#path-finding #bevy #navigation #bevy-ecs #ryot #2d-game #tiled-2d-games

ryot_pathfinder

Provides specialized pathfinding functionalities for Bevy 2D, essential for dynamic navigation and movement within games

2 releases

0.2.3 Apr 29, 2024
0.2.2 Apr 29, 2024

#1419 in Game dev

34 downloads per month
Used in 2 crates

AGPL-3.0-only

2MB
2K SLoC

Ryot PathFinder

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What is Ryot PathFinder

Ryot PathFinder is a high-performance, asynchronous implementation of Pathfinding for Bevy. It is designed to work seamlessly with Bevy's ECS, providing robust pathfinding capabilities for games and simulations that demand dynamic navigation. Even though it's optimized for 2D grid-based environments, it can be easily extended to fit specific game requirements and open-world scenarios. It relies on Ryot Core and has a built integration with Ryot Tiled.

Pathfinding

Pathfinding is a fundamental concept in game development, enabling entities to navigate through complex environments efficiently. It involves finding the shortest path between two points, considering obstacles, and optimizing the route based on various criteria. Pathfinding algorithms are essential for creating engaging gameplay experiences, enabling characters to move intelligently and interact with the game world effectively.

In this context, Ryot PathFinder offers a comprehensive solution for implementing pathfinding systems in Bevy projects, providing a flexible and extensible framework for handling navigation logic. By leveraging asynchronous operations and seamless Bevy integration, developers can create dynamic pathfinding systems that adapt to changing game conditions and player interactions.

PathFinder uses Pathfinding as the underlying pathfinding library. For the 2D default implementation, it uses the A* algorithm to calculate the shortest path between two points.

Capabilities

  • Seamless Bevy Integration: Built to work hand-in-hand with Bevy's ECS, offering smooth integration and ensuring compatibility with Bevy's event systems.
  • Asynchronous Operations: Utilizes async tasks to maintain high performance and responsiveness, particularly suitable for scenarios with extensive pathfinding demands.
  • 2D Optimization: Specially tailored for 2D grid-based navigation, providing robust tools for tile-based and open-world game environments.
  • Extensible Architecture: Designed to be flexible, allowing developers to extend and customize pathfinding logic to fit specific game requirements.

Features

This crate includes an optional feature, ryot_tiled, which integrates with the Ryot Tiled crate to support tiled-2D specific pathfinding systems. This feature extends the pathfinding capabilities to work seamlessly with tiled maps, adding valuable functionality for games using tile-based layouts.

Basic Setup

Before setting up the pathfinder, lets understand the two core concepts of the pathfinder: Pathable<P> and Navigable<N>.

Pathable

The Pathable trait represents the position in the world. It's used to calculate the path between two points. The trait requires the implementation of the generate method, which returns a pathable position based on (x, y, z), a coordinates method that returns the coordinates of the pathable position, and a path_to method that calculates the path to another pathable position.

Navigable

The Navigable trait belongs to ryot_core and is used to determine if a point is navigable or not. Currently, it has two navigation conditions in a dimensional space: is_walkable and blocks_sight. The trait requires the implementation of the is_walkable and blocks_sight methods, which return a boolean value based on the navigable condition. The trait has an append method that appends the navigable value to another navigable value.

It also requires the implementation of the is_default method, which returns true if that implementation corresponds to the default value (useful to skip unnecessary calculations).

Bevy

To integrate ryot_pathfinder you need to add a pathable to your Bevy app. This is done by calling the add_pathable method on your Bevy app builder. A pathable is represented by a pair of Pathable and Navigable implementations <P, N>.

Here is a basic example with the pre-defined Pathable and Navigable implementations from ryot_tiled:

use bevy::prelude::*;
use ryot_pathfinder::prelude::*;
use ryot_core::prelude::*;
use ryot_tiled::prelude::*;

fn setup(mut commands: Commands) {
    commands.spawn(PathFindingQuery::<TilePosition>::default());
}

fn build_app(app: &mut App) -> &mut App {
    app
        .add_plugins(DefaultPlugins)
        .add_pathable::<TilePosition, Flags>()
}

Components

The PathFindingQuery has three main components:

PathFindingQuery<P>

This component is attached to entities that require a pathfinding computation. It specifies the parameters for the pathfinding algorithm:

  • to: target position.
  • cardinal_cost: cost of moving in the cardinal directions.
  • diagonal_cost: cost of moving in the diagonal directions.
  • success_distance: distance from the target position that is considered a successful pathfinding computation.
  • timeout: maximum time in seconds that the pathfinding algorithm can run before returning None.

It's part of the public API and should be used by the user to trigger pathfinding computations.

PathFindingTask<P>

This component is attached to entities that are currently computing a pathfinding algorithm. It holds the future (or task) that will return the path result. It's internal to ryot_pathfinder and cannot be used by the user.

Path<P>

This component is attached to entities that have completed a pathfinding computation. It holds the result of the path finding computation, the actual path that the entity can follow to reach the target.

It's part of the public API and should be used by the user to move the entity along the path.

Workflow

The flow happens in four steps:

  1. PathFindingQuery<P> is added to the entity, specifying the parameters for the pathfinding algorithm.
  2. PathFindingQuery<P> is consumed by trigger_path_finding_tasks system, which creates a PathFindingTask<P> and attaches it to the entity.
  3. PathFindingTask<P> is executed asynchronously, and once it completes, the handle_path_finding_tasks system creates a Path<P> component and attaches it to the entity.
  4. The Path<P> can now be consumed by the user to move the entity along the path.

To better understand how PathFindingQuery<P>, PathFindingTask<P>, and Path<P> interact, check the diagram below:

Examples

Choose an example to run based on your needs, such as handling multiple entities or dealing with obstacles:

cargo run --example example_name

Replace example_name with the name of the example you wish to run.

Understanding the Examples

Each example included in the library showcases different aspects of the pathfinding system:

  • Basic: Demonstrates the simplest form of pathfinding.
  • Multiple: Handles multiple actors navigating simultaneously.
  • Obstacles: Integrates static obstacles within pathfinding calculations.

Experimenting with Advanced Scenarios

As you grow more comfortable, explore more complex examples like stress tests or integration with tile-based systems:

  • Tiled*: Utilizes the ryot_tiled feature to perform pathfinding on a tiled map, using TilePosition and Flags.
  • Stress Test*: Evaluates the pathfinder's performance under high load conditions.

Those examples require the "ryot_tiled" feature to be enabled.

Building Your Own Scenarios

Leverage the ExampleBuilder to customize and create tailored pathfinding example/test scenarios:

fn main() {
    // ExampleBuilder::<P/* custom pathable type */, N/* custom navigable type */>::new()
    //     .with_grid_size(/* custom dimension of a squared grid, default 10 */)
    //     .with_n_entities(/* number of actors to spawn, default 10 */)
    //     .with_n_obstacles(/* number of obstacles to spawn, default 0 */)
    //     .with_max_distance(/* maximum distance to calculate pathfinding, default 10 */)
    //     .with_sleep(/* sleep time (ms) between consuming pathfinding results, default 100 */)
    //     .with_navigable(/* custom navigable (N) value for obstacles, default N::default() */)
    //     .with_query_builder(/* custom query builder, default PathFindingQuery::new(pos).with_success_distance(0.) */)
    //     .drawing_app()
    //     .run();
}

Benchmarks

Performance benchmarks are included to provide insights into the crate's efficiency. The benchmark bench can be run to evaluate performance under various conditions:

cargo bench --features ryot_tiled

Results

Test Name Size Time (ns/iter) Variability (± ns) Iterations per Second (iters/s)
bench_2_sized_path_finding 2 137 2 7,299,270
bench_3_sized_path_finding 3 166 2 6,024,096
bench_5_sized_path_finding 5 285 6 3,508,772
bench_10_sized_path_finding 10 1,131 73 884,148
bench_15_sized_path_finding 15 3,272 188 305,709
bench_20_sized_path_finding 20 7,139 691 140,088
bench_with_obstacles 20 40,044 4,442 24,973
bench_30_sized_path_finding 30 21,406 831 46,726
bench_50_sized_path_finding 50 81,027 5,558 12,343
bench_75_sized_path_finding 75 225,945 86,525 4,424
bench_100_sized_path_finding 100 445,268 241,426 2,246

This README format clearly sections out the features, example usage, and benchmarks, providing a comprehensive guide for anyone looking to integrate the ryot_pathfinder crate into their projects.

Dependencies

~17–30MB
~501K SLoC