Dynamic Pathfinding Algorithms

A collection of pathfinding algorithms including A*, BFS, DFS, Dijkstra, and Greedy Best-First Search, with support for dynamic obstacles.

Table of Contents

1. Introduction
2. Features
3. Installation
4. Usage

Introduction

This project is a simulation of pathfinding algorithms on a grid with static and dynamic obstacles. The algorithms implemented include A*, BFS, DFS, Dijkstra, and Greedy Best-First Search. The dynamic obstacles can move within the grid, providing a real-time challenge for the algorithms to adapt and find the path.

Features

• A Algorithm*: Efficient pathfinding algorithm that uses a heuristic.
• Breadth-First Search (BFS): Finds the shortest path without considering the heuristic.
• Depth-First Search (DFS): Explores as far as possible along each branch before backtracking.
• Dijkstra's Algorithm: Similar to A*, but considers all possible paths.
• Greedy Best-First Search: Utilizes heuristics to make decisions.
• Dynamic Obstacles: Obstacles that can move within the grid, creating a dynamic environment.
• Visualization Tools: Visualize the grid, paths, and dynamic obstacles using matplotlib.

Installation

1. Clone the repository:

   git clone https://github.com/tellsiddh/dynamic-path-planning.git
   

2. Install required dependencies:

   pip install matplotlib numpy
   

Usage

Modify the grid, start, and end variables as desired.

Run the main script:

python pathfinding.py

Visualizations and statistics about the paths found will be displayed in the terminal and graphical plots.