Micro mouse prashant

🤖 Meshmerize MicroMouse Project

A comprehensive autonomous line-following and maze-solving robot project featuring multiple hardware iterations, advanced algorithms, and professional PCB designs.

🏆 Competition Achievements

Our MicroMouse has proven its excellence in prestigious robotics competitions:

• 🥇 1st Place - Locus, Pulchowk Campus
• 🥈 2nd Place - Delta 5.0, Dharan IOE
• 🏅 5th Place - Techfest, IIT Bombay 2024

📋 Table of Contents

• 🎯 Project Overview
• ✨ Key Features
• 🔧 Hardware Versions
• 💻 Software Architecture
• 🚀 Getting Started
• 📁 Repository Structure
• 🛠️ Installation & Setup
• 🎮 Usage Instructions
• 🧠 Algorithms & Control Systems
• 📷 Project Gallery
• 🤝 Contributing
• 📄 License

🎯 Project Overview

The Meshmerize MicroMouse is an autonomous robot designed to navigate line-following courses and solve complex mazes. This project showcases the evolution of robotics engineering through multiple hardware iterations, from Arduino Nano-based prototypes to advanced ESP32-powered systems.

What Makes This Project Special?

• Multiple Hardware Generations: Evolution from v1.0 to v3.1 with continuous improvements
• Dual Platform Support: Both Arduino Nano and ESP32 implementations
• Professional PCB Design: Custom-designed circuit boards using KiCad
• Advanced Algorithms: LSRB maze-solving with optimized PID control
• Competition-Tested: Proven performance in national and international competitions

✨ Key Features

🎯 Navigation Capabilities

• Line Following: Precise black/white line detection and following
• Maze Solving: Autonomous exploration and shortest path execution
• Multi-Surface Support: Adaptable to different track conditions

⚡ Performance Optimization

• PID Control System: Smooth and accurate movement control
• Speed Management: Adaptive speed control for different track sections
• Sensor Calibration: Advanced sensor tuning for reliable detection

🔧 Hardware Excellence

• Custom PCB Design: Professional circuit board layouts
• Modular Architecture: Easy maintenance and upgrades
• Power Management: Efficient voltage regulation for all components

🔧 Hardware Versions

💻 Software Architecture

Programming Platforms

• Arduino IDE: Traditional Arduino Nano development
• PlatformIO: Advanced ESP32 development with better library management

Key Libraries

#include <SparkFun_TB6612.h>  // Motor control library
#include <QTRSensors.h>       // Sensor array library

Algorithm Implementation

• LSRB Algorithm: Left-Straight-Right-Back priority maze solving
• PID Control: Proportional-Integral-Derivative control for smooth movement
• Path Optimization: Shortest path calculation and execution

🚀 Getting Started

Prerequisites

• Arduino IDE or PlatformIO
• USB cable for programming
• Basic electronics knowledge
• Robotics track or maze for testing

Quick Setup

1. Clone the repository

   git clone https://github.com/prashantbhandary/Meshmerize-MicroMouse-.git
   cd Meshmerize-MicroMouse-

2. Choose your platform

   • For Arduino Nano: Navigate to ArduinoNano/ folder
   • For ESP32: Navigate to Esp32/ folder

3. Select the appropriate version

   • Pick the version that matches your hardware setup
   • Latest versions recommended for best performance

📁 Repository Structure

📦 Meshmerize-MicroMouse-
├── 🔧 ArduinoNano/
│   ├── 🧪 arduno_nano_v1_TestingCode/
│   │   ├── arduino_sensor_test2.0/
│   │   ├── black_line_maze_2.0/
│   │   ├── motor-testing/
│   │   └── night_nano/
│   ├── 📋 micro_mouse_v1-files/
│   │   ├── codes/ (Line follower, maze solving, testing)
│   │   └── design/ (3D models, PCB files)
│   ├── 🚀 micro_mouse_v2-files/
│   │   ├── BlackLineMazeFinal_v2-Code/
│   │   ├── LineFollowerPid_v2-Code/
│   │   ├── WhiteLineMazeFinal_v2-Code/
│   │   └── micro_mouse_v2-pcb/
│   └── 🏆 micro_mouse_v3_files/
│       ├── micro_mouse_prashant/
│       ├── micro_mouse_v3.0-PCB/
│       └── micro_mouse_v3.1-PCB/
├── 📡 Esp32/
│   ├── LineFolloweEsp32_v1_code/
│   ├── micromouseEsp32_v1_PCB/
│   └── testing_esp32_v1_code/
├── 📸 IMG/

🛠️ Installation & Setup

For Arduino Nano Versions

1. Install Required Libraries

   - SparkFun TB6612 Motor Driver Library
   - QTRSensors Library

2. Hardware Setup

   • Connect QTR sensors to analog pins A0-A7
   • Wire motor drivers according to pin definitions
   • Connect control switches and LED indicators

3. Upload Code

   • Open the desired version in Arduino IDE
   • Select Arduino Nano board
   • Upload the code

For ESP32 Version

1. PlatformIO Setup

   cd Esp32/LineFolloweEsp32_v1_code/Micromouse/
   pio run --target upload

2. Hardware Configuration

   • Follow ESP32 pin definitions in the code
   • Ensure proper power supply (3.3V/5V compatibility)

🎮 Usage Instructions

Initial Setup

1. Calibration Phase

   • Press switch 1 to start sensor calibration
   • Move the robot over the line during calibration
   • LED indicator confirms successful calibration

2. Mode Selection

   • Switch 2: Left-hand rule (LHS) priority
   • Switch 3: Right-hand rule (RHS) priority

3. Start Operation

   • Press switch 1 again to begin line following/maze solving
   • Robot will autonomously navigate the course

Operating Modes

Line Following Mode

• Robot follows black line on white surface (or vice versa)
• PID control ensures smooth tracking
• Adaptive speed control for optimal performance

Maze Solving Mode

• Exploration Phase: Robot maps the entire maze
• Optimization Phase: Calculates shortest path
• Execution Phase: Runs the optimized route

🧠 Algorithms & Control Systems

LSRB Algorithm (Left-Straight-Right-Back)

// Priority decision making
if (left_path_available) {
    turn_left();
} else if (straight_path_available) {
    move_forward();
} else if (right_path_available) {
    turn_right();
} else {
    turn_back(); // Dead end
}

PID Control System

// PID calculation for smooth movement
error = sensor_position - desired_position;
derivative = error - lastError;
correction = (kp * error) + (kd * derivative);
motor_speed_adjustment = base_speed ± correction;

Key Parameters

• Base Speed: 130-255 (adjustable per version)
• Kp (Proportional): 0.05-0.151 (tuned per hardware)
• Kd (Derivative): 0.1-0.8 (optimized for stability)

📷 Project Gallery

🤖 Robot Images

Side Profile	Front View

Competition Ready Configuration

🎥 Demo Videos

▶️ Watch MicroMouse in Action
Live demonstration from IIT Bombay competition

▶️ Advanced Maze Solving Demo
Complex maze navigation showcase

🤝 Contributing

We welcome contributions to improve the MicroMouse project! Here's how you can help:

Ways to Contribute

• 🐛 Bug Reports: Found an issue? Let us know!
• 💡 Feature Requests: Suggest new capabilities
• 🔧 Code Improvements: Optimize algorithms or add new features
• 📚 Documentation: Help improve guides and tutorials
• 🔬 Testing: Try different hardware configurations

Development Setup

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Make your changes
4. Test thoroughly with hardware
5. Commit your changes (git commit -m 'Add AmazingFeature')
6. Push to the branch (git push origin feature/AmazingFeature)
7. Open a Pull Request

📞 Contact & Support

• Project Maintainer: Prashant Bhandary
• GitHub: @prashantbhandary
• Repository: Meshmerize-MicroMouse-
• Portfolio Website: 🌐 bhandari-prashant.com.np

Getting Help

• 📋 Issues: Use GitHub Issues for bug reports
• 💬 Discussions: Join repository discussions for general questions
• 📖 Wiki: Check the wiki for detailed guides (coming soon)

📄 License

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

What this means:

• ✅ Use in personal and commercial projects
• ✅ Modify and distribute freely
• ✅ Include in other projects
• 📋 Attribution required

Achievements:

1. Winner at Locus 2025, Pulchowk IOE.
2. 1st Runner-up at Delta 5.0 ERC, Dharan IOE.
3. Top 5 finish at Techfest, IIT Bombay 2024 amidst tough competition.

This project was a huge learning experience, teaching us valuable lessons in algorithm optimization, hardware design, and problem-solving.

Special Thanks:

• Teammates and Mentors: Aabiskar Regmi, Sanjog Sapkota, Siddartha Gupta

Can’t wait to tackle even bigger challenges next! 🚀