@phdthesis {pub5154,
	title = {Multi-Robot Coordination Under Temporal Uncertainty},
	author = {Charlie Street},
	year = {2022},
	abstract = {Sources of temporal uncertainty affect the duration and start time of robot actions
during execution. For example, mobile robots may slip on uneven terrain, slowing
them down. The presence of multiple robots in the environment contributes towards
temporal uncertainty, as robot interactions such as congestion affect navigation
performance. Existing multi-robot coordination solutions often disregard temporal
uncertainty through simplifying assumptions such as fixed, identical action durations,
which simplifies the problem at the cost of inefficient execution-time behaviour. To
synthesise multi-robot behaviour that is robust to unexpected temporal disturbances,
we must explicitly capture temporal uncertainty during coordination.
In this thesis, we present techniques for effective multi-robot coordination under
temporal uncertainty. To represent temporal uncertainty, we construct probabilistic
models of action duration for each spatiotemporal situation an action may be
executed in. With this, we build formal multi-robot models which accurately capture
asynchronous robot execution in continuous-time. We then develop planning and
task allocation solutions which reason over the effects of temporal uncertainty to
synthesise efficient multi-robot behaviour. Further, we consider the problem of
policy evaluation, i.e. evaluating properties of synthesised multi-robot behaviour
prior to execution, and present two approaches which trade between the accuracy
of the model and the solution methods used for policy evaluation. Empirically, our
methods outperform solutions which ignore temporal uncertainty, use simplified
models, or constrain robot behaviour to reduce temporal uncertainty.},
	publisher = {University of Oxford},
	booktitle = {University of Oxford},
	institution = {University of Oxford}
}