Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/iammohith/grassfire-algorithm

Grassfire Pathfinding Algorithm Simulation: This project visualizes the Grassfire algorithm for pathfinding in a grid environment. It includes features for displaying the grid, obstacle placement, distance calculations, and the shortest path simulation. Users can interact with the grid and observe the robot's movement toward the goal.
https://github.com/iammohith/grassfire-algorithm

matlab mobilerobots path-planning robotics

Last synced: about 1 month ago
JSON representation

Grassfire Pathfinding Algorithm Simulation: This project visualizes the Grassfire algorithm for pathfinding in a grid environment. It includes features for displaying the grid, obstacle placement, distance calculations, and the shortest path simulation. Users can interact with the grid and observe the robot's movement toward the goal.

Awesome Lists containing this project

README 

        
# Grassfire Pathfinding Algorithm Simulation



This project implements the Grassfire Algorithm for pathfinding in a grid-based environment, simulating the movement of a robot from a start cell to a goal cell while avoiding obstacles. The simulation visualizes the grid, the distances calculated by the Grassfire algorithm, and the shortest path found.



## Table of Contents

- [Introduction](#introduction)

- [Features](#features)

- [Requirements](#requirements)

- [Usage](#usage)

  - [Parameters](#parameters)

  - [Example](#example)

- [File Structure](#file-structure)

- [Results](#results)

  - [Calculated Distances from Grassfire Algorithm](#calculated-distances-from-grassfire-algorithm)

  - [Shortest Path](#shortest-path)

  - [Shortest Path Simulation](#shortest-path-simulation)

- [License](#license)

- [Acknowledgments](#acknowledgments)

- [References](#references)



## Introduction

This project implements a robot pathfinding simulation using a grid-based approach. It utilizes the Grassfire algorithm to compute distances from a goal cell and visualize the shortest path from a specified start cell to the goal while avoiding obstacles.



## Features

- **Grid Visualization**: Displays a grid with start, goal, and obstacle cells.

- **Distance Calculation**: Implements the Grassfire algorithm to calculate distances from the goal to each cell.

- **Shortest Path Simulation**: Visualizes the robot's movement along the shortest path to the goal.

- **Robot Representation**: The robot is represented as a blue rectangle with wheels and an orange top mount.

- **MATLAB Graphics**: Utilizes MATLAB’s graphical capabilities to create an interactive simulation experience.



## Requirements

- MATLAB (preferably R2018b or later)



## Usage

Clone this repository and run the `start_simulation.m` file, providing the grid dimensions, start cell, goal cell, and obstacles as input parameters.



### Parameters

To run the simulation, call the `start_simulation` function with the appropriate parameters:



```matlab

start_simulation(m, n, startCell, goalCell, obstacles)

```



- `m`: Number of rows in the grid.

- `n`: Number of columns in the grid.

- `startCell`: Linear index of the start cell.

- `goalCell`: Linear index of the goal cell.

- `obstacles`: Array of linear indices representing obstacle cells.



### Example

![Problem Statement](Display_Grid.png)

```matlab

m = 5; % Number of rows

n = 5; % Number of columns

startCell = 1; % Start cell index

goalCell = 13; % Goal cell index

obstacles = [7, 8, 12, 14]; % Obstacle cells



start_simulation(m, n, startCell, goalCell, obstacles);

```



## File Structure

The project consists of the following MATLAB functions:



- **`start_simulation.m`**: The main function that initiates the simulation, calling other functions to display the grid, run the Grassfire algorithm, and visualize the robot's path.



- **`display_grid.m`**: Displays the grid with the start cell (in green), goal cell (in red), and obstacles (in black). It ensures proper visualization by dividing the grid into rows and columns.



- **`grassfire_algorithm.m`**: Implements the Grassfire algorithm to calculate the distance of each cell from the goal while considering obstacles. Obstacles are marked as impassable, and distances propagate to the rest of the grid.



- **`display_distances.m`**: Displays the calculated distances from the Grassfire algorithm on the grid.



- **`shortest_path.m`**: Visualizes the shortest path from the start cell to the goal cell based on the Grassfire algorithm's output. It plots the path step-by-step, avoiding obstacles.



- **`draw_robot.m`**: Draws the robot's representation on the grid. The robot features a blue rectangular body, black wheels, and an orange circular mount to distinguish it visually.



- **`index_to_rowcol.m`**: Converts a linear cell index to its corresponding row and column indices in a grid with dimensions `m x n`. Unlike MATLAB's default `ind2sub`, which assumes column-major order, this function works for **row-major order** (left-to-right row-wise).



## Results



### Calculated Distances from Grassfire Algorithm

![Distance Matrix from Grassfire Algorithm](Distances_from_Grassfire_Algorithm.png)



### Shortest Path

![Shortest Path](Shortest_Path.png)



### Shortest Path Simulation

![Shortest Path Simulation](Shortest_Path_Simulation.gif)



## License

This project is licensed under the MIT License. See the LICENSE file for details.



## Acknowledgments

- Inspired by algorithms for pathfinding and robotics.



## References

1. **Grassfire Algorithm**:

   - H. Edelsbrunner, "Algorithms in Combinatorial Geometry," Springer-Verlag, 1987.

   - [Wikipedia: Grassfire algorithm](https://en.wikipedia.org/wiki/Grassfire_transform)



2. **Pathfinding Algorithms**:

   - R. Hart, N. Nilsson, and B. Raphael, "A Formal Basis for the Heuristic Determination of Minimum Cost Paths," *IEEE Transactions on Systems Science and Cybernetics*, vol. 4, no. 2, pp. 100-107, 1968.



3. **Mobile Robots**:

   - B. Siciliano et al., *Springer Handbook of Robotics*, 2nd ed. Springer, 2016.

   - R. Siegwart, I. R. Nourbakhsh, and D. Scaramuzza, *Introduction to Autonomous Mobile Robots*, 2nd ed. MIT Press, 2011.



4. **MATLAB Graphics**:

   - MATLAB Documentation: [Graphics](https://www.mathworks.com/help/matlab/graphics.html)