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Methods for centralized planning of the collision-free trajectories for afleet of mobile robots typically solve the discretized version of the problemand rely on numerous simplifying assumptions, e.g. moves of uniform duration,cardinal only translations, equal speed and size of the robots etc., thus theresultant plans can not always be directly executed by the real roboticsystems. To mitigate this issue we suggest a set of modifications to theprominent prioritized planner -- AA-SIPP(m) -- aimed at lifting the mostrestrictive assumptions (syncronized translation only moves, equal size andspeed of the robots) and at providing robustness to the solutions. We evaluatethe suggested algorithm in simulation and on differential drive robots intypical lab environment (indoor polygon with external video-based navigationsystem). The results of the evaluation provide a clear evidence that thealgorithm 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 proneto imperfect trajectory following. The video of the experiments can be found athttps://youtu.be/Fer_irn4BG0.

Prioritized Multi-agent Path Finding for Differential Drive Robots