Computing Close to Optimal Weighted Shortest Paths in Practice

Tran, Nguyet; Dinneen, Michael J.; Linz, Simone

doi:10.1609/icaps.v30i1.6673

Cited by 3 publications

(1 citation statement)

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“…Bounded suboptimal approximations exist but they run in high-order polynomial time (Mitchell and Papadimitriou 1991). Unbounded suboptimal methods for WRP also exist, but are often complex to implement or not practical for performance sensitive applications such as games; see (Tran, Dinneen, and Linz 2020) for a recent example.…”

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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Path Planning in a Weighted Planar Subdivision Under the Manhattan Metric

Davoodi,

Safari

2024

Graphs and Combinatorics

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In this paper, we consider the problem of path planning in a weighted polygonal planar subdivision. Each polygon has an associated positive weight which shows the cost of path per unit distance of movement in that polygon. The goal is to find a minimum cost path under the Manhattan metric for two given start and destination points. First, we propose an $$O(n^2)$$ O ( n 2 ) time and space algorithm to solve this problem, where n is the total number of vertices in the subdivision. Then, we improve the time and space complexity of the algorithm to $$O(n \log ^2 n)$$ O ( n log 2 n ) and $$O(n \log n)$$ O ( n log n ) , respectively, by applying a divide and conquer approach. We also study the case of rectilinear regions in three dimensions and show that the minimum cost path under the Manhattan metric is obtained in $$ O(n^2 \log ^3 n) $$ O ( n 2 log 3 n ) time and $$ O(n^2 \log ^2 n) $$ O ( n 2 log 2 n ) space.

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