Execution environment for intelligent real-time control systems

Modern telerobot control technology requires the integration of symbolic and non-symbolic programming techniques, different models of parallel computations, and various programming paradigms. The Multigraph Architecture, which has been developed for the implementation of intelligent real-time control systems is described. The layered architecture includes specific computational models, integrated execution environment and various high-level tools. A special feature of the architecture is the tight coupling between the symbolic and non-symbolic computations. It supports not only a data interface, but also the integration of the control structures in a parallel computing environment

Graph-based real-time fault diagnostics

A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components

Methodology for testing and validating knowledge bases

A test and validation toolset developed for artificial intelligence programs is described. The basic premises of this method are: (1) knowledge bases have a strongly declarative character and represent mostly structural information about different domains, (2) the conditions for integrity, consistency, and correctness can be transformed into structural properties of knowledge bases, and (3) structural information and structural properties can be uniformly represented by graphs and checked by graph algorithms. The interactive test and validation environment have been implemented on a SUN workstation

Robot graphic simulation testbed

The objective of this research was twofold. First, the basic capabilities of ROBOSIM (graphical simulation system) were improved and extended by taking advantage of advanced graphic workstation technology and artificial intelligence programming techniques. Second, the scope of the graphic simulation testbed was extended to include general problems of Space Station automation. Hardware support for 3-D graphics and high processing performance make high resolution solid modeling, collision detection, and simulation of structural dynamics computationally feasible. The Space Station is a complex system with many interacting subsystems. Design and testing of automation concepts demand modeling of the affected processes, their interactions, and that of the proposed control systems. The automation testbed was designed to facilitate studies in Space Station automation concepts

Graphic simualtion test bed for robotics applications in a workstation environment

Graphical simulation is a cost-effective solution for developing and testing robots and their control systems. The availability of various high-performance workstations makes these systems feasible. Simulation offers preliminary testing of systems before their actual realizations, and it provides a framework for developing new control and planning algorithms. On the other hand, these simulation systems have to have the capability of incorporating various knowledge-based system components, e.g., task planners, representation formalisms, etc. They also should have an appropriate user interface, which makes possible the creation and control of simulation models. ROBOSIM was developed jointly by MSFC and Vanderbilt University, first in a VAX environment. Recently, the system has been ported to an HP-9000 workstation equipped with an SRX graphics accelerator. The user interface of the system now contains a menu- and icon-based facility, as well as the original ROBOSIM language. The system is also coupled to a symbolic computing system based on Common Lisp, where knowledge-based functionalities are implemented. The knowledge-based layer uses various representation and reasoning facilities for programming and testing the control systems of robots

Multi-Domain Surety Modeling and Analysis for High Assurance Systems

Engineering systems are becoming increasingly complex as state of the art technologies am incorporated into designs. Surety modeling and analysis is an emerging science that permits an engineer to qualitatively and quantitatively predict and assess the completeness and predictability of a design. Surety is a term often used in the Department of Defense (DoD) and Department of Energy (DOE) communities, which refers to the integration of safety, security, reliability and performance aspects of design. Current risk assessment technologies for analyzing complex systems fail to adequately describe the problem, thus making assessment fragmented and non-integrated. To address this problem, we have developed a methodology and extensible software tool set to address model integration and complexity for high consequence systems. The MultiGraph Architecture (MGA) facilitates multi-domain, model-integrated modeling and analyses of complex, high-assurance systems. The MGA modeling environment allows the engineer to customize the modeling environment to match a design paradigm representative of the actual design. Previous modeling tools have a point-defined model space that forms the modeler to work in less than optimal environments. Current approaches for the problem to be bounded and constrained by requirements of the modeling tool and not the actual design problem. In some small cases, this is only maximally adequate MM facilitates the implementation of a surety methodology, which is used to represent high assurance systems with respect to safety and reliability. Formal mathematical models am used to correctly describe design safety and reliability functionality and behavioral fictional and behavioral representations of the design w then analyzed using commercial-off-the-shelf tools

A comparative, sociotechnical design perspective on responsible innovation: multidisciplinary research and education on digitized energy and automated vehicles

This study develops a comparative, sociotechnical design perspective for interdisciplinary teams of social scientists and computer scientists. Sociotechnical design refers to identifying both technical and governance challenges and to understanding the ways in which the two types of problems affect and define each other. Approaching design as an open-ended, iterative process, the study develops a triple comparative perspective to problem finding and solutions: across two types of technological systems (the smart grid and connected and automated vehicles), three areas of societal implication and values (safety, equity, and privacy), and two continents (North America and Europe with a focus on the U.S. and Germany). The study then describes the implementation in an international collaboration of research and teaching. The collaborative experience and comparative research provide insights into the salience of the values across technological systems, portability of solutions across technological systems, and potential for policy harmonization across countries.