Reducing Redundant Work in Jump Point Search

TL;DR

This paper analyzes the limitations of Jump Point Search (JPS) in grid-based pathfinding, identifies its pathological behaviors, and proposes Constrained JPS (CJPS), an online method that improves efficiency and speed in dynamic environments.

Contribution

The paper introduces Constrained JPS (CJPS), a novel online approach that mitigates JPS's pathological behaviors and enhances pathfinding performance in dynamic grid scenarios.

Findings

CJPS reduces redundant scanning of map areas.

CJPS generates fewer suboptimal nodes.

CJPS outperforms JPS by up to 14x in certain scenarios.

Abstract

JPS (Jump Point Search) is a state-of-the-art optimal algorithm for online grid-based pathfinding. Widely used in games and other navigation scenarios, JPS nevertheless can exhibit pathological behaviours which are not well studied: (i) it may repeatedly scan the same area of the map to find successors; (ii) it may generate and expand suboptimal search nodes. In this work, we examine the source of these pathological behaviours, show how they can occur in practice, and propose a purely online approach, called Constrained JPS (CJPS), to tackle them efficiently. Experimental results show that CJPS has low overheads and is often faster than JPS in dynamically changing grid environments: by up to 7x in large game maps and up to 14x in pathological scenarios.