Validation and Verification of the Remote Agent For Spacecraft Autonomy
The six-day Remote Agent Experiment (RAX) on the Deep Space 1 mission will be the first time that an artificially intelligent agent will control a NASA spacecraft.
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The six-day Remote Agent Experiment (RAX) on the Deep Space 1 mission will be the first time that an artificially intelligent agent will control a NASA spacecraft.
The new breed of autonomous goal-driven spacecraft contain much more onboard capability than their syquence-driven predecessors, demanding corresponding advances in software verification techniques. Although autonomous systems are deterministic, they are hightly sensitive to the environment, such that the response of a system in certain contexts must be explored in detail in order to prove confidence in both the design and implementaiton.
The Solar and Heliospheric Observatory (SOHO) project [1] is being carried out by the European Space Agency (ESA) and the US National Aeronautics and Space Administration (NASA) as a cooperative effort between the two agencies in the framework of the Solar Terrestrial Science Program (STSP) comprising SOHO and other missions. SOHO was launched on December 2, 1995.
The challenge of space flight in NASA's future is to enable smaller, more frequent and intensive space exploration at much lower total cost without substantially decreasing mission reliability, capability, or the scientific return on investment. The most effective way to achieve this goal is to build intelligent capabilities into the spacecraft themselves. Our technological vision for meeting the challenge of returning quality science through limited communication bandwidth will actually put scientists in a more direct link with the spacecraft than they have enjoyed to date. Ultimately, new classes of exploration missions will be enabled.
NASA's New Millenium Program (NMP) is designed to dramatically reduce mission costs and enable new and more frequent exploration missons.
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- Background - MPATH Ground Control Station (GCS) - Design Approach - Software Features - Initial Usability Results - Simulated Flight Activity - Real Flight Activity - Multi-Aircraft Control Flight Assessment - Design Recommendations
Emerging concepts for advanced urban air mobility envision responsive air transportation capabilities that will safely move people and cargo in locations presently underserved by aviation. Expanding aviation services to these locales, particularly for high-density autonomous flight operations over urban centers, will require advances beyond the state-of-the-art techniques for airborne sensing. The emerging field of distributed sensing and ‘smart spaces’ – where sensing, processing, communication, and actuation are embedded in the environment in which agents are acting – may provide attractive alternatives over traditional aviation solutions. This paper outlines the challenges and opportunities for distributed sensing and smart space concepts to meet the emerging needs of advanced urban operations in the national airspace. We present an overview of distributed sensing concepts and research currently being investigated under this endeavor.
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NASA’s future long duration exploration missions (LDEMs) will encounter increasing communication transmission delays as they move farther from Earth-based ground stations. Because crews can no longer rely on real-time support from ground planners, they will have to self-schedule their own operational timelines effectively and efficiently. To enable this, our team develops Playbook, a mission planning and scheduling tool. Our research focuses on quantifying scheduling performance using Playbook to inform the design and development of future features aimed at streamlining timeline creation. We also aim to propose standards and guidelines for autonomous crews in LDEMs. This year, we discuss preliminary results from HERA Campaign 6.
There is a desire to design autonomous systems in such a way that capabilities can be easily added or re- combined to produce new behaviors while preserving their safety properties. ICAROUS, a prototype software architecture for building safety-centric autonomous unmanned aircraft applications, is designed to support this type of extensibility and re-configurability. In ICAROUS, core capabilities are implemented as individual soft- ware services, so that enabling access to new capabilities simply requires adding new services. To make use of these capabilities, ICAROUS includes a specialized service that provides a general framework for config- uring the relative priorities, conditions, and rules that govern how different modules should be engaged and disengaged during flight. The inherent complexity of coordinating multiple modules under changing conditions makes it difficult to determine whether a particular configuration could have erroneous behaviors in certain circumstances. A robust set of integration tests can help discover errors, but testing can only realistically cover a relatively small proportion of total system behaviors. Developing good tests and interpreting the results to pinpoint the cause of errors when they arise can also be very time-consuming. To supplement testing, formal methods can be used to model and analyze complex systems, achieving better coverage and simplifying the process of finding, understanding, and fixing errors. To demonstrate these benefits, this paper explores the ap- plication of formal methods to ICAROUS. In particular, the Spin model checker is used to specify requirements for and model portions of the system, then verify whether the model satisfies the requirements and find and fix errors when it does not.