Prioritized Multi-agent Path Finding for Differential Drive Robots
This work addresses the gap between theoretical multi-agent path planning and practical deployment for robotic systems, though it is incremental as it builds on an existing prioritized planner.
The authors tackled the problem of generating executable collision-free trajectories for fleets of differential drive robots by modifying the AA-SIPP(m) prioritized planner to lift restrictive assumptions like synchronized moves and uniform robot properties, resulting in an algorithm that scales to hundreds of robots in simulation and produces safely executable solutions in real-world tests.
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.