for Mars missions and the linked effects on behavioral health and performance of the crew. Artemis missions serve as a valuable analogs for Mars surface operations, with partial gravity and deep space radiation hazards, but with crewmembers that are probably more physically capable than their counterparts would be after a Mars transit. Getting unobtrusive data from early Artemis missions, and knowledge gained from operational experience as Artemis operations develop can improve engineering design, medical requirements and countermeasures, and ultimately ensure mission success on Mars. Linking Mars architectures with the plans and capabilities for ISS and Artemis allows us to plan to most operationally relevant tests of crew health and performance on current spaceflight missions to inform planning for future missions to Mars. By using human spaceflight platforms as well as ground simulation in an integrated way, the international community can improve exploration readiness, develop countermeasures and reduce risks of future human space missions.

International Space Station↗

NASA NTRSDate not supplied

Autonomous Spacecraft Attitude Control Using Deep Reinforcement Learning

While machine learning and spacecraft autonomy continue to gain research interest, significant work remains to be done in efficiently applying modern machine learning techniques to problems in space ight. This study presents a framework for deriving a discrete neural spacecraft attitude controller using reinforcement learning, a paradigm of machine learning, without the need for high-performance computing. The developed attitude controller is an approximately time-optimal solution to a highly constrained control problem, able to achieve well above industry-standard pointing accuracies. Control examples are also presented of the agent performing large-angle spacecraft slews in the developed simulation environment and future extensions of this work are discussed.

ATAP↗

NASA NTRSDate not supplied

The Exploration Crew Medical Officer of the Future

The current paradigm for Crew Medical Officer (CMO) credentials and training in Low Earth Orbit medical operations is likely to be insufficient for exploration-class missions. Long duration lunar surface stays and Mars missions will require increasing levels of autonomy of the CMO. This presentation will investigate the roles and responsibilities of the CMO of the future, including a discussion of clinical competency and training needs

Moriah S. Thompson↗

NASA NTRSDate not supplied

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight methods is paramount for establishing an effective architecture for autonomous systems. A fundamental objective of the ATTRACTOR (Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability) project was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation environment for test and evaluation of autonomous systems. The Autonomous Entity Operations Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single- and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. Together AEON and BEAM enable sim-to-flight with minimal configuration changes. By using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the lab and in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗

NASA NTRSDate not supplied

Bootstrapping Multi-Agent Unmanned Aerial Vehicle (UAV) System Integration Using Ground-Based Assets: Lessons Learned

In support of the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project, a fleet of unmanned ground vehicles (UGVs) was developed as a test and evaluation (T\&E) platform to reduce system integration gaps between simulation and live flight hardware. While simulation and hardware-in-the-loop bench testing provide adequate environments for preliminary validation, differences in system deployment architecture, software interfaces, and hardware infrastructure increase the risks to safety, property, and the project. Given ATTRACTOR’s goal of establishing a basis of certification of trust and trustworthiness in multi-agent autonomous systems, bridging these gaps was critical to successful project execution and feasibility assessment. In this paper we present the UGV fleet and its role in speeding up system integration, smoothing the transition from simulation to flight, and providing researchers an easy-to-use hardware test bed. An overview of the hardware and software on-board the vehicles is provided along with supporting infrastructure. The system integration process is documented including results in supporting both the overarching design reference mission (DRM) of ATTRACTOR and individual research efforts conducted since the creation of the fleet. Finally, we discuss the practical lessons learned regarding the testing, deployment, and operation of multi-agent autonomous systems.

Matthew P. Vaughan↗

NASA NTRS2021

Anomaly Detection, Active Learning, Precursor Identification,and Human Knowledge for Autonomous System Safety

The project Autonomy Teaming and TRajectories for ComplexTrusted Operational Reliability (ATTRACTOR) researched and developed Artificial Intelligence with application to multi-Unmanned Aerial Systems (UAS) missions. Such missions, like other complex systems-of-systems, are likely to have previously-unknown, safety relevant anomalies occur due to many possible factors including system failures or degradations, emergent behavior, changes in the environment in which the systems operate, changes in the way the systems are operated. We discuss the application of anomaly detection, active learning, and precursor identification to identify such anomalies and the conditions under which they are more likely to appear. We demonstrate results on simulated multi-UAS missions that show promise to be applied to real missions.

machine learning↗

NASA NTRSDate not supplied

Towards An Autonomous sUAS Operating in UTM TCL4+ and STEReO Fire Scenario

This study presents two sUAS payload point designs for enabling autonomous BVLOS flight in UTM TCL4+ urban environments and STEReO fire responses. The payload components for the first payload include an onboard computer and a 360 LIDAR. The second payload, which is a continuation of the first, adds a range finder altimeter, downward-facing monocular camera, forward-facing thermal and visible light dual camera, vehicle-to-vehicle radio modem, and Li-ion smart battery. The components are mounted on enclosed structural frames that were designed in-house. Placing the autonomy components onboard leverages the advantages of sUAS over manned aircraft such as low-cost, quick response time, and increased scalability. Autonomous capabilities include embedded processing, SLAM, object and fire detection, and V2V communication. Data processing is conducted onboard the aircraft to eliminate the dependency on a groundstation downlink. The first iteration of the payload is tested in both simulation and flight tests while the continuing iteration is in development.

Autonomous UAS↗

NASA NTRSDate not supplied

Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Text-based information about past engineering failures presents one possible solution by facilitating the retrieval of information that can inform new designs. However, identifying documents containing relevant information and extracting required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing-based framework is proposed to discover relevant knowledge from documents containing failure-related design information. Documents containing usable information are filtered using sentiment analysis based on a custom lexicon specialized for engineering design and by filtering out documents containing only irrelevant topics. Next, from the identified usable documents, information relating to engineering failures, contributing factors that can be controlled at design time (“risk factors”), and recommended preventative actions are extracted. Semantic similarity is then used to group similar pieces of extracted information for improved generalizability. The proposed framework is applied to NASA’s Lessons Learned Information System (LLIS). The framework can be used to identify documents containing usable failure-related design information from other databases, extract relevant information from these documents, and generalize the acquired knowledge such that it can be applied to novel systems.

Sequoia R. Andrade↗

NASA NTRSDate not supplied

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

Benjamin N Kelley↗

NASA NTRS2021

Inertial Transfer Concept for Autonomous In-Space Assembly

Rocket payload and fairing size have placed strict mass and volume limitations on single launch in-space structures. These limitations are what motivates In-Space Assembly (ISA)and where Inertial Transfer has potential to improve or augment ISA capabilities by utilizing an efficient multi-agent autonomous system to transport untethered payloads. Adoption of an inertial transfer approach significantly reduces system complexity, energy and time needed for assembly compared to conventional ISA capabilities, increasing the likelihood of early adoption for assembly. The Inertial Transfer concept is presented including autonomy capabilities, potential risks, and three system configurations. These configurations are based on the autonomous system’s available sensor coverage that tracks the payload’s state. Participating agents have well-defined roles, expectations, and assumptions in their physical organization and coordination of actions depending the mode of operation. The sensor information available to participating agents is considered as it affects the uncertainty in the estimated state and theactions required to capture the payload.

Multi-Agent↗

NASA NTRSDate not supplied

Monitoring Human Performance on Future Deep Space Missions Abstract

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

Kritina Holden↗

NASA NTRSDate not supplied

A Lunar Ground Truth of Microbes that are Integral to Sustaining Biological Life Support Systems

A lunar outpost such as Gateway or a Mars transit vehicle will use a semi-closed to closed loop biological life support system (BLSS) adapted for microgravity and high levels of radiation. A moon habitat will likely exist under partial gravity conditions and sustained levels of high radiation as a semi-closed loop system able to get resources from Earth via Gateway. Additionally, the moon will act as a closed-loop testbed for Mars habitat operations and will employ various elements of in situ resource utilization (ISRU). Perhaps the longest running BLSS collective study to date is that of the European MELiSSA (Micro Ecological Life Support System Alternative) project, a circular life support system, established to gain knowledge on regenerative systems aimed at the highest degree of autonomy to produce food, water and oxygen from mission wastes. This setup has evaluated the use of discrete microbial compartments and a higher plant compartment to carry out the necessary life support functions to support crew. Such compartments utilize thermophilic anaerobes to break down human and inedible plant wastes, photoheterotrophic bacteria that can further metabolize volatile fatty acids, nitrifying bacteria that can convert ammonium to plant and microalgae available nitrates, and photoautotrophic bacteria and higher plants which will convert carbon dioxide to oxygen, purify water, and provide food for human consumption. This being the most advanced BLSS model example to date -aside from the Yuegong-1, Chinese Lunar Palace- with the most defined compartment composition, and the assumption that other BLSSs will build upon such models, it becomes relevant to study the effects of the lunar environment upon the microbes that are integral to a BLSS. In ramping up to advanced stage BLSS systems, relatively simple experiments can be conducted on Artemis missions using continuous culturing and sampling of the model microbes, subsequently subjected to DNA sequencing for mutational analysis monitoring and chemical analysis to assess the sustained ability to carry out their hallmark biochemical processes efficiently. A suite of microbes should be assessed, prioritizing those with the duel capacity to be utilized in an BLSS and the ability to biochemically facilitate ISRU goals, such as the ability to transform the biogeochemistry of Moon or Martian regolith into materials that can support crop growth or extract elements of industrial significance such as aluminum or iron.

Biological Life Support Systems↗