RAHBAR (رہبر) – Dynamic Path-Finding Agent

RAHBAR is an interactive Python-based visualization tool designed to demonstrate and compare pathfinding algorithms in both static and dynamic environments.

It allows users to visualize how different search strategies navigate a 2D grid while adapting to real-time environmental changes.

FEATURES

Algorithm Selection • Toggle between:

A* (Optimal Search)

Greedy Best-First Search (GBFS)

Dual Heuristics • Manhattan Distance • Euclidean Distance

Dynamic Environments • A Dynamic Mode that spawns obstacles in real-time. • The agent automatically re-calculates its path if blocked.

Interactive Grid • Draw or erase walls using your mouse. • Create custom mazes and test cases.

Performance Metrics • Real-time tracking of:

Visited nodes

Path cost

Execution time (in milliseconds)

INSTALLATION

Prerequisites • Python 3.10 or higher • pygame library

Clone the Repository

git clone https://github.com/Mzt00/Dynamic-Path-Finding-Agent.git

cd Dynamic-Path-Finding-Agent

Install Dependencies

pip install pygame

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How to Use

Run the application:

python main.py

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Controls

Input	Action
Mouse Left Click	Toggle walls on the grid
Space Bar	Start the search and initiate agent movement
R Key	Randomize map obstacles
D Key	Toggle Dynamic Mode
1 / 2 Keys	Switch between A* (1) and GBFS (2)
M / E Keys	Switch between Manhattan (M) and Euclidean (E) heuristics
C Key	Clear the grid and reset the agent

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Technical Architecture

The project is structured into modular components for clarity and scalability.

Core Logic

grid.py

• Manages grid state
• Handles wall placement
• Validates neighbors

agent.py

• Handles agent position
• Controls movement
• Stores path information

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Search Strategies

a_star.py

Implements:

f(n) = g(n) + h(n)

• Guarantees the shortest path.

gbfs.py

Implements:

f(n) = h(n)

• Faster but may produce sub-optimal paths.

heuristics.py

• Contains Manhattan and Euclidean distance implementations.

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GUI and Visualization

renderer.py

• Manages the Pygame window
• Draws the grid
• Renders the control panel

main.py

• Entry point of the application
• Handles event loop
• Manages user interactions

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Utilities

priority_queue.py

• A wrapper around Python’s heapq
• Manages node expansion based on priority

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Implementation Details

A* Implementation

• Uses a priority queue
• Expands nodes based on:

f(n) = g(n) + h(n)

• Supports 8-way movement
• Diagonal movement cost is weighted at 1.41 for realistic distance calculation

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Dynamic Re-Routing

When Dynamic Mode is active:

1. The system checks if a newly spawned obstacle intersects the agent’s current path.

2. If blocked:

   • The agent immediately triggers a new search
   • The search starts from the agent’s current position
   • The original goal remains unchanged

This enables real-time adaptive navigation in changing environments.

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Purpose

RAHBAR is designed for:

• Algorithm visualization
• AI and search strategy comparison
• Academic demonstrations
• Understanding dynamic path re-planning

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Built With

• Python
• Pygame