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https://github.com/beefsack/go-astar
Go implementation of the A* search algorithm
https://github.com/beefsack/go-astar
Last synced: about 2 months ago
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Go implementation of the A* search algorithm
- Host: GitHub
- URL: https://github.com/beefsack/go-astar
- Owner: beefsack
- License: mit
- Created: 2014-05-28T02:00:03.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2022-01-27T15:08:37.000Z (almost 3 years ago)
- Last Synced: 2024-10-25T05:22:29.119Z (about 2 months ago)
- Language: Go
- Size: 18.6 KB
- Stars: 598
- Watchers: 12
- Forks: 80
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- awesome-go - go-astar - Go implementation of the A\* path finding algorithm. (Game Development / Search and Analytic Databases)
- awesome-go - go-astar - Go implementation of the A* search algorithm - ★ 287 (Game Development)
- awesome-go-extra - go-astar - 05-28T02:00:03Z|2022-01-27T15:08:37Z| (Game Development / Advanced Console UIs)
README
go-astar
========**A\* pathfinding implementation for Go**
[![Build Status](https://travis-ci.org/beefsack/go-astar.svg?branch=master)](https://travis-ci.org/beefsack/go-astar)
The [A\* pathfinding algorithm](http://en.wikipedia.org/wiki/A*_search_algorithm) is a pathfinding algorithm noted for its performance and accuracy and is commonly used in game development. It can be used to find short paths for any weighted graph.
A fantastic overview of A\* can be found at [Amit Patel's Stanford website](http://theory.stanford.edu/~amitp/GameProgramming/AStarComparison.html).
Examples
--------The following crude examples were taken directly from the automated tests. Please see `path_test.go` for more examples.
### Key
* `.` - Plain (movement cost 1)
* `~` - River (movement cost 2)
* `M` - Mountain (movement cost 3)
* `X` - Blocker, unable to move through
* `F` - From / start position
* `T` - To / goal position
* `●` - Calculated path### Straight line
```
.....~...... .....~......
.....MM..... .....MM.....
.F........T. -> .●●●●●●●●●●.
....MMM..... ....MMM.....
............ ............
```### Around a mountain
```
.....~...... .....~......
.....MM..... .....MM.....
.F..MMMM..T. -> .●●●MMMM●●●.
....MMM..... ...●MMM●●...
............ ...●●●●●....
```### Blocked path
```
............
.........XXX
.F.......XTX -> No path
.........XXX
............
```### Maze
```
FX.X........ ●X.X●●●●●●..
.X...XXXX.X. ●X●●●XXXX●X.
.X.X.X....X. -> ●X●X.X●●●●X.
...X.X.XXXXX ●●●X.X●XXXXX
.XX..X.....T .XX..X●●●●●●
```### Mountain climber
```
..F..M...... ..●●●●●●●●●.
.....MM..... .....MM...●.
....MMMM..T. -> ....MMMM..●.
....MMM..... ....MMM.....
............ ............
```### River swimmer
```
.....~...... .....~......
.....~...... ....●●●.....
.F...X...T.. -> .●●●●X●●●●..
.....M...... .....M......
.....M...... .....M......
```Usage
-----### Import the package
```go
import "github.com/beefsack/go-astar"
```### Implement Pather interface
An example implementation is done for the tests in `path_test.go` for the Tile type.
The `PathNeighbors` method should return a slice of the direct neighbors.
The `PathNeighborCost` method should calculate an exact movement cost for direct neighbors.
The `PathEstimatedCost` is a heuristic method for estimating the distance between arbitrary tiles. The examples in the test files use [Manhattan distance](http://en.wikipedia.org/wiki/Taxicab_geometry) to estimate orthogonal distance between tiles.
```go
type Tile struct{}func (t *Tile) PathNeighbors() []astar.Pather {
return []astar.Pather{
t.Up(),
t.Right(),
t.Down(),
t.Left(),
}
}func (t *Tile) PathNeighborCost(to astar.Pather) float64 {
return to.MovementCost
}func (t *Tile) PathEstimatedCost(to astar.Pather) float64 {
return t.ManhattanDistance(to)
}
```### Call Path function
```go
// t1 and t2 are *Tile objects from inside the world.
path, distance, found := astar.Path(t1, t2)
if !found {
log.Println("Could not find path")
}
// path is a slice of Pather objects which you can cast back to *Tile.
```Authors
-------Michael Alexander
Robin Ranjit Chauhan