The FastMap Algorithm for Shortest Path Computations

Cohen, Liron; Uras, Tansel; Jahangiri, Shiva; Arunasalam, Aliyah; Koenig, Sven; Kumar, T. K. Satish

doi:10.48550/arxiv.1706.02792

Cited by 2 publications

(2 citation statements)

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“…Another type of speedup technique involves precomputed heuristic functions. Examples include singledimension estimators, such as Differential Heuristics (Sturtevant et al 2009) and FastMap (Cohen et al 2017), as well as APSP oracles, such as Compressed Path Databases (Bono et al 2019) and Hub Labels (Delling et al 2014). These approaches all assume that traversal costs can never fall below some floor (e.g., free-flow cost in a road network).…”

Section: Related Workmentioning

confidence: 99%

Optimal Pathfinding on Weighted Grid Maps

Carlson,

Moghadam,

Harabor

et al. 2023

AAAI

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In many computer games up to hundreds of agents navigate in real-time across a dynamically changing weighted grid map. Pathfinding in these situations is challenging because the grids are large, traversal costs are not uniform, and because each shortest path has many symmetric permutations, all of which must be considered by an optimal online search. In this work we introduce Weighted Jump Point Search (JPSW), a new type of pathfinding algorithm which breaks weighted grid symmetries by introducing a tiebreaking policy that allows us to apply effective pruning rules in symmetric regions. We show that these pruning rules preserve at least one optimal path to every grid cell and that their application can yield large performance improvements for optimal pathfinding. We give a complete theoretical description of the new algorithm, including pseudo-code. We also conduct a wide-ranging experimental evaluation, including data from real games. Results indicate JPSW is up to orders of magnitude faster than the nearest baseline, online search using A*.

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Section: Related Workmentioning

confidence: 99%

Optimal Pathfinding on Weighted Grid Maps

Carlson,

Moghadam,

Harabor

et al. 2023

AAAI

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“…Over the years, numerous excellent algorithms have been proposed, and the Grid-Based Path Planning Competition (GPPC) (Sturtevant et al 2015) has served as the cornerstone for the development of these novel path-planning achievements. The impact of these advancements is essential and far-reaching, and they will revolutionize path planning in the coming years (Botea 2011;Uras and Koenig 2014;Harabor and Grastien 2014;Rabin and Sturtevant 2016;Sturtevant and Rabin 2016;Uras and Koenig 2017;Goldenberg et al 2017;Cohen et al 2017;Harabor and Stuckey 2018;Salvetti et al 2018;Hu et al 2019Hu et al , 2021.…”

Section: Introductionmentioning

confidence: 99%

More Flexible Proximity Wildcards Path Planning with Compressed Path Databases

Chen,

Zhang,

Zhang

2024

ICAPS

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Grid-based path planning is one of the classic problems in AI, and a popular topic in application areas such as computer games and robotics. Compressed Path Databases (CPDs) are recognized as a state-of-the-art method for grid-based path planning. It is able to find an optimal path extremely fast without state-space search. In recent years, researchers have tended to focus on improving CPDs by reducing CPD size or improving search performance. Among various methods, proximity wildcards are one of the most proven improvements in reducing the size of CPD. However, its proximity area is significantly restricted by complex terrain, which significantly affects the pathfinding efficiency and causes additional costs. In this paper, we enhance CPDs from the perspective of improving search efficiency and reducing search costs. Our work focuses on using more flexible methods to obtain larger proximity areas, so that more heuristic information can be used to improve search performance. Experiments conducted on the Grid-Based Path Planning Competition (GPPC) benchmarks demonstrate that the two proposed methods can effectively improve search efficiency and reduce search costs by up to 3 orders of magnitude. Remarkably, our methods can further reduce the storage cost, and improve the compression capability of CPDs simultaneously.

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The FastMap Algorithm for Shortest Path Computations

Cited by 2 publications

References 0 publications

Optimal Pathfinding on Weighted Grid Maps

Optimal Pathfinding on Weighted Grid Maps

More Flexible Proximity Wildcards Path Planning with Compressed Path Databases

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