Modelling and simulation for high-autonomy systems.

Abstract

The basic objective of this research is to develop an architecture for systems capable of highly autonomous behavior by combining decision (intelligence), perception (sensory processing), and action (effector) components. The major challenge of this dissertation is the integration of high-level symbolic models with low-level dynamic (control-theoretic) models into a coherent model base. The systematic inclusion of dynamic and symbolic models each dedicated to support a single function such as planning, operations, diagnosis or perception allows us to extend existing multi-layered control and information architectures. A knowledge-based simulation environment is employed to simulate and verify the proposed integrated model-based architecture. The constructed working simulation version of an autonomous robot-managed laboratory demonstrates the use of multiple model families for experiment planning and execution. Tools to support the development and integration of such model families are also developed. The developed model-based architecture is elaborated by incorporating time-based simulation and causal propagation model families supporting diagnosis, repair, and replanning. This involves tools to automatically extract such models from more detailed dynamic models and structural knowledge. Systems with high levels of autonomy are critical for unmanned, and partially manned, space missions. The utility of the proposed high autonomy system will be demonstrated with models of a robot-managed fluid handling laboratory for International Space Station Freedom to be used for research in life sciences, microgravity sciences, and space medicine. NASA engineers will be able to base designs of intelligent controllers for such systems on the architecture developed in this dissertation. They will be able to employ our tools and simulation environment to verify such designs prior to their implementation.