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NASA NTRS2003

A New Simulation Framework for Autonomy in Robotic Missions

Autonomy is a key factor in remote robotic exploration and there is significant activity addressing the application of autonomy to remote robots. It has become increasingly important to have simulation tools available to test the autonomy algorithms. While indus1;rial robotics benefits from a variety of high quality simulation tools, researchers developing autonomous software are still dependent primarily on block-world simulations. The Mission Simulation Facility I(MSF) project addresses this shortcoming with a simulation toolkit that will enable developers of autonomous control systems to test their system s performance against a set of integrated, standardized simulations of NASA mission scenarios. MSF provides a distributed architecture that connects the autonomous system to a set of simulated components replacing the robot hardware and its environment.

Flueckiger, Lorenzo↗

NASA NTRS2003

Mission Simulation Facility: Simulation Support for Autonomy Development

The Mission Simulation Facility (MSF) supports research in autonomy technology for planetary exploration vehicles. Using HLA (High Level Architecture) across distributed computers, the MSF connects users autonomy algorithms with provided or third-party simulations of robotic vehicles and planetary surface environments, including onboard components and scientific instruments. Simulation fidelity is variable to meet changing needs as autonomy technology advances in Technical Readiness Level (TRL). A virtual robot operating in a virtual environment offers numerous advantages over actual hardware, including availability, simplicity, and risk mitigation. The MSF is in use by researchers at NASA Ames Research Center (ARC) and has demonstrated basic functionality. Continuing work will support the needs of a broader user base.

Pisanich, Greg↗

NASA NTRS2008

Lunar and Mars Exploration: The Autonomy Factor

Long duration space flight crews have relied heavily on almost constant communication with ground control mission support. Ground control teams provide vehicle status and system monitoring, while offering near real time support for specific tasks, emergencies, and ensuring crew health and well being. With extended exploration goals to lunar and Mars outposts, real time communication with ground control teams and the ground s ability to conduct mission monitoring will be very limited compared to the resources provided to current International Space Station (ISS) crews. An operational shift toward more autonomy and a heavier reliance on the crew to monitor their vehicle and operations will be required for these future missions. NASA s future exploration endeavors and the subsequent increased autonomy will require a shift in crew skill composition, i.e. engineer, doctor, mission specialist etc. and lead to new training challenges and mission scenarios. Specifically, operational and design changes will be necessary in many areas including: Habitat Infrastructure and Support Systems, Crew Composition, Training, Procedures and Mission Planning. This paper will specifically address how to apply ISS lessons learned to further use ISS as a test bed to address decreased amounts of ground support to achieve full autonomous operations for lunar and Mars missions. Understanding these lessons learned and applying them to current operations will help to address the future impacts of increased crew autonomy for the lunar and Mars outposts and pave the way for success in increasingly longer mission durations.

Rando, Cynthia M.↗

NASA NTRS2006

Enhancing Science and Automating Operations using Onboard Autonomy

In this paper, we will describe the evolution of the software from prototype to full time operation onboard Earth Observing One (EO-1). We will quantify the increase in science, decrease in operations cost, and streamlining of operations procedures. Included will be a description of how this software was adapted post-launch to the EO-1 mission, which had very limited computing resources which constrained the autonomy flight software. We will discuss ongoing deployments of this software to the Mars Exploration Rovers and Mars Odyssey Missions as well as a discussion of lessons learned during this project. Finally, we will discuss how the onboard autonomy has been used in conjunction with other satellites and ground sensors to form an autonomous sensor-web to study volcanoes, floods, sea-ice topography, and wild fires. As demonstrated on EO-1, onboard autonomy is a revolutionary advance that will change the operations approach on future NASA missions...

Earth Observing One (EO-1)↗

NASA NTRS2001

Architectures and Evaluation for Adjustable Control Autonomy for Space-Based Life Support Systems

In the past five years, a number of automation applications for control of crew life support systems have been developed and evaluated in the Adjustable Autonomy Testbed at NASA's Johnson Space Center. This paper surveys progress on an adjustable autonomous control architecture for situations where software and human operators work together to manage anomalies and other system problems. When problems occur, the level of control autonomy can be adjusted, so that operators and software agents can work together on diagnosis and recovery. In 1997 adjustable autonomy software was developed to manage gas transfer and storage in a closed life support test. Four crewmembers lived and worked in a chamber for 91 days, with both air and water recycling. CO2 was converted to O2 by gas processing systems and wheat crops. With the automation software, significantly fewer hours were spent monitoring operations. System-level validation testing of the software by interactive hybrid simulation revealed problems both in software requirements and implementation. Since that time, we have been developing multi-agent approaches for automation software and human operators, to cooperatively control systems and manage problems. Each new capability has been tested and demonstrated in realistic dynamic anomaly scenarios, using the hybrid simulation tool.

Malin, Jane T.↗

NASA NTRS2015

Who's Got the Bridge? - Towards Safe, Robust Autonomous Operations at NASA Langley's Autonomy Incubator

NASA aeronautics research has made decades of contributions to aviation. Both aircraft and air traffic management (ATM) systems in use today contain NASA-developed and NASA sponsored technologies that improve safety and efficiency. Recent innovations in robotics and autonomy for automobiles and unmanned systems point to a future with increased personal mobility and access to transportation, including aviation. Automation and autonomous operations will transform the way we move people and goods. Achieving this mobility will require safe, robust, reliable operations for both the vehicle and the airspace and challenges to this inevitable future are being addressed now in government labs, universities, and industry. These challenges are the focus of NASA Langley Research Center's Autonomy Incubator whose R&D portfolio includes mission planning, trajectory and path planning, object detection and avoidance, object classification, sensor fusion, controls, machine learning, computer vision, human-machine teaming, geo-containment, open architecture design and development, as well as the test and evaluation environment that will be critical to prove system reliability and support certification. Safe autonomous operations will be enabled via onboard sensing and perception systems in both data-rich and data-deprived environments. Applied autonomy will enable safety, efficiency and unprecedented mobility as people and goods take to the skies tomorrow just as we do on the road today.

Allen, B. Danette↗

NASA NTRS2017

Medical Systems Engineering to Support Mars Mission Crew Autonomy

Human spaceflight missions to Mars face exceptionally challenging resource limitations that far exceed those faced before. Increasing transit times, decreasing opportunity for resupply, communications challenges, and extended time to evacuate a crew to definitive medical care dictate a level of crew autonomy in medical care that is beyond the current medical model. To approach this challenge, a medical systems engineering approach is proposed that relies on a clearly articulated Concept of Operations and risk analysis tools that are in development at NASA. This paper proposes an operational clinical model with key terminology and concepts translated to a controls theory paradigm to frame a common language between clinical and engineering teams. This common language will be used for design and validation of an exploration medical system that is fully integrated into a Mars transit vehicle. This approach merges medical simulation, human factors evaluation techniques, and human-in-the-loop testing in ground based analogs to tie medical hardware and software subsystem performance and overall medical system functionality to metrics of operational medical autonomy. Merging increases in operational clinical autonomy with a more restricted vehicle system resource scenario in interplanetary spaceflight will require an unprecedented level of medical and engineering integration. Full integration of medical capabilities into a Mars vehicle system may require a new approach to integrating medical system design and operations into the vehicle Program structure. Prior to the standing-up of a Mars Mission Program, proof of concept is proposed through the Human Research Program.

Antonsen, Erik↗

NASA NTRS2015

Supervised Remote Robot with Guided Autonomy and Teleoperation (SURROGATE): A Framework for Whole-Body Manipulation

The use of the cognitive capabilties of humans to help guide the autonomy of robotics platforms in what is typically called "supervised-autonomy" is becoming more commonplace in robotics research. The work discussed in this paper presents an approach to a human-in-the-loop mode of robot operation that integrates high level human cognition and commanding with the intelligence and processing power of autonomous systems. Our framework for a "Supervised Remote Robot with Guided Autonomy and Teleoperation" (SURROGATE) is demonstrated on a robotic platform consisting of a pan-tilt perception head, two 7-DOF arms connected by a single 7-DOF torso, mounted on a tracked-wheel base. We present an architecture that allows high-level supervisory commands and intents to be specified by a user that are then interpreted by the robotic system to perform whole body manipulation tasks autonomously. We use a concept of "behaviors" to chain together sequences of "actions" for the robot to perform which is then executed real time.

Hebert, Paul↗

NASA NTRS2019

Impact of the CIF Program on NASA’s Autonomy Capability: the NASA Platform for Autonomous Systems (NPAS)

The CIF (Center Innovation Fund) Program provided support to advance the autonomous operation technology that was at a low TRL (Technological Readiness Level) scale to push the technology to a higher TRL where then other programs could sponsor further advances and infusion of the capability into other projects. This is the case with NPAS (NASA Platform for Autonomous Systems). Autonomy capability encompasses technologies that enable a system to function independent from operators, except for times of low frequency high level interactions. With this degree of autonomy, the system has to be able to determine its condition (Integrated System Health Management - ISHM), and apply autonomy strategies that permit progression towards achieving a mission, in spite of anomalies or external unforeseen disturbances.

Armstrong, Curtis D.↗

NASA NTRS2016

Designing and Building a Crew-Centric Mobile Scheduling and Planning Tool for Exploring Crew Autonomy Concepts Onboard the International Space Station

During 2015, as part of our ongoing research on crew autonomy, our team adapted and extended the plan viewing tool, Playbook, into a mobile tool for exploring crew autonomy onboard the International Space Station (ISS). This work enables crewmembers to intuitively re-plan and schedule on their own with limited input from mission control. Designed for ease of use, re-planning is composed of simple drag and drop interactions and learning the tool requires little to no training. Playbook was successfully uplinked onto ISS as a technology demonstration and completed its onboard ground and crew checkouts in August and September 2015, respectively. We will discuss and describe how Playbook was able to integrate with actual ISS operational plans and operational procedures without disrupting real mission operations. This work will also outline the design and technical constraints required to build a collaborative tool that allows multiple users to simultaneously self-schedule and synchronize during communication dropouts in the restricted networking and computing environment onboard ISS. Finally, we will explore the aspects of crew autonomy that this technology enables as well as discuss our next steps and possible extensions.

ISS↗

NASA NTRSDate not supplied

Autonomy Voice Assistant for NPAS (NASA Platform for Autonomous Systems)

A prototype voice interaction system, Autonomy Voice Assistant (AVA), is described in this paper. AVA is designed to seamlessly integrate into the NASA Platform for Autonomous Systems (NPAS), an autonomy software platform, and to enable an operator to interact with NPAS autonomy applications through voice conversations. By integrating VA with NPAS, a major enhancement to NPAS applications is facilitated, enabling interaction through natural language expressions. An AVA prototype has been designed incorporating two principles:(1) self-containment (no external data or computations required), and (2) a readily modifiable, reconfigurable, and flexible architecture. By using voice messages in an NPAS application, an additional layer of user interface capability is enabled, thereby enhancing a user’s overall experience. Advancements, over the past several decades in speech recognition and natural language processing technologies has made it possible for AVA to implement robust messaging capabilities while still being lightweight. The main objective of incorporating a voice assistant like AVA is to augment the number and effectiveness of interactions a user has with a system that typically uses mouse-based interaction, while simultaneously enriching the user experience and providing heightened system awareness.

Lucian Murdock↗

NASA NTRS2021

A Systematic Approach to Developing Paths Towards Airborne Vehicle Autonomy

Advanced Air Mobility (AAM) demands greater levels of aircraft autonomy than are currently implemented today. To enable this requirement, novel aircraft functionalities and technologies as well as supporting airworthiness and operational regulations are required. A structured method to derive a comprehensive list of aircraft level decision-making functions is defined and applied. The resulting function set is programmed into an ontology, and enables autonomous decision-making through the application of a defined decision-making process. Paths to implementing the functions are generated by applying a structured four step method. By surveying current technologies, airspace, procedures and regulations, the paths generation method defines incremental paths to autonomy that the current regulatory environment can support. Opportunities to implement novel technologies and functions are identified, and regulatory mechanisms supporting their implementation are underscored. The analysis provides the tools to further define aircraft functions and paths to their implementation, while demonstrating that for particular use cases, aircraft autonomy is attainable in the medium-term.

Paul Vajda↗

NASA NTRSDate not supplied

Resilient Autonomy in the Face of Adversity

The NASA Resilient Autonomy Project developed a software framework that implemented a Run Time Assurance (RTA) architecture that leveraged ASTM International’s F3269 Industry Standard for safely bounding complex behavior in aircraft. This framework was called the Expandable Variable Autonomy Architecture, or EVAA. EVAA was developed during the height of the Covid-19 lockdown that caused the Resilient Autonomy team to pivot from flight test to distributed simulator testing. EVAA was developed to be platform and mission agnostic where platform specifics were behind a hardware abstraction layer that EVAA called a Coupler. EVAA was able to host multiple safety monitors that could resolve individual safety hazards. EVAA was able to resolve priority conflicts when multiple safety hazards needed to be resolved simultaneously and was able to resolve highly complex situations in a safe manner that could exceed human capabilities.

Ethan Williams↗

NASA NTRS2021

Case Studies in Verifying Spacecraft Autonomy

Spacecraft, operating with a time-to-effect faster than human command response, or with absent or delayed communication, have depended on autonomy. It is likely that this reliance will continue, and increase further in future missions. Spacecraft autonomy is used when operator intervention is not available or feasible, or when dynamic aspects of a spacecraft situation cannot be predicted in advance. This paper provides case studies of several successfully verified autonomous software systems across multiple spacecraft, flight phases (launch, transit, entry and landing, science utilization), and software classifications, and can serve as examples to future missions in methods of successfully assuring spacecraft autonomy.

Johnson, Stephen B.↗

NASA NTRSDate not supplied

A Simulation Architecture for Air Traffic Over Urban Environments Supporting Autonomy Research in Advanced Air Mobility

NASA is conducting investigations into Advanced Air Mobility (AAM) concepts, aircraft, and operations. One of the most challenging scenarios for AAM will be enabling safe routine access into densely populated urban centers. To address challenges in the urban environment, a moderately high-fidelity simulation capability is needed to investigate AAM flight operations over a regional area for the development and evaluation of autonomy technologies. This paper describes a system to simulate flight operations around regions such as the San Francisco-Oakland Bay area at a moderately-high scale (10's-100's of flights) that incorporates detailed vehicle models and control necessary to support research in airborne autonomy. The flight vehicle utilizes NASA AAM concept vehicle dynamics integrated with a custom flight management system and flight control system to accurately simulate all phases of flight. Glass cockpit displays have been developed for monitoring aircraft operation over a detailed simulated urban environment. Simulation models have been integrated to simulate air and ground-based sensors, such as radar and LIDAR. The commercial X-Plane software package is used as a rendering engine to mimic vision-based sensors (such as onboard and ground-based cameras) at various times of day and in various weather conditions over a relatively detailed graphical model of the city. The paper presents a detailed illustration of the simulation and software architecture used for traffic over this urban region. This system is enabling the evaluation of NASA research concepts in autonomy for urban AAM operations on the path toward aircraft flight test evaluation.

Keerthana Kannan↗

NASA NTRSDate not supplied

A Simulation Architecture for Air Traffic Over Urban Environments Supporting Autonomy Research in Advanced Air Mobility

As part of its research, NASA investigates concepts, aircraft, and operations related to Advanced Air Mobility (AAM). One of the most challenging scenarios for AAM will be enabling safe routine access near densely populated urban centers. AAM flight operations over a regional area require a moderately high-fidelity simulation capability to develop and evaluate autonomy technologies in the urban environment. This paper aims to describe a system to simulate flight operations around regions such as the San Francisco-Oakland Bay area at a moderately-high scale (tens to hundreds of flights) that incorporates detailed vehicle models and control necessary to support research in airborne autonomy. The flight vehicle utilizes NASA AAM concept vehicle dynamics integrated with a custom flight management system and flight control system to simulate all flight phases accurately. The simulation incorporates a detailed simulated urban environment and includes glass cockpit displays to monitor aircraft operations. Simulation models integrate to simulate air and ground-based sensors, such as Radar and LiDAR. As a commercially available rendering engine, X-Plane 11 is used as the renderer to simulate vision-based sensors (such as onboard and ground-based cameras) with a detailed graphical model of a city at different times of the day and weather conditions. This paper presents the simulation and software architecture used for simulating AAM traffic over this urban region. This system enables the evaluation of NASA research concepts in autonomy for urban AAM operations on the path toward flight test evaluation.

distributed sensing↗

NASA NTRSDate not supplied

A Simulation Architecture for Air Traffic Over Urban Environments Supporting Autonomy Research in Advanced Air Mobility

Distributed sensing↗

NASA NTRS2023

Autonomy Verification & Validation Roadmap and Vision 2045

Advanced capabilities planned for the next generation of autonomous and increasingly autonomous air vehicles will include non-traditional components based on artificial intelligence, machine learning, and complex optimization and planning algorithms. These complex components will be used to provide enhanced safety and high-level decision-making functions. However, there are serious barriers to the deployment of autonomous aircraft in the National Airspace System (NAS). Current civil aviation certification processes are based on the concept that the correct behavior of a system or a component must be completely specified and verified prior to operation. This report from the Autonomy Verification and Validation (V&V) Roadmap and Vision 2045 project presents the most recent effort to build a comprehensive list of verification challenges and needs for autonomous aircraft, a roadmap to meet those autonomy V&V needs, the services they can enable, and point to the certification gaps they fill. To accomplish these goals, we assembled a team of world-class researchers from the aerospace industry (Boeing, Collins Aerospace, and GeneralElectric) and academia (University of Michigan, University of Texas, and Massachusetts Institute of Technology) with deep expertise in autonomy, aerospace systems, and assurance of Artificial Intelligence/machine learning systems.

Software Assurance↗