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Using adaptive goal-directed sensing to overcome sensor uncertainty

The purpose of sensing is to collect information about the environment, with which the system interacts, to assist in performing tasks. For the system to be efficient and reliable in accomplishing tasks, sensing should provide information which is key to the task accomplishment. The ability to automatically reconfigure sensors between operations to collect sensory data enables the planning of sensing strategies for achieving this goal. The sensory action of acquiring data, however, will improve knowledge about the environment; hence improved knowledge could be utilized in determining subsequent sensory actions suitable for the increasingly-understood environment. Thus, an adaptive sensing strategy is more desirable than a pre-determined plan. In this paper, we demonstrate how the techniques of Bayesian decision theory can be used to develop sensing strategies which are adaptive and goal-directed. We emphasize how to model undertainties of sensory outcomes to improve the robustness of task achievement. The methods were applied to the problem of identifying and localizing electrical components using a camera mounted on a robot arm. This implementation is described and the automatically-generated strategies are discussed.