Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Robot Target Search using Probabilistic Consensus on Discrete
  Markov Chains   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.

19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc