We describe the use of Bayesian belief networks and decision theoretic principles for sensor management in multi-sensor systems. This framework provides a way of representing sensory data and choosing actions under uncertainty. The work considers how to distribute functionality between sensors and the controller. Use is made of logical sensors based on complementary physical sensors to provide information at the task level of abstraction represented within the network. We are applying these methods in the area of low level planning in mobile robotics. A key feature of the work is the development of quantified models to represent diverse sensors, in particular the sonar array and infra-red triangulation sensors we use on our AGV. We need to develop a model which can handle these very different sensors but provides a common interface to the sensor management process. We do this by quantifying the uncertainty through probabilistic models of the sensors, taking into account their physical characteristics and interaction with the expected environment. Modelling the sensor characteristics to an appropriate level of detail has the advantage of giving more accurate and robust mapping between the physical and logical sensor, as well as a better understanding of environmental dependency and its limitations. We describe a model of a sonar array, which explicitly takes into account features such as beam-width and ranging errors, and its integration into the sensor management process.