Recent improvements in the ability of mobile robots to operate safely in human populated
environments have allowed their deployment in households, offices and public buildings,
such as museums and hospitals. However, the structure of these environments is typically
not known a priori, which requires the robots to build their own models of their operational
environments. This process is commonly known as "exploration" in mobile robotics.
Moreover, real-world environments tend to change over time, which means that to achieve
long-term autonomous operation, robots must also update their environment models as a
part of their daily routine. The assumption of a perpetually-changing world adds a temporal
dimension to the exploration problem, making exploration a never-ending lifelong
learning process. To the best of our knowledge, this process termed "lifelong exploration"
has never been studied in detail before and forms the main topic of the work presented in
this thesis. Effcient lifelong exploration requires a robot to choose the right locations and
times at which to collect observations in order to improve its environment model.
To evaluate the ability of a robot to build and maintain its environment models, we
need to be able to compare lifelong exploration strategies under repeatable experimental
conditions. An evaluation methodology based on pre-recorded sensory datasets would not
be suitable for this purpose, as this would not allow the robot to choose the location or time
of its observations. Evaluating lifelong exploration requires the deterministic reproduction
of environment changes, while preserving the robots ability to decide upon its own actions
during the experiment. This thesis therefore contributes a new benchmarking methodology
for lifelong exploration, which replicates the events occurring in real environments through
physical simulations that reflect the environment changes gathered by ambient sensors over
long periods of time. The established experimental benchmarks are based on long-term
sensory datasets recorded in a smart home, with dynamics produced by a single person
over a period of one year, and an office environment, with dynamics produced by a team
of workers.
Building upon the aforementioned benchmarking methodology, the thesis investigates
possible strategies for lifelong exploration. An experimental comparison of lifelong exploration
strategies that combine various decision-making paradigms and spatio-temporal
representations is presented. Moreover, a new approach to lifelong explorations is proposed
that applies information-theoretic exploration techniques to environment representations
that model the uncertainty of world states as probabilistic functions of time. The proposed
method explicitly models the world dynamics and can predict the environment changes.
The predictive ability is used to reason about the most informative locations to explore
for a given time. A 16 week long experiment shows that the combination of dynamic
environment representations with information-gain exploration principles allows to create
and maintain up-to-date models of continuously changing environments, enabling efficient
and self-improving long-term operation of mobile service robots.
The final part of the thesis considers the problem of acquiring and maintaining dense
3D models of dynamic environments during long-term operation, building on the work
presented in the earlier chapters. The term "4D mapping" is used to indicate 3D mapping
by mobile robots over extended periods of time. A new approach to lifelong 4D mapping
and exploration is presented, which was deployed on a real robotic platform during long term
operation in real-world human-populated environments. The approach integrates
sensory data captured by the robot at different times and locations into a global, metric
I 4D spatio-temporal model and then uses the model to decide where and when to perform
the next round of observations. Finally, the deployment of the 4D exploration method in a
real-world office scenario is described and evaluated. The one week long experiments show
that the method enables reliable 4D mapping and persistent self-localisation of autonomous
mobile robots, continually improving the robots maps to reflect the ever-changing world
