  Avoiding collisions is the core problem in multi-agent navigation. In
decentralized settings, when agents have limited communication and sensory
capabilities, collisions are typically avoided in a reactive fashion, relying
on local observations/communications. Prominent collision avoidance techniques,
e.g. ORCA, are computationally efficient and scale well to a large number of
agents. However, in numerous scenarios, involving navigation through the tight
passages or confined spaces, deadlocks are likely to occur due to the egoistic
behaviour of the agents and as a result, the latter can not achieve their
goals. To this end, we suggest an application of the locally confined
multi-agent path finding (MAPF) solvers that coordinate sub-groups of the
agents that appear to be in a deadlock (to detect the latter we suggest a
simple, yet efficient ad-hoc routine). We present a way to build a grid-based
MAPF instance, typically required by modern MAPF solvers. We evaluate two of
them in our experiments, i.e. Push and Rotate and a bounded-suboptimal version
of Conflict Based Search (ECBS), and show that their inclusion into the
navigation pipeline significantly increases the success rate, from 15% to 99%
in certain cases.
