18 releases (5 breaking)
0.8.0 | Apr 21, 2023 |
---|---|
0.7.6 | Apr 16, 2023 |
0.6.2 | Apr 9, 2023 |
0.5.1 | Mar 31, 2023 |
0.3.5 | Mar 29, 2023 |
#1874 in Algorithms
77KB
1.5K
SLoC
Path finding library
This library will contain standard path finding algorithms and return the resulting graph object. You can search for paths in a graph based or grid based structure.
Table of contents generated with markdown-toc
Currently supported:
- Construct graphs
- Graph operations
- Create grids
- Grid operations
- Create Minimum Spanning Tree (MST) from a graph
- Find path with Depth-First Search (DFS)
- With Breadth-First Search (BFS)
- With Bidirectional Breadth-First Search (BBFS)
- With the Dijkstra algorithm
- With the A* algorithm, with heuristic:
- Euclidean distance
- Manhattan distance
- TBC: with a Hierarchical Path-Finding A* (HPA*), with heuristic:
- Euclidean distance
- Manhattan distance
Download the crate: https://crates.io/crates/path-finding
How to use
At the moment, we have following major concepts:
- Edge
- Node
- Graph
- Vec3
- Grid
You only need to pass edges to the graph. The nodes are generated automatically. Each pathfinding method will accept a graph, and return a graph that only contains the edges and nodes of the result.
Alternatively, you can also create a graph if you provide an adjacency matrix. Edges and nodes will be generated automatically.
If you want to use the A* path-finding algorithm, please make sure to provide positional information for each node.
Additionally, we work on a support for each of those algorithms based on a 2D grid based structure.
Create Graph
- Create Edge
pub fn your_function() {
graph::Edge::from(
0 /* edge index */,
0 /* source node */,
1 /* destination node */,
0.1, /* weight */
);
}
- Create Graph from edges
pub fn your_function() {
graph::Graph::from(Vec::from([edge1, edge2]));
}
- Create Graph from adjacency matrix
pub fn your_function() {
let mut matrix: &[&[f32]] = &[
&[0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.0, 0.0],
&[4.0, 0.0, 8.0, 0.0, 0.0, 0.0, 0.0, 11.0, 0.0],
&[0.0, 8.0, 0.0, 7.0, 0.0, 4.0, 0.0, 0.0, 2.0],
&[0.0, 0.0, 7.0, 0.0, 9.0, 14.0, 0.0, 0.0, 0.0],
&[0.0, 0.0, 0.0, 9.0, 0.0, 10.0, 0.0, 0.0, 0.0],
&[0.0, 0.0, 4.0, 14.0, 10.0, 0.0, 2.0, 0.0, 0.0],
&[0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 1.0, 6.0],
&[8.0, 11.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 7.0],
&[0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 6.0, 7.0, 0.0]
];
graph::Graph::from_adjacency_matrix(matrix);
}
Graph operations
You may want to get some information or mutate the graph in some way. Therefore, the graph currently supports three functions for convenience operations or to provide data for a heuristic function.
sorted_by_weight_asc
pub fn your_function() {
let edges: Vec<Edge> = graph.sorted_by_weight_asc(); // will return a vector with edges ascending by weight
}
offer_positions
pub fn your_function() {
// provide a hashmap, mapping the node id to a position - used for a* pathfinding heuristics
graph.offer_positions(HashMap::from([(1, Vec3::from(0.1, 0.2, 0.3))]));
}
Create Grid
- Create Grid from cost matrix
In some cases, such as 2D games, a grid based structure is advantageous. The grid can be created by passing a cost
matrix to the construction function ::from
. At each entry of the matrix you provide an unsigned integer, indicating
the effort it takes
to move to this position from any neighbouring position. In the example below moving from position (0, 0)
to position
(0, 1)
will require an effort, or require a cost, of 2. Equivalently, moving from (2, 2)
to (1, 1)
will incur a
cost of 1. Based on this information, we want to provide a support for grid based structures in each path finding
algorithm.
pub fn your_function() {
let grid = grid::Grid::from(&[
&[4.0, 2.0, 1.0],
&[2.0, 1.0, 0.0],
&[3.0, 4.0, 7.0]
]);
}
Grid operations
outside
Check if a position is outside the grid. Pass a coordinate to the function below.
pub fn your_function() {
grid.outside((0, 1));
}
within
Check if a position is within the grid. Pass a coordinate to the function below.
pub fn your_function() {
grid.within((0, 1));
}
node_id
Convert your coordinate to a node_id
pub fn your_function() {
grid.node_id((0, 1));
}
cost
Retrieve the cost required to move to a provided node id
pub fn your_function() {
grid.cost(3);
}
Minimum spanning tree
pub fn your_function() {
let mst_graph = graph::minimum_spanning(graph);
}
Depth-first search
For graphs
pub fn your_function() {
let dfs = path::in_graph(
4 /* source */,
1 /* target */,
&graph,
Box::from(DepthFirstSearch {}) /* used algorithm */
);
}
For grids
pub fn your_function() {
let dfs = path::in_grid(
4 /* source */,
1 /* target */,
&grid,
Box::from(DepthFirstSearch {}) /* used algorithm */
);
}
Breadth-first search
For graphs
pub fn your_function() {
let bfs = path::in_graph(
4 /* source */,
1 /* target */,
&graph,
Box::from(BreadthFirstSearch {}) /* used algorithm */
);
}
For grids
pub fn your_function() {
let dfs = path::in_grid(
4 /* source */,
1 /* target */,
&grid,
Box::from(BreadthFirstSearch {}) /* used algorithm */
);
}
Bidirectional breadth-first search
For graphs
pub fn your_function() {
let bi_bfs = path::in_graph(
4 /* source */,
1 /* target */,
&graph,
Box::from(BiBreadthFirstSearch {}) /* used algorithm */
);
}
For grids
pub fn your_function() {
let bi_bfs = path::in_grid(
4 /* source */,
1 /* target */,
&grid,
Box::from(BiBreadthFirstSearch {}), /* used algorithm */
);
}
Dijkstra path search
For graphs
pub fn your_function() {
let dijkstra = path::in_graph(
4 /* source */,
1 /* target */,
&graph,
Box::from(Dijkstra {}) /* used algorithm */
);
}
For grids
pub fn your_function() {
let bi_bfs = path::in_grid(
4 /* source */,
1 /* target */,
&grid,
Box::from(Dijkstra {}), /* used algorithm */
);
}
A* path search
You can use the A* path-finding algorithm by providing either an existing heuristic function as shown below. Or you provide your own heuristic function. In case you use an existing heuristic function, make sure to provide the positional information for the nodes.
For graphs
pub fn your_function_with_euclidean_distance() {
let a_star = path::in_graph(
4 /* source */,
1 /* target */,
&graph,
Box::from(AStar { heuristic: Box::from(euclidean_distance) }), /* used algorithm + euclidean distance heuristic function */
);
}
pub fn your_function_with_manhattan_distance() {
let a_star = path::in_graph(
4 /* source */,
1 /* target */,
&graph,
Box::from(AStar { heuristic: Box::from(manhattan_distance) }), /* used algorithm + manhattan distance heuristic function */
);
}
For grids
pub fn your_function_with_euclidean_distance() {
let a_star = path::in_grid(
4 /* source */,
1 /* target */,
&grid,
Box::from(AStar { heuristic: Box::from(euclidean_distance) }), /* used algorithm + euclidean distance heuristic function */
);
}
pub fn your_function_with_manhattan_distance() {
let a_star = path::in_grid(
4 /* source */,
1 /* target */,
&grid,
Box::from(AStar { heuristic: Box::from(manhattan_distance) }), /* used algorithm + manhattan distance heuristic function */
);
}
TBC: Hierarchical A* path search
Similar to the A* path-finding algorithm, you can provide either an existing heuristic function as shown in the previous section. Or you provide your own heuristic function. In case you use an existing heuristic function, make sure to provide the positional information for the nodes.
In addition to the functionality of A*, this algorithm will require you to pass the graph to the Hierarchical A* instance. The reason is simple, the algorithm will divide the graph into segments on creation and cache information required in the subsequent path-finding process.
pub fn your_function_with_euclidean_distance() {
let a_star = path::in_graph(
4 /* source */,
1 /* target */,
&graph,
Box::from(HierarchicalAStar { heuristic, graph }), /* used algorithm and graph */
);
}
Dependencies
~3MB
~58K SLoC