Science-driven Spacecraft Autonomy
In this paper we focus on the use of autonomy to enable and enhance scientific goals for spaceborne missions.
aerospace system Autonomous Spacecraft↗
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Toward intelligent robot systems in aerospace
The incorporation of progressively more autonomous capabilities in spacecraft has been made possible by advancements in electronics technologies for sensors, communication, and computing equipment; as a result, space missions have been able to cope with ever-increasing complexity and data throughputs, as demonstrated by the six-order-of-magnitude increase in planetary mission data rates. In order to continue this pace of development into the Space Station era, NASA has accelerated its R&D in automation and robotics, with emphasis on autonomous, knowledge-based and expert system-employing technologies and AI.
Heer, Ewald↗
Power Management Software for the New Millennium
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New Millennium Program NMP spacecraft automation a↗
Advanced Information Processing System - Fault detection and error handling
The Advanced Information Processing System (AIPS) is designed to provide a fault tolerant and damage tolerant data processing architecture for a broad range of aerospace vehicles, including tactical and transport aircraft, and manned and autonomous spacecraft. A proof-of-concept (POC) system is now in the detailed design and fabrication phase. This paper gives an overview of a preliminary fault detection and error handling philosophy in AIPS.
Lala, J. H.↗
A Briefing on Metrics and Risks for Autonomous Decision-Making in Aerospace Applications
Significant technology advances will enable future aerospace systems to safely and reliably make decisions autonomously, or without human interaction. The decision-making may result in actions that enable an aircraft or spacecraft in an off-nominal state or with slightly degraded components to achieve mission performance and safety goals while reducing or avoiding damage to the aircraft or spacecraft. Some key technology enablers for autonomous decision-making include: a continuous state awareness through the maturation of the prognostics health management field, novel sensor development, and the considerable gains made in computation power and data processing bandwidth versus system size. Sophisticated algorithms and physics based models coupled with these technological advances allow reliable assessment of a system, subsystem, or components. Decisions that balance mission objectives and constraints with remaining useful life predictions can be made autonomously to maintain safety requirements, optimal performance, and ensure mission objectives. This autonomous approach to decision-making will come with new risks and benefits, some of which will be examined in this paper. To start, an account of previous work to categorize or quantify autonomy in aerospace systems will be presented. In addition, a survey of perceived risks in autonomous decision-making in the context of piloted aircraft and remotely piloted or completely autonomous unmanned autonomous systems (UAS) will be presented based on interviews that were conducted with individuals from industry, academia, and government.
Frost, Susan↗
NASA NDE Applications for Mobile MEMS Devices and Sensors
NASA would like new devices and sensors for performing nondestructive evaluation (NDE) of aerospace vehicles. These devices must be small in size/volume, mass, and power consumption. The devices must be autonomous and mobile so they can access the internal structures of aircraft and spacecraft and adequately monitor the structural health of these craft. The platforms must be mobile in order to transport NDE sensors for evaluating structural integrity and determining whether further investigations will be required. Microelectromechanical systems (MEMS) technology is crucial to the development of the mobile platforms and sensor systems. This paper presents NASA s needs for micro mobile platforms and MEMS sensors that will enable NDE to be performed on aerospace vehicles.
Wilson, William C.↗
Enabling a Voice Management System for Space Applications, Design and Software Development
Sustainable missions, beyond low Earth orbit, will require autonomous capabilities in order to achieve NASA’s Artemis program objectives. Correspondingly, the crew must have a means to efficiently interact with these autonomous systems; this can be facilitated via voice and speech communications. Voice-based controls enable the user to access autonomous systems hands-free/eyes-free, allowing the user to better focus on critical tasks. The goal of this project was to explore the knowledge and technology needed to successfully design effective voice interfaces for autonomous systems. The main objective was to understand how a crew member, through voice interaction, could most efficiently and intuitively communicate with a notional autonomous vehicle system manager. This project leveraged prior research conducted by the University of Michigan’s Bioastronautics and Life Support System (BLiSS) team as part of a NASA Moon to Mars eXploration Systems and Habitation (M2M X-Hab) 2020 Academic Innovation Challenge. The X-Hab 2020 work from the BliSS Team resulted in an intuitive graphical user interface/user experience that was built on an Internet of Things (IOT) platform. The Voice User Interface (VUI) design for the M2M X-Hab 2021 project leveraged this technology and incorporated a voice-based assistant and NASA’s Platform for Autonomous Systems (NPAS) software. This required technologies to convert voice to text, conduct semantic interpretations, and convert responses from the autonomous system to text and to speech; additionally, the background noise environment of spacecraft was assessed, and a relatable personality for the autonomous system to facilitate human-like conversations was created. This work’s success was largely due to the diverse team that included expertise in Space Systems Engineering, Human Computer Interaction, Aerospace Engineering, Computer Science, Biomedical Engineering, and Applied Physics. The differing perspectives fostered elaborate discussions, resulting in the conception of three main interactions: (1) User-System, (2) NPAS-System, and (3) Environment-System. The system developed, i.e. the VUI, had to be unique, efficient, and intuitive; thus, the team crafted a personality for the system to enable human-like conversation. User surveys sent to students and young professionals were used to help determine these personality traits by capturing perspectives and expectations of the “Artemis Generation Astronauts”. To further simulate human-like conversations, the system had to be able to quickly interpret user speech and be able to integrate with NASA’s NPAS system for quick and reliable information transfer. Results of this research include (1) a working prototype user interface, that is compatible with NASA’s NPAS system; (2) software that demonstrates the ability to interpret user requests and respond appropriately; (3) the capability to implement fully expanded conversations between user and system using intuitive communication in four request categories; and (4) software and hardware recommendations that optimize the system’s ability to operate, i.e. be heard, in a noisy environment. The technologies chosen for this project’s demonstrations included the following: Raspberry Pi, RASA, Mozilla Deep Speech, Coqui, RTX Voice and Adobe XD. This work has laid the foundation for the development of VUI’s used for autonomy, and is intended to provide guidance for future VUI development.
Tara Vega↗
Integration of the Remote Agent for the NASA Deep Space One Autonomy Experiment
This paper describes the integration of the Remote Agent (RA), a spacecraft autonomy system which is scheduled to control the Deep Space 1 spacecraft during a flight experiment in 1999. The RA is a reusable, model-based autonomy system that is quite different from software typically used to control an aerospace system. We describe the integration challenges we faced, how we addressed them, and the lessons learned. We focus on those aspects of integrating the RA that were either easier or more difficult than integrating a more traditional large software application because the RA is a model-based autonomous system. A number of characteristics of the RA made integration process easier. One example is the model-based nature of RA. Since the RA is model-based, most of its behavior is not hard coded into procedural program code. Instead, engineers specify high level models of the spacecraft's components from which the Remote Agent automatically derives correct system-wide behavior on the fly. This high level, modular, and declarative software description allowed some interfaces between RA components and between RA and the flight software to be automatically generated and tested for completeness against the Remote Agent's models. In addition, the Remote Agent's model-based diagnosis system automatically diagnoses when the RA models are not consistent with the behavior of the spacecraft. In flight, this feature is used to diagnose failures in the spacecraft hardware. During integration, it proved valuable in finding problems in the spacecraft simulator or flight software. In addition, when modifications are made to the spacecraft hardware or flight software, the RA models are easily changed because they only capture a description of the spacecraft. one does not have to maintain procedural code that implements the correct behavior for every expected situation. On the other hand, several features of the RA made it more difficult to integrate than typical flight software. For example, the definition of correct behavior is more difficult to specify for a system that is expected to reason about and flexibly react to its environment than for a traditional flight software system. Consequently, whenever a change is made to the RA it is more time consuming to determine if the resulting behavior is correct. We conclude the paper with a discussion of future work on the Remote Agent as well as recommendations to ease integration of similar autonomy projects.
Dorais, Gregory A.↗
Orbital Express Advanced Video Guidance Sensor
In May 2007 the first US fully autonomous rendezvous and capture was successfully performed by DARPA's Orbital Express (OE) mission. Since then, the Boeing ASTRO spacecraft and the Ball Aerospace NEXTSat have performed multiple rendezvous and docking maneuvers to demonstrate the technologies needed for satellite servicing. MSFC's Advanced Video Guidance Sensor (AVGS) is a primary near-field proximity operations sensor integrated into ASTRO's Autonomous Rendezvous and Capture Sensor System (ARCSS), which provides relative state knowledge to the ASTRO GN&C system. This paper provides an overview of the AVGS sensor flying on Orbital Express, and a summary of the ground testing and on-orbit performance of the AVGS for OE. The AVGS is a laser-based system that is capable of providing range and bearing at midrange distances and full six degree-of-freedom (6DOF) knowledge at near fields. The sensor fires lasers at two different frequencies to illuminate the Long Range Targets (LRTs) and the Short Range Targets (SRTs) on NEXTSat. Subtraction of one image from the other image removes extraneous light sources and reflections from anything other than the corner cubes on the LRTs and SRTs. This feature has played a significant role for Orbital Express in poor lighting conditions. The very bright spots that remain in the subtracted image are processed by the target recognition algorithms and the inverse-perspective algorithms, to provide 3DOF or 6DOF relative state information. Although Orbital Express has configured the ASTRO ARCSS system to only use AVGS at ranges of 120 m or less, some OE scenarios have provided opportunities for AVGS to acquire and track NEXTSat at greater distances. Orbital Express scenarios to date that have utilized AVGS include a berthing operation performed by the ASTRO robotic arm, sensor checkout maneuvers performed by the ASTRO robotic arm, 10-m unmated operations, 30-m unmated operations, and Scenario 3-1 anomaly recovery. The AVGS performed very well during the pre-unmated operations, effectively tracking beyond its 10-degree Pitch and Yaw limit-specifications, and did not require I-LOAD adjustments before unmated operations. AVGS provided excellent performance in the 10-m unmated operations, effectively tracking and maintaining lock for the duration of this scenario, and showing good agreement between the short and long range targets. During the 30-m unmated operations, the AVGS continuously tracked the SRT to 31.6 m, exceeding expectations, and continuously tracked the LRT from 8.8 m out to 31.6 m, with good agreement between these two target solutions. After this scenario was aborted at a 10-m separation during remate operations, the AVGS tracked the LRT out 54.3 m, until the relative attitude between the vehicles was too large. The vehicles remained apart for eight days, at ranges from 1 km to 6 km. During the approach to remate in this recovery operation, the AVGS began tracking the LRT at 150 m, well beyond the OE planned limits for AVGS ranges, and functioned as the primary sensor for the autonomous rendezvous and docking.
Howard, Ricky↗
Application of Sparse Identification of Nonlinear Dynamics for Physics-Informed Learning
Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions.
Corbetta, Matteo↗
Enabling a Voice Management System for Space Applications
The sustainable missions beyond Low Earth Orbit (LEO) envisioned for NASA’s Artemis program will require autonomous capabilities. Moreover, Artemis mission crews will need a means to efficiently interact with a spacecraft’s autonomous systems. This interaction can be facilitated by voice and speech communications because voice-based controls enable users to interact hands- and eyes-free, allowing the user to better focus on critical tasks. The goal of our project was to explore the knowledge and technology needed to successfully design effective Voice User Interfaces (VUIs) for autonomous systems utilizing Human Centered Design (HCD) principles. The focus of the human factors’ aspect of engineering, pays close attention to psychological and physiological principles in the development of autonomous crew operation systems. A main objective was to understand how a crew member, through voice interaction, could efficiently and intuitively communicate with a notional autonomous vehicle system manager. This project was a part of the NASA Moon to Mars eXploration Systems and Habitation (M2M X-Hab) 2020 Academic Innovation Challenge. The work from the BLiSS Team, at the University of Michigan, resulted in the design of a system persona, Diego, to which an astronaut may quickly build trust with autonomous systems, to alleviate known stressors on mental health expected during long duration space missions. Optimal software to facilitate integration of the system persona into a reference Lunar orbiting Gateway station was defined. Additionally, a Speech to Text (STT) system and a Graphical User Interface (GUI) that could be implemented in future missions was developed on an Internet of Things (IOT) platform. The Voice User Interface (VUI) design for the M2M X-Hab 2020 project leveraged previous technology developed by the BLiSS team to incorporate a voice-based interface into NASA’s Platform for Autonomous Systems (NPAS) software. This required technologies to convert voice to text, conduct semantic interpretations, and convert responses from the autonomous system to text and to speech; additionally, the spacecraft background noise environment was assessed, a noise mitigation technique was developed, and a relatable personality for the autonomous system was developed in order to facilitate human-like conversations. The success of our effort was largely due to the diversity of the team that included expertise in Space Systems Engineering, Human Computer Interaction, Aerospace Engineering, Computer Science, Biomedical Engineering, and Applied Physics. The diverse perspectives fostered elaborate discussions, resulting in the conception of three main subsystems: (1) User-System, (2) NPAS-System, and (3) Environment-System. The VUI was unique and had to be efficient and intuitive. For this project, 5 subteams were formed, each with a separate objective, Voice Design team, Background Noise Mitigation team, Software Integration team and Graphical User Interface team. The BLiSS team crafted a personality for the VUI to enable human-like conversation and drive user adoption and trust. User surveys were completed and used to help determine the required VUI system personality traits by capturing perspectives and expectations of prospective “Artemis Generation Astronauts”. To further simulate human-like conversations, the system had to be able to quickly interpret user speech and be able to integrate with NASA’s NPAS platform for quick and reliable information transfer. The outcomes of our research were: (1) a working prototype user interface, that is compatible with NASA’s NPAS platform; (2) software that demonstrates the ability of the VUI system to interpret user requests and respond appropriately; (3) the capability to implement fully expanded conversations between user and system using intuitive communication in four request categories; and (4) software and hardware recommendations that optimize the system’s ability to operate in a noisy environment. Our research has laid the foundation for the development of VUI’s for autonomy, and provides a baseline for future VUI developments.
Voice user interface↗
Performance Monitoring and Assessment of Neuro-Adaptive Controllers for Aerospace Applications Using a Bayesian Approach
Modem aircraft, UAVs, and robotic spacecraft pose substantial requirements on controllers in the light of ever increasing demands for reusability, affordability, and reliability. The individual systems (which are often nonlinear) must be controlled safely and reliably in environments where it is virtually impossible to analyze-ahead of time- all the important and possible scenarios and environmental factors. For example, system components (e.g., gyros, bearings of reaction wheels, valves) may deteriorate or break during autonomous UAV operation or long-lasting space missions, leading to a sudden, drastic change in vehicle performance. Manual repair or replacement is not an option in such cases. Instead, the system must be able to cope with equipment failure and deterioration. Controllability of the system must be retained as good as possible or re-established as fast as possible with a minimum of deactivation or shutdown of the system being controlled. In such situations the control engineer has to employ adaptive control systems that automatically sense and correct themselves whenever drastic disturbances and/or severe changes in the plant or environment occur.
Gupta, Pramod↗
Applications of artificial intelligence 1993: Knowledge-based systems in aerospace and industry; Proceedings of the Meeting, Orlando, FL, Apr. 13-15, 1993
The present volume on applications of artificial intelligence with regard to knowledge-based systems in aerospace and industry discusses machine learning and clustering, expert systems and optimization techniques, monitoring and diagnosis, and automated design and expert systems. Attention is given to the integration of AI reasoning systems and hardware description languages, care-based reasoning, knowledge, retrieval, and training systems, and scheduling and planning. Topics addressed include the preprocessing of remotely sensed data for efficient analysis and classification, autonomous agents as air combat simulation adversaries, intelligent data presentation for real-time spacecraft monitoring, and an integrated reasoner for diagnosis in satellite control. Also discussed are a knowledge-based system for the design of heat exchangers, reuse of design information for model-based diagnosis, automatic compilation of expert systems, and a case-based approach to handling aircraft malfunctions.
Fayyad, Usama M.↗
Information Systems for NASA's Aeronautics and Space Enterprises
The aerospace industry is being challenged to reduce costs and development time as well as utilize new technologies to improve product performance. Information technology (IT) is the key to providing revolutionary solutions to the challenges posed by the increasing complexity of NASA's aeronautics and space missions and the sophisticated nature of the systems that enable them. The NASA Ames vision is to develop technologies enabling the information age, expanding the frontiers of knowledge for aeronautics and space, improving America's competitive position, and inspiring future generations. Ames' missions to accomplish that vision include: 1) performing research to support the American aviation community through the unique integration of computation, experimentation, simulation and flight testing, 2) studying the health of our planet, understanding living systems in space and the origins of the universe, developing technologies for space flight, and 3) to research, develop and deliver information technologies and applications. Information technology may be defined as the use of advance computing systems to generate data, analyze data, transform data into knowledge and to use as an aid in the decision-making process. The knowledge from transformed data can be displayed in visual, virtual and multimedia environments. The decision-making process can be fully autonomous or aided by a cognitive processes, i.e., computational aids designed to leverage human capacities. IT Systems can learn as they go, developing the capability to make decisions or aid the decision making process on the basis of experiences gained using limited data inputs. In the future, information systems will be used to aid space mission synthesis, virtual aerospace system design, aid damaged aircraft during landing, perform robotic surgery, and monitor the health and status of spacecraft and planetary probes. NASA Ames through the Center of Excellence for Information Technology Office is leading the effort in pursuit of revolutionary, IT-based approaches to satisfying NASA's aeronautics and space requirements. The objective of the effort is to incorporate information technologies within each of the Agency's four Enterprises, i.e., Aeronautics and Space Transportation Technology, Earth, Science, Human Exploration and Development of Space and Space Sciences. The end results of these efforts for Enterprise programs and projects should be reduced cost, enhanced mission capability and expedited mission completion.
Kutler, Paul↗
Advancing Sensor Technology for Aerospace Propulsion
NASA's Stennis Space Center (SSC) and Glenn Research Center (GRC) participate in the development of technologies for propulsion testing and propulsion applications in air and space transportation. Future transportation systems and the test facilities needed to develop and sustain them are becoming increasingly complex. Sensor technology is a fundamental pillar that makes possible development of complex systems that must operate in automatic mode (closed loop systems), or even in assisted-autonomous mode (highly self-sufficient systems such as planetary exploration spacecraft). Hence, a great deal of effort is dedicated to develop new sensors and related technologies to be used in research facilities, test facilities, and in vehicles and equipment. This paper describes sensor technologies being developed and in use at SSC and GRC, including new technologies in integrated health management involving sensors, components, processes, and vehicles.
Figueroa, Fernando↗
Using a Multicore Processor for Rover Autonomous Science
Multicore processing promises to be a critical component of future spacecraft. It provides immense increases in onboard processing power and provides an environment for directly supporting fault-tolerant computing. This paper discusses using a state-of-the-art multicore processor to efficiently perform image analysis onboard a Mars rover in support of autonomous science activities.
Mars↗
Development and Capabilities of Unique Structural Seal Test Rigs
High temperature structural seals are necessary in many aerospace and aeronautical applications to minimize any detrimental effects originating from undesired leakage. The NASA Glenn Research Center has been and continues to be a pioneer in the development and evaluation of these types of seals. The current focus for the development of structural seals is for the 3rd Generation Reusable Launch Vehicle (RLV), which is scheduled to replace the current space shuttle system around 2025. Specific areas of development under this program include seals for propulsion systems (such as the hypersonic air-breathing ISTAR engine concept based upon Rocket Based Combined Cycle technology) and control surface seals for spacecraft including the autonomous rescue X-38 Crew Return Vehicle and the X-37 Space Maneuver Vehicle.
DeMange, Jeffrey J.↗
Update on the Development and Capabilities of Unique Structural Seal Test Rigs
High temperature structural seals are necessary in many aerospace and aeronautical applications to minimize any detrimental effects originating from undesired leakage. The NASA Glenn Research Center has been and continues to be a pioneer in the development and evaluation of these types of seals. The current focus for the development of structural seals is for the 3rd Generation Reusable Launch Vehicle (RLV), which is scheduled to replace the current space shuttle system by 2025. Specific areas of development under this program include seals for propulsion systems (such as the hypersonic air-breathing ISTAR engine concept based upon Rocket Based Combined Cycle technology) and control surface seals for spacecraft including the autonomous rescue X-38 Crew Return Vehicle and the X-37 Space Maneuver Vehicle.
DeMange, Jeffrey J.↗