  In this paper, we propose a probabilistic consensus-based multi-robot search
strategy that is robust to communication link failures, and thus is suitable
for disaster affected areas. The robots, capable of only local communication,
explore a bounded environment according to a random walk modeled by a
discrete-time discrete-state (DTDS) Markov chain and exchange information with
neighboring robots, resulting in a time-varying communication network topology.
The proposed strategy is proved to achieve consensus, here defined as agreement
on the presence of a static target, with no assumptions on the connectivity of
the communication network. Using numerical simulations, we investigate the
effect of the robot population size, domain size, and information uncertainty
on the consensus time statistics under this scheme. We also validate our
theoretical results with 3D physics-based simulations in Gazebo. The
simulations demonstrate that all robots achieve consensus in finite time with
the proposed search strategy over a range of robot densities in the
environment.
