#path-finding #screeps #algorithm #goal #pathfinder #position #search

screeps-pathfinding

Pathfinding algorithms for Screeps: World in native Rust

3 releases

0.1.2 Sep 1, 2024
0.1.1 Aug 8, 2024
0.1.0 Jun 25, 2024

#601 in Algorithms

Download history 110/week @ 2024-08-06 5/week @ 2024-08-13 158/week @ 2024-08-27 28/week @ 2024-09-03 30/week @ 2024-09-10 18/week @ 2024-09-17 29/week @ 2024-09-24 42/week @ 2024-10-01 11/week @ 2024-10-08

161 downloads per month

MIT license

81KB
1.5K SLoC

Screeps Pathfinding

Crates.io Version Crates.io License

Pathfinding algorithms in native Rust for the programmable MMO Screeps: World.

Purpose

The intent of this library is to have multiple pathfinding algorithms implemented in native Rust to avoid the need for crossing the WASM boundary and doing pathfinding in JS with Pathfinder. Calling into JS will be avoided wherever possible and will be clearly notated when unavoidable.

The intent of having multiple pathfinding algorithms available is to allow for using more advanced algorithms than just the Jump-Point Search that Pathfinder implements, which would be beneficial for pathfinding with moving goals and starting positions (such as during combat).

For convenience in actually using the library, it also includes utility functions for common pathfinding use-cases, as well as caching structures for terrain, cost matrices, and paths. The intent is not to provide a one-size-fits-all drop-in navigation replacement, but instead to provide flexible building blocks that can be used to build the appropriate solution for an individual Screeps bot.

Current Algorithms

  • Dijkstra's Shortest Path
  • A*

Simple Timing Comparisons

Tests were done on Shard3 of MMO. Iterations were spread across multiple ticks. Start and goal positions were static.

1 iteration:

Algorithm CPU Used
A* 0.1967
Dijkstra 1.3069
Pathfinder 1.9285

5 iterations:

Algorithm CPU Used
A* 0.2142
Dijkstra 1.3660
Pathfinder 0.6378

20 iterations:

Algorithm CPU Used
A* 0.2128
Dijkstra 1.3544
Pathfinder 0.3958

300 iterations:

Algorithm CPU Used
A* 0.2141
Dijkstra 1.3530
Pathfinder 0.2574

Of particular note, Pathfinder averages start to drop significantly after the first iteration, which likely means that there's some JIT optimization going on in JS-Land since we're making the exact same start-goal pathfinding calls each time.

Dependencies

~2.2–3.5MB
~65K SLoC