GPU-based Dynamic Search on Adaptive Resolution Grids

Francisco M. Garcia, Mubbasir Kapadia, Norman I. Badler

Abstract:

This paper presents a GPU-based wave-front
propagation technique for multi-agent path planning in extremely large, complex, dynamic environments. Our work
proposes an adaptive subdivision of the environment with
efficient indexing, update, and neighbor-finding operations on
the GPU to address several known limitations in prior work.
In particular, an adaptive environment representation reduces
the device memory requirements by an order of magnitude
which enables for the first time, GPU-based goal path planning
in truly large-scale environments (> 2048 m2) for hundreds
of agents with different targets. We compare our approach to
prior work that uses an uniform grid on several challenging
navigation benchmarks and report significant memory savings,
and up to a 1000X computational speedup.