Snake-Robot-Simulation🐍🤖

Snake-Robot-Simulation: Bringing Agility and Vision to Disaster Response

Introduction👋

This project explores the development of a snake robot prototype for potential applications in disaster response. The focus lies on its motion control and image classification capabilities, laying the groundwork for future advancements in autonomous navigation and casualty detection.

Project Philosophy🎓

This project supports the fusion of robotics and computer vision to create efficient and adaptable solutions for emergencies. With their inherent flexibility and manoeuvrability, we believe that snake robots hold significant potential for navigating disaster-stricken environments and aiding search and rescue efforts.

Tech Stack💻

Summary🕐

• Manual Motion Control: The current prototype utilizes user-controlled joint angles for movement, simulating the agility of natural snakes.
• Image Classification Integration: The robot analyzes captured images and video in real-time to identify potential casualties, enhancing its situational awareness using the Viola-Jones algorithm.
• Solid foundation for future advancements: The project is a stepping stone for developing autonomous control algorithms and robust image classification systems.

Documentation📄

The project report provides detailed information on:

• Hardware design considerations and process (SolidWorks)
• Software development process (MATLAB & Simulink)
• Image classification algorithms employed
• Simulation and Results

Getting Started🚀

To explore the project, please follow these steps:

1. Clone the repository: https://github.com/Gayanukaa/Snake-Robot-Simulation
2. Install required software: SolidWorks, MATLAB, Simulink
3. Requires additional packages Computer Vision Toolbox and MATLAB Support Package for USB Webcams
4. Review the documentation for detailed instructions

Contributing🤝

We welcome contributions from individuals and teams interested in further developing this project. Potential areas include:

• Enhancing the motion control algorithms for autonomous navigation
• Refining image classification algorithms for improved accuracy and robustness
• Contributing to hardware design optimization and sensor integration
• Sharing insights and feedback through discussions and pull requests

License📄

This project is licensed under the MIT License, allowing for open-source collaboration and modification. We encourage community involvement and hope this platform sparks further innovation in disaster response robotics.