Path-length analysis for grid-based path planning

Abstract

In video games and robotics, one often discretizes a continuous 2D environment into a regular grid with blocked and unblocked cells and then finds shortest paths for the agents on the resulting grid graph. Shortest grid paths, of course, are not necessarily true shortest paths in the continuous 2D environment. In this article, we therefore study how much longer a shortest grid path can be than a corresponding true shortest path on all regular grids with blocked and unblocked cells that tessellate continuous 2D environments. We study 5 different vertex connectivities that result from both different tessellations and different definitions of the neighbors of a vertex. Our path-length analysis yields either tight or asymptotically tight worst-case bounds in a unified framework. Our results show that the percentage by which a shortest grid path can be longer than a corresponding true shortest path decreases as the vertex connectivity increases. Our path-length analysis is topical because it determines the largest path-length reduction possible for any-angle path-planning algorithms (and thus their benefit), a class of path-planning algorithms in artificial intelligence and robotics that has become popular.