Abstract

This paper presents an exploration method that allows
mobile robots to build and maintain spatio-temporal models
of changing environments. The assumption of a perpetuallychanging
world adds a temporal dimension to the exploration
problem, making spatio-temporal exploration a never-ending,
life-long learning process. We address the problem by application
of information-theoretic exploration methods to spatio-temporal
models that represent the uncertainty of environment states as
probabilistic functions of time. This allows to predict the potential
information gain to be obtained by observing a particular area
at a given time, and consequently, to decide which locations to
visit and the best times to go there.
To validate the approach, a mobile robot was deployed
continuously over 5 consecutive business days in a busy office
environment. The results indicate that the robot’s ability to spot
environmental changes im