Linking Mobile Robot Performances With the Environment Using System Maps

Determining the performance of a robot is a challenge because success or failure depends not only on the capabilities of the robot but on the difficulties of the environment as well. This paper presents a method of understanding the performance of a robot with respect to its environment through the interaction of a set of metrics. Metric interactions are learned in a dynamic Bayesian network and placed in a probabilistic systems model called a system map, which is used to understand how the metrics relate both to each other and to a partially known environment. Initial results presented here demonstrate how this model identifies environmental dependencies and how performance can be predicted even in an uncertain environment