A Bayesian approach to optimal sensor placement
By "intelligently" locating a sensor with respect to its environment it is possible to minimize the number of sensing operations required to perform many tasks. This is particularly important for sensing media which provide only "sparse" data, such as tactile sensors and sonar. In this thesis, a system is described which uses the principles of statistical decision theory to determine the optimal sensing locations to perform recognition and localization operations. The system uses a Bayesian approach to utilize any prior object information (including object models or previously-acquired sensory data) in choosing the sensing locations.