https://github.com/saurabhjalendra/ai-pathfinding
AI Pathfinding Implementation featuring Uniform Cost Search (uninformed) and Recursive Best-First Search (informed) algorithms. Solves grid-based navigation with dynamic cost calculations, performance metrics, and path visualization. Developed for academic research in search algorithms.
https://github.com/saurabhjalendra/ai-pathfinding
artificial-intelligence machine-learning pathfinding python rbfs-algorithm ucs-algorithm
Last synced: 15 days ago
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AI Pathfinding Implementation featuring Uniform Cost Search (uninformed) and Recursive Best-First Search (informed) algorithms. Solves grid-based navigation with dynamic cost calculations, performance metrics, and path visualization. Developed for academic research in search algorithms.
- Host: GitHub
- URL: https://github.com/saurabhjalendra/ai-pathfinding
- Owner: SaurabhJalendra
- Created: 2025-02-25T08:07:24.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-25T14:42:39.000Z (over 1 year ago)
- Last Synced: 2025-06-22T05:33:29.168Z (12 months ago)
- Topics: artificial-intelligence, machine-learning, pathfinding, python, rbfs-algorithm, ucs-algorithm
- Language: Jupyter Notebook
- Homepage:
- Size: 701 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AI Pathfinding Project
## Overview
This repository contains the implementation of a pathfinding solution focused on problem-solving through search algorithms. The project features two primary search strategies: **Uninformed Search** and **Informed Search**, specifically utilizing **Uniform Cost Search (UCS)** and **Recursive Best First Search (RBFS)** algorithms. The system navigates grid-based environments while accounting for dynamic costs and obstacles, providing performance metrics and path visualization.
## Table of Contents
- [Project Description](#project-description)
- [Installation](#installation)
- [Usage](#usage)
- [Algorithms Implemented](#algorithms-implemented)
- [Performance Metrics](#performance-metrics)
- [Contributors](#contributors)
- [License](#license)
- [Acknowledgments](#acknowledgments)
## Project Description
This system implements a **Pathfinding Agent** that navigates grid environments while considering:
- Dynamic movement costs
- Building obstacles and roadblocks
- Real-time path optimization
- Visualized solution paths
- Performance benchmarking
## Installation
To run the code in this repository, ensure you have Python installed on your machine. Follow these steps to set up the environment:
1. **Clone the Repository**:
```bash
git clone https://github.com/SaurabhJalendra/AI-Pathfinding.git
cd AI-Pathfinding
pip install -r requirements.txt
```
2. **Jupyter Notebook**:
Ensure you have Jupyter Notebook installed. If not, you can install it using:
```bash
pip install notebook
```
## Usage
1. **Open the Jupyter Notebook**:
Launch Jupyter Notebook from the command line:
```bash
jupyter notebook "Pathfinding_Implementation.ipynb"
```
2. **Run the Notebook**:
Follow the instructions in the notebook to execute the code cells. The notebook is structured to guide you through the process of setting up the environment, defining the grid, and invoking the search algorithms.
3. **Dynamic Input Handling**:
The code is designed to handle dynamic inputs for the starting and goal positions, allowing for flexible testing of different scenarios.
4. **Visual Output**:
The notebook will display the grid, the paths taken by the agent, and the performance metrics for each algorithm.
## Algorithms Implemented
### 1. Recursive Best First Search (RBFS)
- **Description**: RBFS is an informed search algorithm that uses recursion to explore paths. It maintains a limited memory footprint by discarding paths that are not promising based on heuristic evaluations.
- **Key Features**:
- Utilizes a heuristic function to guide the search.
- Backtracks when a path exceeds the cost of the best alternative path.
- Efficient for smaller grids but may struggle with larger, more complex environments.
### 2. Uniform Cost Search (UCS)
- **Description**: UCS is an uninformed search algorithm that expands the least costly node first. It guarantees finding the optimal path by evaluating all possible paths based on cumulative costs.
- **Key Features**:
- Maintains a priority queue to manage nodes based on their cost.
- Consistently provides the shortest path but may require more memory to store expanded nodes.
- Effective in densely populated grids with numerous obstacles.
## Performance Metrics
The project tracks and displays the following performance metrics for each algorithm:
- **Total Cost**: The cumulative cost of the path taken by the agent.
- **Expanded Nodes**: The number of nodes that were expanded during the search process.
- **Execution Time**: The time taken to find the solution, measured in seconds.
These metrics are printed at the end of each algorithm's execution, providing insights into the efficiency and effectiveness of the search strategies.
## Contributors
- **Reddy Balaji .C** (BITS ID: 2023AC05862; Email: 2023ac05862@wilp.bits-pilani.ac.in)
- **Saurabh Jalendra** (BITS ID: 2023AC05912; Email: 2023ac05912@wilp.bits-pilani.ac.in)
- **Tushar Shandilya** (BITS ID: 2023AC05573; Email: 2023ac05573@wilp.bits-pilani.ac.in)
- **Bandana Kumari** (BITS ID: 2023AC05879; Email: 2023ac05879@wilp.bits-pilani.ac.in)
- **Monica Malik** (BITS ID: 2023AC05875; Email: 2023ac05875@wilp.bits-pilani.ac.in)
## Acknowledgments
- Special thanks to the instructors and peers for their support and guidance throughout this project.
- Acknowledgment to the authors of the libraries and resources utilized in this project.
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Feel free to modify any sections to better fit your project's specifics or to add any additional information that may be relevant! This README aims to provide a comprehensive understanding of the project, making it easier for users to navigate and utilize the repository effectively.