Zeyn A. Saigol, Richard W. Dearden, Jeremy L. Wyatt and Bramley J. Murton.
Belief Change Maximisation for Hydrothermal Vent Hunting Using Occupancy Grids.
In Tony Belpaeme and Guido Bugmann and Chris Melhuish and Mark Witkowski (editors) Proceedings of the Eleventh Towards Autonomous Robotic Systems (TAROS-10), pages 247--254, University of Plymouth.
The problem of where a mobile robot should go
to efficiently build a map of its surroundings is frequently
addressed using entropy reduction techniques. However, in
exploration problems where the goal is to find an object or
objects of interest, such techniques can be a useful heuristic
but are optimising the wrong quantity. An example of such a
problem is an autonomous underwater vehicle (AUV) searching
the sea floor for hydrothermal vents. The state of the art in these
problems is information lookahead in the action-observation
space which is computationally expensive. We present an
original belief-maximisation algorithm for this problem, and
use a simulation of the AUV problem to show that our method
outperforms straightforward entropy reduction and runs much
faster than information lookahead while approaching it in
terms of performance. We further introduce a heuristic using
an orienteering-problem (OP) solver, which improves the
performance of both our belief-maximisation algorithm and
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