Proceedings of the AAAI Conference on Artificial Intelligence · 2011 · 378 citations · 12 references
ComputingComputer ScienceAlgorithmsComputational GeometryPath PlanningCombinatorial OptimizationGraph AlgorithmsGraph PruningJump SearchMemory OverheadOnline Graph AlgorithmsPath OptimizationPathfinding AlgorithmsPruningSearch Strategy Evaluation
Pathfinding in uniform‑cost grid environments is common in robotics and video games, yet current hierarchical algorithms are fast and memory‑efficient but often produce suboptimal paths. This work introduces a grid‑specific search strategy that is fast, optimal, and requires no memory overhead. The algorithm employs a macro operator that identifies jump points and expands only those nodes, leaving intermediate nodes between jump points unexpanded. Empirical results show that using jump points accelerates A* by at least an order of magnitude and yields significant improvements over the state of the art.
Pathfinding in uniform-cost grid environments is a problem commonly found in application areas such as robotics and video games. The state-of-the-art is dominated by hierarchical pathfinding algorithms which are fast and have small memory overheads but usually return suboptimal paths. In this paper we present a novel search strategy, specific to grids, which is fast, optimal and requires no memory overhead. Our algorithm can be described as a macro operator which identifies and selectively expands only certain nodes in a grid map which we call jump points. Intermediate nodes on a path connecting two jump points are never expanded. We prove that this approach always computes optimal solutions and then undertake a thorough empirical analysis, comparing our method with related works from the literature. We find that searching with jump points can speed up A* by an order of magnitude and more and report significant improvement over the current state of the art.
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Near Optimal Hierarchical Path-Finding.
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