Zeta*-SIPP: Improved Time-Optimal Any-Angle Safe-Interval Path Planning

Y. Zou*, C. Borst

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Any-angle path planning is an extension of traditional path-planning algorithms that aims to generate smoother and shorter paths in graphs by allowing any-angle moves between vertices, rather than being restricted by edges. Many any-angle path-planning algorithms have been proposed, such as Theta*, Block A* and Anya, but most of them are designed only for static environments, which is not applicable when dynamic obstacles are present. Time-Optimal Any-Angle Safe-Interval Path Planning (TO-AA-SIPP) was developed to fill this gap, which can find an optimal collision-free any-angle path that minimizes the traversal time. However, as indicated by its authors, TO-AA-SIPP may not be efficient enough to be used in multi-agent pathfinding (MAPF). Therefore, this paper presents a new algorithm Zeta*-SIPP to improve TO-AA-SIPP by means of 1) reducing useless search nodes that have no contribution to finding optimal solutions, and 2) introducing Field of View (FoV) instead of Line of Sight (LoS) to speed up visibility checks with static obstacles. Benchmark experiments showed that Zeta*-SIPP reduced the computation time of TO-AA-SIPP by around 70%-90% on average.
Original languageEnglish
Title of host publicationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
EditorsKate Larson
PublisherInternational Joint Conferences on Artificial Intelligence (IJCAI)
Pages6823-6830
Number of pages8
ISBN (Electronic)978-1-956792-04-1
Publication statusPublished - 2024
Event33rd International Joint Conference on Artificial Intelligence - International Convention Center Jeju (ICC Jeju), Jeju Island, Korea, Republic of
Duration: 3 Aug 20249 Aug 2024
Conference number: 33
https://ijcai24.org/

Conference

Conference33rd International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI 2024
Country/TerritoryKorea, Republic of
CityJeju Island
Period3/08/249/08/24
Internet address

Funding

The authors would like to thank the financial support from the China Scholarship Council (CSC) No. 202106830036.

Keywords

  • any-angle path planning
  • multi-agent path finding (MAPF)
  • safe-interval path planning
  • heuristic search

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