Abstract:
We consider a search for a target moving within a
known indoor environment partitioned into interconnected
regions of varying sizes. The knowledge of target location is
described as a probability distribution over the regions, and the
searcher can only move from one region to another as the
structure allows. The objective is to find a feasible path through
the regions that maximizes the probability of locating the target
within fixed time. This problem generalizes the existing optimal
searcher path problem (OSP) by additionally stipulating a
minimum amount of time that a finite-speed searcher must spend
to travel through a region before reaching the next. We propose a
technique to obtain the upper bound of detection for solving the
problem in a branch and bound framework. Comparisons show
that the technique is also superior to known bounding methods
for the original optimal searcher path problem.
