https://github.com/sunnyallana/astar-threaded-search
This repo implements A* search with multithreading for faster pathfinding.
https://github.com/sunnyallana/astar-threaded-search
astar-algorithm astar-pathfinding astar-search astar-search-algorithm operating-system
Last synced: about 1 year ago
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This repo implements A* search with multithreading for faster pathfinding.
- Host: GitHub
- URL: https://github.com/sunnyallana/astar-threaded-search
- Owner: sunnyallana
- License: mit
- Created: 2024-03-22T15:10:32.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-13T18:21:00.000Z (almost 2 years ago)
- Last Synced: 2025-01-13T09:34:57.435Z (about 1 year ago)
- Topics: astar-algorithm, astar-pathfinding, astar-search, astar-search-algorithm, operating-system
- Language: C++
- Homepage:
- Size: 1.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## About This Repository
This repository hosts an implementation of the A* search algorithm using threads for parallel processing. A* (pronounced "A star") is a popular pathfinding algorithm commonly used in artificial intelligence and robotics for finding the shortest path between two points. By leveraging multithreading capabilities, this implementation enhances the efficiency of the A* algorithm, allowing it to explore potential paths concurrently.
### Key Features:
- **Threaded Processing**: Utilizes multiple threads to explore potential paths simultaneously, improving search performance on multicore systems.
- **Efficient Pathfinding**: Employs the A* algorithm to efficiently find the shortest path between two points on a graph or grid.
- **Configurable Parameters**: Allows customization of various parameters such as heuristic functions, thread count, and grid configurations to suit different use cases.
- **Clear Documentation**: Well-commented code and comprehensive documentation to aid understanding and further development.
### Usage:
1. **Clone Repository**: Clone this repository to your local machine.
2. **Compile**: Compile the source code using your preferred compiler with support for multithreading.
3. **Run**: Execute the compiled binary and specify the input parameters as needed.
4. **Explore**: Experiment with different configurations and parameters to observe performance variations.
### Contributions:
Contributions to this repository are welcome! Whether you want to optimize performance, add new features, or improve documentation, feel free to fork the repository and submit a pull request.
### License:
This project is licensed under the [MIT License](LICENSE), allowing for both personal and commercial use with proper attribution.