  Exploration of unknown environments is an important challenge in the field of
robotics. While a single robot can achieve this task alone, evidence suggests
it could be accomplished more efficiently by groups of robots, with advantages
in terms of terrain coverage as well as robustness to failures. Exploration can
be guided through belief maps, which provide probabilistic information about
which part of the terrain is interesting to explore (either based on risk
management or reward). This process can be centrally coordinated by building a
collective belief map on a common server. However, relying on a central
processing station creates a communication bottleneck and single point of
failure for the system. In this paper, we present Distributed Online Risk-Aware
(DORA) Explorer, an exploration system that leverages decentralized information
sharing to update a common risk belief map. DORA Explorer allows a group of
robots to explore an unknown environment discretized as a 2D grid with
obstacles, with high coverage while minimizing exposure to risk, effectively
reducing robot failures
