Methods › Reinforcement Learning › Heuristic Search Algorithms › 4D A*
Four-dimensional A-star
4D A*
Introduced by Andrea de Giorgio et al. in Artificial Intelligence Control in 4D Cylindrical Space for Industrial Robotic Applications
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
The aim of 4D A* is to find the shortest path between two four-dimensional (4D) nodes of a 4D search space - a starting node and a target node - as long as there is a path. It achieves both optimality and completeness. The former is because the path is shortest possible, and the latter because if the solution exists the algorithm is guaranteed to find it.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Artificial Intelligence Control in 4D Cylindrical Space for Industrial Robotic Applications 23 Sep 2020 · 1 repository
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Industrial Robots | 1 |
| Motion Planning | 1 |
| Trajectory Planning | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections