  Methods for centralized planning of the collision-free trajectories for a
fleet of mobile robots typically solve the discretized version of the problem
and rely on numerous simplifying assumptions, e.g. moves of uniform duration,
cardinal only translations, equal speed and size of the robots etc., thus the
resultant plans can not always be directly executed by the real robotic
systems. To mitigate this issue we suggest a set of modifications to the
prominent prioritized planner -- AA-SIPP(m) -- aimed at lifting the most
restrictive assumptions (syncronized translation only moves, equal size and
speed of the robots) and at providing robustness to the solutions. We evaluate
the suggested algorithm in simulation and on differential drive robots in
typical lab environment (indoor polygon with external video-based navigation
system). The results of the evaluation provide a clear evidence that the
algorithm scales well to large number of robots (up to hundreds in simulation)
and is able to produce solutions that are safely executed by the robots prone
to imperfect trajectory following. The video of the experiments can be found at
https://youtu.be/Fer_irn4BG0.
