  Robotic solutions for quick disaster response are essential to ensure minimal
loss of life, especially when the search area is too dangerous or too vast for
human rescuers. We model this problem as an asynchronous multi-agent
active-search task where each robot aims to efficiently seek objects of
interest (OOIs) in an unknown environment. This formulation addresses the
requirement that search missions should focus on quick recovery of OOIs rather
than full coverage of the search region. Previous approaches fail to accurately
model sensing uncertainty, account for occlusions due to foliage or terrain, or
consider the requirement for heterogeneous search teams and robustness to
hardware and communication failures. We present the Generalized
Uncertainty-aware Thompson Sampling (GUTS) algorithm, which addresses these
issues and is suitable for deployment on heterogeneous multi-robot systems for
active search in large unstructured environments. We show through simulation
experiments that GUTS consistently outperforms existing methods such as
parallelized Thompson Sampling and exhaustive search, recovering all OOIs in
80% of all runs. In contrast, existing approaches recover all OOIs in less than
40% of all runs. We conduct field tests using our multi-robot system in an
unstructured environment with a search area of approximately 75,000 sq. m. Our
system demonstrates robustness to various failure modes, achieving full
recovery of OOIs (where feasible) in every field run, and significantly
outperforming our baseline.
