Identifying and localizing electrical components: a case study of adaptive goal-directed sensing

The ability to reconfigure sensors dynamically between data collection operations (often termed active sensing) enables planning of sensing strategies. Each sensory action will improve knowledge of the environment; hence, each sensory action can be chosen utilizing a larger knowledge base than was available for previous actions. Consequently, a strategy consisting of a sequence of sensory actions can be planned in an adaptive manner, with data obtained from each action influencing the selection of subsequent actions. A system for identifying and localizing electrical components is described which is both adaptive and goal-directed. The mathematical framework of Bayesian decision theory is applied to the problem of selecting appropriate sensor actions in the presence of uncertain knowledge about the environment. This enables a consistent Bayesian framework for reasoning with uncertainty for the associated tasks of world modeling, sensor modeling, data fusion, and the selection of sensory actions