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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1989

Testbed for an autonomous system

In previous works we have defined a general architectural model for autonomous systems, which can easily be mapped to describe the functions of any automated system (SDAG-86-01), and we illustrated that model by applying it to the thermal management system of a space station (SDAG-87-01). In this note, we will further develop that application and design the detail of the implementation of such a model. First we present the environment of our application by describing the thermal management problem and an abstraction, which was called TESTBED, that includes a specific function for each module in the architecture, and the nature of the interfaces between each pair of blocks.

Dikshit, Piyush↗

NASA NTRS2005

Autonomous Systems, Robotics, and Computing Systems Capability Roadmap: NRC Dialogue

Contents include the following: Introduction. Process, Mission Drivers, Deliverables, and Interfaces. Autonomy. Crew-Centered and Remote Operations. Integrated Systems Health Management. Autonomous Vehicle Control. Autonomous Process Control. Robotics. Robotics for Solar System Exploration. Robotics for Lunar and Planetary Habitation. Robotics for In-Space Operations. Computing Systems. Conclusion.

Zornetzer, Steve↗

NASA NTRS1988

Knowledge acquisition for autonomous systems

Knowledge-based capabilities for autonomous aerospace systems, such as the NASA Space Station, must encompass conflict-resolution functions comparable to those of human operators, with all elements of the system working toward system goals in a concurrent, asynchronous-but-coordinated fashion. Knowledge extracted from a design database will support robotic systems by furnishing geometric, structural, and causal descriptions required for repair, disassembly, and assembly. The factual knowledge for these databases will be obtained from a master database through a technical management information system, and it will in many cases have to be augmented by domain-specific heuristic knowledge acquired from domain experts.

Lum, Henry↗

NASA NTRS2006

Verification of Autonomous Systems for Space Applications

Autonomous software, especially if it is based on model, can play an important role in future space applications. For example, it can help streamline ground operations, or, assist in autonomous rendezvous and docking operations, or even, help recover from problems (e.g., planners can be used to explore the space of recovery actions for a power subsystem and implement a solution without (or with minimal) human intervention). In general, the exploration capabilities of model-based systems give them great flexibility. Unfortunately, it also makes them unpredictable to our human eyes, both in terms of their execution and their verification. The traditional verification techniques are inadequate for these systems since they are mostly based on testing, which implies a very limited exploration of their behavioral space. In our work, we explore how advanced V&V techniques, such as static analysis, model checking, and compositional verification, can be used to gain trust in model-based systems. We also describe how synthesis can be used in the context of system reconfiguration and in the context of verification.

Brat, G.↗

NASA NTRS1988

Intelligent, autonomous systems in space

The Space Station is expected to be equipped with intelligent, autonomous capabilities; to achieve and incorporate these capabilities, the required technologies need to be identitifed, developed and validated within realistic application scenarios. The critical technologies for the development of intelligent, autonomous systems are discussed in the context of a generalized functional architecture. The present state of this technology implies that it be introduced and applied in an evolutionary process which must start during the Space Station design phase. An approach is proposed to accomplish design information acquisition and management for knowledge-base development.

Lum, H.↗

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 NTRS2006

Contingency Software in Autonomous Systems: Technical Level Briefing

Contingency management is essential to the robust operation of complex systems such as spacecraft and Unpiloted Aerial Vehicles (UAVs). Automatic contingency handling allows a faster response to unsafe scenarios with reduced human intervention on low-cost and extended missions. Results, applied to the Autonomous Rotorcraft Project and Mars Science Lab, pave the way to more resilient autonomous systems.

autonomous systems↗

NASA NTRS2009

Evolutionary Computation for the Identification of Emergent Behavior in Autonomous Systems

Over the past several years the Center for Evolutionary Computation and Automated Design at the Jet Propulsion Laboratory has developed a technique based on Evolutionary Computational Methods (ECM) that allows for the automated optimization of complex computationally modeled systems. An important application of this technique is for the identification of emergent behaviors in autonomous systems. Mobility platforms such as rovers or airborne vehicles are now being designed with autonomous mission controllers that can find trajectories over a solution space that is larger than can reasonably be tested. It is critical to identify control behaviors that are not predicted and can have surprising results (both good and bad). These emergent behaviors need to be identified, characterized and either incorporated into or isolated from the acceptable range of control characteristics. We use cluster analysis of automatically retrieved solutions to identify isolated populations of solutions with divergent behaviors.

Terrile, Richard J.↗

NASA NTRS2012