Association for the Advancement of Artificial Intelligence Press (United States)
Abstract
Any-angle pathfinding is a fundamental problem in robotics and computer games. The
goal is to find a shortest path between a pair of points on a grid map such that the
path is not artificially constrained to the points of the grid. Prior research has focused
on approximate online solutions. A number of exact methods exist but they all require super-linear space and pre-processing time. In this study, we describe Anya: a new and optimal any-angle pathfinding algorithm. Where other works find approximate any-angle
paths by searching over individual points from the grid, Anya finds optimal paths by
searching over sets of states represented as intervals. Each interval is identified on-the-fly. From each interval Anya selects a single representative point that it uses to compute an admissible cost estimate for the entire set. Anya always returns an optimal path if one exists. Moreover it does so without any offline pre-processing or the introduction of additional memory overheads. In a range of empirical comparisons we show that Anya is
competitive with several recent (sub-optimal) online and pre-processing based techniques and is up to an order of magnitude faster than the most common benchmark algorithm, a grid-based implementation of A*