Warehouse Mobile Robots Navigation Solutions

Welcome to the Warehouse Mobile Robots Navigation Solutions repository! This repository contains a collection of Python scripts designed to solve various problems related to robot movements and pathfinding within a warehouse environment. Each script addresses a unique challenge, demonstrating specific aspects of autonomous navigation, collision handling, and optimal path planning for mobile robots in a grid-like warehouse setup.

Table of Contents

• Overview
• Files Description
• Getting Started
• Usage
• Contributing
• License

Overview

In modern warehouses, mobile robots are integral to automating tasks such as picking, packing, and transporting goods. Efficient navigation and optimal path planning are crucial for ensuring high productivity and minimizing operational costs. This repository provides solutions to several common problems faced by robots in warehouse environments, including pathfinding, collision avoidance, and ensuring efficient navigation. Each script is crafted to tackle a specific problem, making it easier to comprehend and adapt to real-world applications.

Files Description

Here is a brief description of each file in this repository:

1. Minimum_Cost_Homecoming_of_a_Robot_in_a_Grid.py

   • This script calculates the minimum cost for a robot to return to its home position in a warehouse grid, considering obstacles and varying movement costs. It focuses on optimal path planning to minimize operational costs.

2. Movement_Of_Robots.py

   • This script simulates the movement of multiple robots within a warehouse grid, tracking their positions and ensuring efficient navigation. It highlights the importance of coordinated movement and traffic management in a busy warehouse.

3. Robot_Bounded_In_Circle.py

   • This script determines whether a robot, after executing a series of predefined movements, remains bounded within a circular area in the warehouse grid. This is crucial for ensuring that robots do not stray from their designated areas.

4. Robot_Collisions.py

   • This script simulates multiple robots moving within a warehouse grid and detects potential collisions between them. It enables the implementation of collision avoidance strategies, which are vital for maintaining a smooth operation.

5. Robot_Return_To_Origin.py

   • This script checks if a robot returns to its origin point after executing a series of movements, ensuring it completes its tasks and returns to the starting position. This is essential for task completion and battery recharging cycles.

6. Walking_Robot_Simulation.py

   • This script simulates the movement of a walking robot in a warehouse grid, adhering to specific movement rules to navigate efficiently. It demonstrates the application of movement constraints in a structured environment.

7. Walking_Robot_Simulation_2.py

   • This script is a variant of the walking robot simulation with additional or modified rules to further optimize the robot's navigation within the warehouse grid. It focuses on improving navigation strategies for complex environments.

Getting Started

To get started with these scripts, you will need to have Python installed on your system. You can download Python from the official website.

Clone this repository to your local machine using the following command:

git clone https://github.com/Fouad-Smaoui/Robotics-Solutions.git
cd Robotics-Solutions

Usage

Each script can be executed independently. Navigate to the directory containing the script you wish to run and execute it using Python. For example, to run the Minimum_Cost_Homecoming_of_a_Robot_in_a_Grid.py script, use the following command:

python Minimum_Cost_Homecoming_of_a_Robot_in_a_Grid.py

Ensure you have the necessary dependencies installed (if any). You can use pip to install any required packages.

Contributing

Contributions are welcome! If you have suggestions for improvements, new features, or additional challenges, please open an issue or create a pull request. Follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add some feature').
5. Push to the branch (git push origin feature-branch).
6. Open a pull request.

License

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

---

Feel free to reach out if you have any questions or need further assistance. Happy coding!

---