An Effective Algorithmic Framework for Near Optimal Multi-Robot Path Planning
This addresses efficient path planning for multiple robots in dense settings, offering a centralized framework with orders of magnitude speed improvements, though it appears incremental as it builds on discretization and optimization techniques.
The paper tackles multi-robot path planning in continuous 2D environments by minimizing task completion time, achieving near-optimal solutions for up to 50 robots in seconds with high robot density, and scaling to hundreds of robots occupying over 30% of free space.
We present a centralized algorithmic framework for solving multi-robot path planning problems in general, two-dimensional, continuous environments while minimizing globally the task completion time. The framework obtains high levels of effectiveness through the composition of an optimal discretization of the continuous environment and the subsequent fast, near-optimal resolution of the resulting discrete planning problem. This principled approach achieves orders of magnitudes better performance with respect to both speed and the supported robot density. For a wide variety of environments, our method is shown to compute globally near-optimal solutions for fifty robots in seconds with robots packed close to each other. In the extreme, the method can consistently solve problems with hundreds of robots that occupy over 30% of the free space.