Making A∗ run faster than D∗-lite for path-planning in partially known terrain

Abstract

Focused D∗ and D∗-Lite are two popular incremental heuristic search algorithm amenable to goal-directed navigation in partially known terrain. Recently it has been shown that, unlike commonly believed, a version of A∗ is in many cases faster than D∗-Lite, posing the question of whether or not there exist other variants of A∗ which could outperform algorithms in the D∗ family on most problems. In this paper we present Multipath Adaptive A∗ (MPAA∗), a simple, easy-to-implement modification of Adaptive A∗ (AA∗) that reuses paths found in previous searches to speed up subsequent searches, and that almost always outperforms D∗Lite. We evaluate MPAA∗ against D∗-Lite on random maps and standard game, room, and maze maps, assuming partially known terrain. In environments comparable to indoor and outdoor navigation (room and game maps) MPAA∗ is 35% faster than D∗Lite on average, while on random maps MPAA∗ is over 3 times faster than D∗Lite. D∗Lite is faster than MPAA∗ only in mazes; notwithstanding, we show that if a small percentage of obstacle cells in a maze are made traversable, MPAA∗ outperforms D∗Lite. In addition, we prove MPAA∗ is optimal and that it finds a solution if one exists. We conclude that for most real-life goal-directed navigation applications MPAA∗ should be preferred to D∗Lite.