A general mathematical framework is described for applying Bayesian decision theory to selecting optimal sensing actions for achieving a given sensory goal. The information utilized in the selection process for achieving the goal includes all sensory data acquired prior to the currently considered action. This enables the selection of intelligent sensing strategies to be both adaptive and goal-directed. The authors first show how Bayesian decision theory can facilitate the selection of plans for collecting information relevant to a given task. The approach taken is quite general. It is directly applicable to multi-sensor systems. It could be used in selecting sensory actions to acquire multiple types of information at once. The use of the approach is demonstrated with an example from the domain of robot vision.>

By intelligently locating a tactile sensor with respect to a sensed object it is possible to minimize the number of sensed points required to identify or localize the object. The author applies principles of statistical decision theory to determine the optimal sensing location to constrain an object model, maximally based on any prior object information, including models or previously sensed points. He shows how information about an object's shape can be combined with sensory data to produce a probablistic membership function on the workspace. Utility functions on the workspace are derived which qualitatively describe the constraining value of obtaining sensory data at each location in the environment. Also described are techniques of sequential analysis which are used to process sensory information as it is acquired. An implementation of these principles is presented, namely, a two-dimensional sensing problem, using a camera with a restricted field of view to acquire sparse sensory data, which are used to discriminate the identity of a shape from among a given set.>

A tactile sensor has been developed which uses a layer of photoelastic material as its primary sensing element. This sensor has several desirable characteristics, including very high resolution, adaptable shape, and edge enhancement. Interestingly, the sensor has a similar sensing mechanism to human skin. A mathematical model has been developed for the sensor in an attempt to better understand its operation and to optimize its performance. The model draws heavily on the established fields of continuum mechanics and photoelastic stress analysis. The results of this analysis provide several important insights into tactile sensing and human touch, including an explanation of the importance of motion to touch, and the enhanced sensitivity of the sensor (and human skin) in detected edges.>