Trusted Autonomy: Autonomy and Autonomous Systems at NASA LaRC's Autonomy Incubator
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Allen, B. Danette↗
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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.↗
Deep Space Control Challenges of the New Millennium
The exploration of deep space presents a variety of significant control challenges. Long communication delays coupled with challenging new science objectives require high levels of system autonomy and increasingly demanding pointing and control capabilities. Historically, missions based on the use of a large single spacecraft have been successful and popular since the early days of NASA. However, these large spacecraft missions are currently being displaced by more frequent and more focused missions based on the use of smaller and less expensive spacecraft designs. This trend drives the need to design smart software and good algorithms which together with the miniaturization of control components will improve performance while replacing the heavier and more expensive hardware used in the past. NASA's future space exploration will also include mission types that have never been attempted before, posing significant challenges to the underlying control system. This includes controlled landing on small bodies (e.g., asteroids and comets), sample return missions (where samples are brought back from other planets), robotic exploration of planetary surfaces (e.g., intelligent rovers), high precision formation flying, and deep space optical interferometry, While the control of planetary spacecraft for traditional flyby and orbiter missions are based on well-understood methodologies, control approaches for many future missions will be fundamentally different. This paradigm shift will require completely new control system development approaches, system architectures, and much greater levels of system autonomy to meet expected performance in the presence of significant environmental disturbances, and plant uncertainties. This paper will trace the motivation for these changes and will layout the approach taken to meet the new challenges. Emerging missions will be used to explain and illustrate the need for these changes.
Bayard, David S.↗
Automated Subsystem Control for Life Support System (ASCLSS)
The Automated Subsystem Control for Life Support Systems (ASCLSS) program has successfully developed and demonstrated a generic approach to the automation and control of space station subsystems. The automation system features a hierarchical and distributed real-time control architecture which places maximum controls authority at the lowest or process control level which enhances system autonomy. The ASCLSS demonstration system pioneered many automation and control concepts currently being considered in the space station data management system (DMS). Heavy emphasis is placed on controls hardware and software commonality implemented in accepted standards. The approach demonstrates successfully the application of real-time process and accountability with the subsystem or process developer. The ASCLSS system completely automates a space station subsystem (air revitalization group of the ASCLSS) which moves the crew/operator into a role of supervisory control authority. The ASCLSS program developed over 50 lessons learned which will aide future space station developers in the area of automation and controls..
Block, Roger F.↗
Piloted Evaluation of the H-Mode, a Variable Autonomy Control System, in Motion-Based Simulation
As aircraft become able to autonomously respond to a range of situations with performance surpassing human operators, we are compelled to look for new methods that help understand their use and guide the design of new, more effective forms of automation and interaction. The "H-mode" is one such method and is based on the metaphor of a well-trained horse. The concept allows the pilot to manage a broad range of control automation functionality, from augmented manual control to FMS-like coupling and automation initiated actions, using a common interface system and easily learned set of interaction skills. The interface leverages familiar manual control interfaces (e.g., the control stick) and flight displays through the addition of contextually dependent haptic-multimodal elements. The concept is relevant to manned and remotely piloted vehicles. This paper provides an overview of the H-mode concept followed by a presentation of the results from a recent evaluation conducted in a motion-based simulator. The evaluation focused on assessing the overall usability and flying qualities of the concept with an emphasis on the effects of turbulence and cockpit motion. Because the H-mode results in interactions between traditional flying qualities and management of higher-level flight path automation, these effects are of particular interest. The results indicate that the concept may provide a useful complement or replacement to conventional interfaces, and retains the usefulness in the presence of turbulence and motion.
Goodrich, Kenneth H.↗
Enabling Smarter Systems - Advanced Autonomy Research at NASA Ames Research Center
This presentation discusses high-level capabilities and interests in autonomy at NASA Ames, and describes some methods for collaboration.
Ippolito, Corey A.↗
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.↗
Autonomy Technologies for Systems of a Moon Base
The aim of the workshop is to “explore emerging autonomy technologies that could enable or enhance mission capabilities, reduce mission risk, and reduce mission cost.” Enhancing mission capabilities will depend on how capable and trustworthy the autonomy implemented is. As systems increase in complexity, and also with multiple interdependent/interacting systems, current autonomy capability and trustworthiness is very low. A paradigm and technology from NASA that addresses this shortcoming will be discussed, the NASA Platform for Autonomous Systems (NPAS). NPAS also happens to address reduction of mission risk and cost. NPAS will be discussed as a capability that is reaching readiness for space use, but also serves as a reference to develop technologies suitable for integrated autonomous operations of lunar systems; encompassing autonomous systems, situational awareness, and reasoning and acting.
Autonomous systems↗
Autonomy, automation, and systems
Automation technologies which support autonomy in artificial intelligence systems are discussed. The motivation for system control architecture is reviewed, and the concept of an operating system for autonomous control is examined. Design requirements for complex automated systems are summarized, and system engineering tool applications in design support are discussed.
Turner, Philip R.↗
TDAS: The Thermal Expert System (TEXSYS) data acquisition system
As part of the NASA Systems Autonomy Demonstration Project, a thermal expert system (TEXSYS) is being developed. TEXSYS combines a fast real time control system, a sophisticated human interface for the user and several distinct artificial intelligence techniques in one system. TEXSYS is to provide real time control, operations advice and fault detection, isolation and recovery capabilities for the space station Thermal Test Bed (TTB). TEXSYS will be integrated with the TTB and act as an intelligent assistant to thermal engineers conducting TTB tests and experiments. The results are presented from connecting the real time controller to the knowledge based system thereby creating an integrated system. Special attention will be paid to the problem of filtering and interpreting the raw, real time data and placing the important values into the knowledge base of the expert system.
Hack, Edmund C.↗
Standardizing Microprocessor and GPU Radiation Test Approaches
Microprocessor, Graphics Processing Units (GPUs) and DDRx memory devices have emerged as promising next-generation technologies that enables both high performance processing and acceleration of complex algorithms for the latest challenges in human spaceflight, autonomous vehicles and artificial intelligence (AI). The feature sets of these devices offer exponential increases to throughput, calculation capability and system autonomy when compared to legacy flight systems. NASA's Electronic Part and Packaging (NEPP) Program has conducted an investigation into the radiation susceptibility of leading edge devices and process technologies by establishing standardized test approaches. Unlike most discrete devices, these require state of the art test systems to induce specific hardware activity similar to application software, thus allowing the characterization of failure modes within the system. To best characterize the tested part, NEPP eliminates variables that may impact device performance under radiation. Simplification of remaining system-level variables leads to an improved understanding of complex computational devices and their intended applications. The failure modes and error signatures that are recorded during testing are used to determine radiation sensitivity of the semiconductor process and the microcode architecture of the design. This presentation will discuss the test methodology that NASA Electronic Parts and Packaging (NEPP) is working to establish for its microprocessor, GPU and DDRx memory test programs to provide guidance on these devices and their underlying technology, in regards to their potential usage in future space flight systems.
GPU↗
A Thinking System and Thinking Autonomy
There are many definitions of what a “Thinking System” (TS) is. The literature primarily addresses what is called “Systems Thinking.” Dr. Marie Morganelli from Southern New Hampshire University states that “Systems thinking is a holistic way to investigate factors and interactions that could contribute to a possible outcome. A mindset more than a prescribed practice, systems thinking provides an understanding of how individuals can work together in different types of teams and through that understanding, create the best possible processes to accomplish just about anything.” So, a TS is a system that is capable of “systems thinking,” as it should be able to “ … create [utilize] the best possible processes [and intelligence] to accomplish just about anything.” To achieve this capability, human-like thinking is required. A truly autonomous system must be one that is capable of human-like thinking. This paper will address “Thinking Autonomy” (TA) enabled by a “Thinking System.” It will describe an architecture with the elements required to achieve the “thinking” behavior: understanding, intellect, reason, decision, will. The paper will further describe the contents and functionality of these elements and how to implement them, including software capabilities needed. Finally, the paper will provide details of a software platform that enables TA, the NASA Platform for Autonomous Systems (NPAS), and describe implementations of thinking systems. Thinking systems will enable a fundamental change how AI and autonomy are implemented. It will change from a “brute force” approach that results in one-time implementations that are minimally intelligent or autonomous to a “thinking” approach where implementations evolve continuously and enable powerful intelligence and autonomy on systems of high complexity as well as on systems-of-systems.
Thinking systems↗
Automation of the space station core module power management and distribution system
Under the Advanced Development Program for Space Station, Marshall Space Flight Center has been developing advanced automation applications for the Power Management and Distribution (PMAD) system inside the Space Station modules for the past three years. The Space Station Module Power Management and Distribution System (SSM/PMAD) test bed features three artificial intelligence (AI) systems coupled with conventional automation software functioning in an autonomous or closed-loop fashion. The AI systems in the test bed include a baseline scheduler/dynamic rescheduler (LES), a load shedding management system (LPLMS), and a fault recovery and management expert system (FRAMES). This test bed will be part of the NASA Systems Autonomy Demonstration for 1990 featuring cooperating expert systems in various Space Station subsystem test beds. It is concluded that advanced automation technology involving AI approaches is sufficiently mature to begin applying the technology to current and planned spacecraft applications including the Space Station.
Weeks, David J.↗
The space station - An overview of the design process
The design factors being considered in the NASA space-station development program are summarized. The currently envisioned mission requirements are listed, and the system architecture is defined as a core station, mission-dedicated elements, and supporting equipment such as an orbit maneuvering vehicle. System design factors discussed include orbit selection, contamination control, autonomy, system safety, technology implementation, long life, reliability and maintainability, and cost; subsystem design factors include structural considerations, electrical power, environmental control and life support, data management, communications and tracking, onboard propulsion, habitability, and crew support. Configurational design is seen as driven by a number of factors, primarily the need to fit all components into the Shuttle payload bay for assembly in LEO by the Shuttle crew.
Covington, C.↗
Demonstrating artificial intelligence for space systems - Integration and project management issues
As part of its Systems Autonomy Demonstration Project (SADP), NASA has recently demonstrated the Thermal Expert System (TEXSYS). Advanced real-time expert system and human interface technology was successfully developed and integrated with conventional controllers of prototype space hardware to provide intelligent fault detection, isolation, and recovery capability. Many specialized skills were required, and responsibility for the various phases of the project therefore spanned multiple NASA centers, internal departments and contractor organizations. The test environment required communication among many types of hardware and software as well as between many people. The integration, testing, and configuration management tools and methodologies which were applied to the TEXSYS project to assure its safe and successful completion are detailed. The project demonstrated that artificial intelligence technology, including model-based reasoning, is capable of the monitoring and control of a large, complex system in real time.
Hack, Edmund C.↗