Sensor Validation Using Dynamic Belief Networks

TL;DR

This paper introduces an extension to Dynamic Belief Networks for robot sensor validation, enabling the detection and explanation of sensor faults over time in dynamic environments.

Contribution

It proposes adding an invalidating node to the DBN to model sensor faults, improving fault detection and explanation capabilities.

Findings

The extended DBN can identify faulty sensors effectively.

The model handles both persistent and intermittent sensor faults.

Qualitative explanations of sensor failures are provided.

Abstract

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.