Sensor Validation Using Dynamic Belief Networks
This addresses sensor validation for robots in restricted dynamic environments, but it is incremental as it builds on existing DBN methods.
The paper tackles the problem of sensor data being partially or totally incorrect in robot trajectory monitoring using Dynamic Belief Networks (DBNs), by modifying the DBN to handle specific types of incorrect data and adding an invalidating node to model sensor status, enabling handling of persistent and intermittent faults.
The trajectory of a robot is monitored in a restricted dynamic environment using light beam sensor data. We have a Dynamic Belief Network (DBN), based on a discrete model of the domain, which provides discrete monitoring analogous to conventional quantitative filter techniques. Sensor observations are added to the basic DBN in the form of specific evidence. However, sensor data is often partially or totally incorrect. We show how the basic DBN, which infers only an impossible combination of evidence, may be modified to handle specific types of incorrect data which may occur in the domain. We then present an extension to the DBN, the addition of an invalidating node, which models the status of the sensor as working or defective. This node provides a qualitative explanation of inconsistent data: it is caused by a defective sensor. The connection of successive instances of the invalidating node models the status of a sensor over time, allowing the DBN to handle both persistent and intermittent faults.