Presents an approach to the management of perception in a multi-sensor robotic system. The approach is based around an adaptive sensory perception controller which has two significant characteristics: (1) it performs real-time selection of process monitors based on the minimisation of the expected cost of perception with constraints on the uncertainty of perception, and (2) it performs online adaptation of the perception models on which the monitor selection is based. The approach is shown to be useful and effective in experiments involving a range of sensing modalities which may typically be encountered in robotic applications.

Models of perception are an important element in the control of sensory perception in autonomous systems. The performance of a perception controller will depend on how well the models reflect the time-varying performance characteristics of sensors and data processing algorithms. A novel approach to achieving high quality models through real-time adaptation is presented. Models reflecting observation uncertainty are adapted in accordance with online sensor performance using a radial basis function approach modified to allow real-time operation.

We discuss the relationship between a sensory perception controller and traditional data fusion techniques. The perception controller selects process monitors in real-time, based on the expected uncertainty and cost. We experimentally compare its operation to traditional data fusion methods and show how to combine the two for improved performance.

We present an approach to managing the quality and cost of perception in a multi-sensor robotic system. The approach involves prediction of monitoring errors of low-performance process monitors using a weighted least squares algorithm, and detection of instances when high-performance process monitoring is necessary. Two significant characteristics of the approach are (1) dynamic, real-time management of process monitors, and (2) the ability to deliver high quality information while keeping the cost of perception low.

We present an approach to the control of sensory perception. It involves real-time selection of process monitors based on the expected value of the monitoring operations. Appropriate definitions of the value of monitoring are a key element of the approach. The sensory perception control methodology is evaluated through simulation of an environment mapping task.