  The increasing use of autonomous robot systems in hazardous environments
underscores the need for efficient search and rescue operations. Despite
significant advancements, existing literature on object search often falls
short in overcoming the difficulty of long planning horizons and dealing with
sensor limitations, such as noise. This study introduces a novel approach that
formulates the search problem as a belief Markov decision processes with
options (BMDP-O) to make Monte Carlo tree search (MCTS) a viable tool for
overcoming these challenges in large scale environments. The proposed
formulation incorporates sequences of actions (options) to move between regions
of interest, enabling the algorithm to efficiently scale to large environments.
This approach also enables the use of customizable fields of view, for use with
multiple types of sensors. Experimental results demonstrate the superiority of
this approach in large environments when compared to the problem without
options and alternative tools such as receding horizon planners. Given compute
time for the proposed formulation is relatively high, a further approximated
"lite" formulation is proposed. The lite formulation finds objects in a
comparable number of steps with faster computation.
