This book is concerned with planning and acting under uncertainty
in partially-observable continuous domains. It focusses on the
problem of mobile robot navigation given a known map. The dominant
paradigm for robot localisation is to use Bayesian estimation to
maintain a probability distribution over possible robot poses. In
contrast, control algorithms often base their decisions on the
assumption that the most likely state is correct, rather than
considering the entire distribution. This book formulates an
approach to planning in the space of continuous parameterised
approximations to probability distributions. Theoretical and
practical results are presented which show that, when compared with
similar methods from the literature, this approach is capable of
scaling to larger and more realistic problems. The algorithms have
been implemented and demonstrated during real-time control of a
mobile robot in a challenging navigation task. Results show that
this approach produces significantly more robust behaviour when
compared with heuristic planners which consider only the most
likely states and outcomes.
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