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NASA NTRS2023

A Remote Vehicle Operations Center’s Role in Collecting Human Factors Data

The National Aeronautics and Space Administration is supporting research to develop a prototype remote vehicle operations center at Langley Research Center to explore current and future advanced air mobility operations using small unmanned aerial systems vehicles as surrogates for future, larger-scale passenger carrying vehicles. Data collected within the Remote Operations for Autonomous Missions (ROAM) Unmanned Aerial Systems (UAS) Operations Center will be used to explore different roles and responsibilities of remote operators managing multiple autonomous vehicles, with the goal of exploring human-autonomy teaming concepts that enable m:N operations (i.e., m operators managing N vehicles). ROAM has developed into a world-class research, development, and technology (RD&T) environment that can support both the collection of human factors data and the command and control of remote vehicles in beyond visual line of sight conditions. Presented in this paper is an overview of ROAM, with a focus on the design components that support human factors data collection and a review of initial usability results of the facility.

Human factors↗
NASA NTRS2019

Artificial Intelligence: Powering Human Exploration of the Moon and Mars

Artificial Intelligence (AI) is a growing field of computa- tional science techniques designed to mimic functions per- formed by people. Advancements in autonomy will depend on a portfolio of AI technologies. Automated planning and scheduling is a venerable field of study in AI, and is needed for a variety of mission planning functions. Plan execution technology is less well studied, but important for auton- omy and robotics. Specialized forms of automated reason- ing and machine learning are key technologies to enable fault management. Over the past decade, the NASA Au- tonomous Systems and Operations (ASO) project has devel- oped and demonstrated numerous autonomy enabling tech- nologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission op- erations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission oper-ations functions. Vehicle System Management uses AI tech- niques to turn the astronaut's spacecraft into a robot, allow- ing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots as crew assistants or proxies when the crew are not present. When these capabilities are used to enable astro- nauts to operate autonomously, they must be integrated with user interfaces, introducing numerous human factors con- siderations; when these capabilities are used to enable vehi- cle system management, they must be integrated with flight software, and run on embedded processors under the control of real-time operating systems.We first describe human spaceflight mission operations capabilities. The remainder of the paper will describe the ASO project, and the development and demonstration per- formed by ASO since 2011. We will describe the AI tech- niques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learn- ing based approaches. Finally, we conclude with an assess- ment of future development needs for AI to enable NASA's future Exploration missions.

Mission Operations↗
NASA NTRS2000

Immersive Environment Technologies for Mars Exploration

JPL's charter includes the unmanned exploration of the Solar System. One of the tools for exploring other planets is the rover as exemplified by Sojourner on the Mars Pathfinder mission. The light speed turnaround time between Earth and the outer planets precludes the use of teleoperated rovers so autonomous operations are built in to the current and upcoming generation devices. As the level of autonomy increases, the mode of operations shifts from low-level specification of activities to a higher-level specification of goals. To support this higher-level activity, it is necessary to provide the operator with an effective understanding of the in-situ environment and also the tools needed to specify the higher-level goals. Immersive environments provide the needed sense of presence to achieve this goal. Use of immersive environments at JPL has two main thrusts that will be discussed in this talk. One is the generation of 3D models of the in-situ environment, in particular the merging of models from different sensors, different modes (orbital, descent, and lander), and even different missions. The other is the use of various tools to visualize the environment within which the rover will be operating to maximize the understanding by the operator. A suite of tools is under development which provide an integrated view into the environment while providing a variety of modes of visualization. This allows the operator to smoothly switch from one mode to another depending on the information and presentation desired.

Wright, John R.↗
NASA NTRS2001

Human Centered Autonomous and Assistant Systems Testbed for Exploration Operations

The Engineering and Mission Operations Directorates at NASA Johnson Space Center are combining laboratories and expertise to establish the Human Centered Autonomous and Assistant Systems Testbed for Exploration Operations. This is a testbed for human centered design, development and evaluation of intelligent autonomous and assistant systems that will be needed for human exploration and development of space. This project will improve human-centered analysis, design and evaluation methods for developing intelligent software. This software will support human-machine cognitive and collaborative activities in future interplanetary work environments where distributed computer and human agents cooperate. We are developing and evaluating prototype intelligent systems for distributed multi-agent mixed-initiative operations. The primary target domain is control of life support systems in a planetary base. Technical approaches will be evaluated for use during extended manned tests in the target domain, the Bioregenerative Advanced Life Support Systems Test Complex (BIO-Plex). A spinoff target domain is the International Space Station (ISS) Mission Control Center (MCC). Prodl}cts of this project include human-centered intelligent software technology, innovative human interface designs, and human-centered software development processes, methods and products. The testbed uses adjustable autonomy software and life support systems simulation models from the Adjustable Autonomy Testbed, to represent operations on the remote planet. Ground operations prototypes and concepts will be evaluated in the Exploration Planning and Operations Center (ExPOC) and Jupiter Facility.

Malin, Jane T.↗
NASA NTRS2017

Development of Increasingly Autonomous Traffic Data Manager Using Pilot Relevancy and Ranking Data

NASA's Safe Autonomous Systems Operations (SASO) project goal is to define and safely enable all future airspace operations by justifiable and optimal autonomy for advanced air, ground, and connected capabilities. This work showcases how Increasingly Autonomous Systems (IAS) could create operational transformations beneficial to the enhancement of civil aviation safety and efficiency. One such IAS under development is the Traffic Data Manager (TDM). This concept is a prototype 'intelligent party-line' system that would declutter and parse out non-relevant air traffic, displaying only relevant air traffic to the aircrew in a digital data communications (Data Comm) environment. As an initial step, over 22,000 data points were gathered from 31 Airline Transport Pilots to train the machine learning algorithms designed to mimic human experts and expertise. The test collection used an analog of the Navigation Display. Pilots were asked to rate the relevancy of the displayed traffic using an interactive tablet application. Pilots were also asked to rank the order of importance of the information given, to better weight the variables within the algorithm. They were also asked if the information given was enough data, and more importantly the "right" data to best inform the algorithm. The paper will describe the findings and their impact to the further development of the algorithm for TDM and, in general, address the issue of how can we train supervised machine learning algorithms, critical to increasingly autonomous systems, with the knowledge and expertise of expert human pilots.

Le Vie, Lisa R.↗
NASA NTRSDate not supplied

Urban Air Mobility: A Control-Centric Approach to Addressing Technical Challenges

Urban Air Mobility (UAM) is an emerging aviation sector and is playing an integral part in the on-demand mobility revolution. UAM is powered by the convergence of advances in distributed electrical propulsion (DEP) and vehicle autonomy. The complexity of operations in the urban environment and the unconventional vehicle configurations designed to take advantage of new propulsion technologies, result in numerous challenges that benefit from a control-centric approach. In this talk we outline some of these challenges and present our current approach to addressing them. For example, in order to achieve full market potential and access to UAM, vehicle autonomous flight is required. A key barrier to autonomous flight in a large multi-agent system is dealing with off-nominal situations and contingencies in a safe and predictable manner. We present our approach to intelligent contingency management, and share recent results and open problems. Additionally, we discuss another major barrier to ubiquitous UAM – the noise signature produced by vehicles with multiple rotors. We present our approach to minimizing such noise within the framework of the acoustically-aware vehicle.

UAM↗
NASA NTRS2003

Autonomous Mission Manager for Rendezvous, Inspection and Mating

To meet cost and safety objectives, space missions that involve proximity operations between two vehicles require a high level of autonomy to successfully complete their missions. The need for autonomy is primarily driven by the need to conduct complex operations outside of communication windows, and the communication time delays inherent in space missions. Autonomy also supports the goals of both NASA and the DOD to make space operations more routine, and lower operational costs by reducing the requirement for ground personnel. NASA and the DoD have several programs underway that require a much higher level of autonomy for space vehicles. NASA's Space Launch Initiative (SLI) program has ambitious goals of reducing costs by a factor or 10 and improving safety by a factor of 100. DARPA has recently begun its Orbital Express to demonstrate key technologies to make satellite servicing routine. The Air Force's XSS-ll program is developing a protoflight demonstration of an autonomous satellite inspector. A common element in space operations for many NASA and DOD missions is the ability to rendezvous, inspect anclJor dock with another spacecraft. For DARPA, this is required to service or refuel military satellites. For the Air Force, this is required to inspect un-cooperative resident space objects. For NASA, this is needed to meet the primary SLI design reference mission of International Space Station re-supply. A common aspect for each of these programs is an Autonomous Mission Manager that provides highly autonomous planning, execution and monitoring of the rendezvous, inspection and docking operations. This paper provides an overview of the Autonomous Mission Manager (AMM) design being incorporated into many of these technology programs. This AMM provides a highly scalable level of autonomous operations, ranging from automatic execution of ground-derived plans to highly autonomous onboard planning to meet ground developed mission goals. The AMM provides the capability to automatically execute the plans and monitor the system performance. In the event of system dispersions or failures the AMM can modify plans or abort to assure overall system safety. This paper describes the design and functionality of Draper's AMM framework, presents concept of operations associated with the use of the AMM, and outlines the relevant features of the flight demonstrations.

Zimpfer, Douglas J.↗
NASA NTRS2020

Designing and Training for Appropriate Trust in Increasingly Autonomous Advanced Air Mobility Operations: A Mental Model Approach: Version 1

To enable effective human-autonomy teaming (HAT) in Advanced Air Mobility (AAM) operations, the current paper presents a theoretical framework to design and train for appropriate trust in automation. The novel contribution of this work resides in connecting the construct of trust to mental models and showing how this method could be used to enable emerging HAT concepts such as Adaptive Trust Calibration. To contextualize this framework, in section 2 we discuss simplified vehicle operations (SVO) and remote vehicle operations (RVO), which are leading operational concepts within AAM. In section 3 we describe our perspective on automation and increasingly autonomous systems and present a brief discussion on human-automation interaction and human-autonomy teaming. In section 4 we provide a detailed discussion on the construct of trust in automation. In section 5 we present a framework that associates mental models with trust through principles of transparent design. Finally, in section 6 we present three descriptive models for designing and training for appropriate trust in increasingly autonomous systems.

Human-Autonomy Teaming↗
NASA NTRSDate not supplied

HAT m:N Cognitive Task Analysis (CTA)

This Cognitive Task Analysis (CTA) study was designed to understand the capability of the m:N Tactical Operator (TO) interfaces developed by the Human-Autonomy Teaming Laboratory at NASA Ames to support operators responsible for simplified pilot operations of 100 independently operated small UAS (sUAS) in a constrained geographic area. The m:N sUAS TO interface includes a central Tactical Situation Display (TSD) digital map with moving icons reflecting the sUAS location and planned flight route. The interface also has two side panels. The left panel includes a tabular list of UAS assets and mission tasking, a list of recently viewed assets, and a list of events and alerts. The right panel includes a tabular list of UAS assets and their associated telemetry, text-based chat communication window, and a tabbed checklist window. This CTA was adapted from the incident-based applied cognitive task analysis (Militello & Hutton, 1998) and included demographics questions, scenario-based simulations, a task diagram and knowledge audit methods. In addition to examining the support provided by this m:N sUAS TO interface, this CTA study, conducted with aviation subject matter experts in analogous roles to the future tactical operator, was designed to illuminate and project likely cognitive requirements of the tactical operator. Interviewees participated in two scenario-based simulations using the m:N sUAS TO interfaces. In the first simulation, the interviewees supervised 12 sUAS operating in downtown San Diego, California transiting to and from a central sUAS Hive, restaurant locations, and customer drop off locations. Interviewees were asked to react to a UAS Volume Reservation (UVR) event with a two-phase impact on food delivery operations. In the second scenario, the interviewees supervised 100 sUAS operating in the same airspace and with the same mission. Interviewees used the interfaces to recognize and react to two sUAS air vehicle problems. After each scenario, we asked the interviewees a semi-structured list of questions to elicit their reflections about using the interfaces. Interviewees were confident in their ability to respond to two off-nominal situations in each simulation. Interviewees felt that, given high levels of automation on the sUAS, they would be able to manage the events without requiring additional support or handing off the sUAS to a colleague or supervisor in both the n=12 or n=100 sUAS settings. In the n=12 sUAS condition, interviewees used the center map to understand the asset location and progress along with mission tasking. An additional display window, Asset Telemetry, helped interviewees understand battery state and sUAS altitude. When the number of sUAS increased, interviewees altered their behavior. Rather than maintaining awareness of individual assets, interviewees appeared to become more reactive, managing exceptions. Interviewees reported that they spent less time looking at the nominal aircraft, and focused their attention primarily on the off-nominal aircraft. In addition, in the second simulation with n=100 sUAS, interviewees reported that they relied more on the side panels (Mission Timeline and Asset Telemetry) to gather information.

human-autonomy teaming↗
NASA NTRSDate not supplied

Risk-Reduction Autonomy Implementation to Enable NASA Artemis Missions

To achieve NASA’s Artemis program mission objectives a high level of autonomy that is ubiquitous throughout the systems that are being developed will be necessary. The autonomous systems of Artemis will require a distributed autonomy capability, with autonomous systems organized functionally in a hierarchical architecture, where systems at higher levels of the hierarchy have authority over systems at lower levels. The challenge of developing autonomy technologies and Concepts of Operations (ConOps) for Artemis has been undertaken by the NASA Gateway Working Group. This group has developed requirements, architectures, ConOps, and interface control documents (ICDs), in the context of a hierarchical distributed architecture that includes the following: a Vehicle System Manager (VSM) that autonomously manages the entire Gateway; Module System Managers (MSMs) that autonomously manage each module; and System Managers (SMs) that autonomously manage systems within a module (i.e. ECLSS). A substantially high level of autonomy needs to be achieved by each element of the hierarchy (VSM, MSM, SM) to meet requirements for uncrewed operations; this includes conditions that will have minimal and/or delayed ground intervention (i.e. requirements for sustainability for months of operation without crew or ground support). To advance an implementation of this autonomy design (Gateway Autonomy Design – GAD), a collaboration was established between the Autonomous Systems Laboratory (ASL) at NASA Stennis Space Center and Lockheed Martin. The objectives of this partnership were the following: (1) to implement autonomy at the VSM, MSM, and SM levels; (2) to implement communications among a VSM, 2 MSMs, ORION (a visiting vehicle somewhat equivalent to a module) and 1 SM (a power system), and (3) test autonomous operations with representative use cases. A SM backed by a high-fidelity simulation was created to facilitate demonstrations of use cases that originated in a system of a module. Communication between the VSM and MSMs was implemented according to Concepts of Operations and Interface Control Documents (ICDs). Demonstrations were conducted to address nominal and off-nominal operations and multi-module interactions with the VSM. Additionally, user interfaces were created to provide awareness about ongoing processes and results while enhancing the demonstration. Demonstrations included the following use cases: (1) Orion as visiting vehicle registers with VSM; (2) VSM reschedules a module’s timelines when another module’s MSM task fails; and (3) a module’s Power System Manager (PSM) demonstration that included component failure diagnostics, tracing component failure to effected components, which in turn, reports failure information up to the VSM for acknowledgement and display. This paper will describe the detailed technology and autonomous systems developed, and the integrated multi-module demonstrations conducted. Also, challenges that must be met to fully implement the GAD defined by Gateway will be addressed.

Fernando Figueroa↗
NASA NTRS1977

A discrete adaptive guidance system for a roving vehicle

An adaptive guidance technique which provides a self-correcting path following capability in an environment sensitive semi-autonomous roving robot is described. A real-time maneuver planning function performs tradeoffs between speed, magnitude of expected deviations and accelerations to best satisfy the design goals of vehicle safety and reliability, subsystem autonomy, performance accuracy and operational efficiency. The appropriate combination of maneuver parameters is selected through an iterative process using knowledge of vehicle performance characteristics, environmental model updates and vehicle state

Miller, J. A.↗
NASA NTRS1985

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures.

Firschein, O.↗
NASA NTRS1985

NASA space station automation: AI-based technology review. Executive summary

Research and Development projects in automation technology for the Space Station are described. Artificial Intelligence (AI) based technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics.

Firschein, O.↗
NASA NTRS1985

Space Station attitude control - An overview of requirements and solutions

Attitude control and various structural aspects of NASA's permanent manned Space Station are discussed in the framework of design flexibility, obsolescence as a deterent to a long operational life, modularity, and autonomy. Among the variable factors of specific importance, consideration is given to internal factors, such as inertial variations (up to 400 percent), center of mass movements (up to 28 m), and shifts in modal characteristics, as well as to external torque shifts associated with aerodynamic moments and gravity gradients. The compatibility among multiple-user requirements is also considered. As the Station is a low-orbit spacecraft, its attitude will be greatly affected by the inhomogeneity of the atmosphere, placing a priority on the attitude control techniques. A combination of passive (spring-mass-damper suspension systems) and active (sensor/disturbance-canceling device) systems are expected to be used.

Buchanan, H. J.↗
NASA NTRS1987

Autonomous intelligent systems

NASA's Office of Aeronautics and Space Technology (OAST) has instituted a 'Systems Autonomy' technology development program aimed at the creation of increasingly sophisticated AI systems. Expert system production will in due course be followed by fully autonomous AI systems capable of the complex mission objectives of the Space Station, as well as of lunar and Martian exploration. AI systems must demonstrate the degree of their autonomy in a real-time operational environment, and then undergo mating to robotics technologies. OAST's Systems Autonomy Program and Robotics Program are discussed with a view to anticipated difficulties in their integration.

Lum, H.↗
NASA NTRS2003

Methods for Determining the Level of Autonomy to Design into a Human Spaceflight Vehicle: A Function Specific Approach

The next-generation human spaceflight vehicle is in a unique position to realize the benefits of more than thirty years of technological advancements since the Space Shuttle was designed. Computer enhancements, the emergence of highly reliable decision-making algorithms, and an emphasis on efficiency make an increased use of autonomous systems highly likely. NASA is in a position to take advantage of these advances and apply them to the human spaceflight environment. One of the key paradigm shifts will be the shift, where appropriate, of monitoring, option development, decision-making, and execution responsibility from humans to an Autonomous Flight Management (AFM) system. As an effort to reduce risk for development of an AFM system, NASA engineers are developing a prototype to prove the utility of previously untested autonomy concepts. This prototype, called SMART (Spacecraft Mission Assessment and Replanning Tool), is a functionally decomposed flight management system with an appropriate level of autonomy for each of its functions. As the development of SMART began, the most important and most often asked question was, How autonomous should an AFM system be? A thorough study of the literature through 2002 surrounding autonomous systems has not yielded a standard method for designing a level of autonomy into either a crewed vehicle or an uncrewed vehicle. The current focus in the literature on defining autonomy is centered on developing IQ tests for built systems. The literature that was analyzed assumes that the goal of all systems is to strive for complete autonomy from human intervention, rather than identifying how autonomous each function within the system should have been. In contrast, the SMART team developed a method for determining the appropriate level of autonomy to be designed into each function within a system. This paper summarizes the development of the Level of Autonomy Assessment Tool and its application to the SMART project.

AFM (AUTONOMOUS FLIGHT MANAGEMENT)↗