🤖 AlgoRun Robot 2.0

Autonomous micromouse with floodfill algorithm and speed optimization

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🚀 Features

• Floodfill maze mapping with intelligent exploration
• Persistent memory (ESP32 NVRAM) for instant speed runs
• Advanced motion: Curve turns, diagonal paths, PID control
• Webots simulation for algorithm validation before deployment
• HC-12 wireless module for debugging and data transmission

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🔩 Hardware

Sensors:

• 5× VL53L0X TOF (Front, Left, Right, 45°L, 45°R)
• 2× Quadrature Encoders

Specs:

• ESP32 Dual-Core | 58:1 Gear Motors
• Max: 14.5×14.5cm | <24V | 43mm wheels
• HC-12 wireless module (433MHz)

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📁 Repository Structure

algorun-robot-2.0/
├── Algorun_main_3_0_full/
│   └── Algorun_main_3_0_full.ino    # ESP32 firmware (C++)
│
└── Webots/
    ├── controllers/N03/
    │   ├── N03.py                    # Python simulation
    │   └── maze_data.json            # Exported maze data
    └── path/
        └── main maze.obj             # 16×16 Blender maze model

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🛠️ Setup

ESP32 Firmware

Required Arduino Libraries:

Wire.h           // I2C communication
VL53L0X.h        // Pololu TOF sensor library
Preferences.h    // ESP32 NVRAM storage

Upload:

git clone https://github.com/SkyLark-19/algorun-robot-2.0.git
cd algorun-robot-2.0
# Open Algorun_main_3_0_full/Algorun_main_3_0_full.ino in Arduino IDE
# Board: ESP32 Dev Module | Upload Speed: 921600
# Upload to ESP32

Webots Simulation

Requirements:

• Webots R2023b or later
• Python 3.8+

Run Simulation:

cd Webots/
# Open Webots and load the world file
# In Robot window: Controller → Select "N03"
# Press ► (Play) to start simulation

The Python controller will automatically explore the maze and output results to console.

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🎯 Algorithm Overview

Floodfill Strategy:

RUN 1: Explore to center goal              
RUN 2: Return to start (explore all cells) 
RUN 3: Fast run using known maze           
RUN 4: Ultra-optimized (curves + diagonals)

Interface:

• Button 1 : First exploration mode
• Button 2 : Fast run with saved maze
• Button 3 : Ultra-optimized speed run
  
  
• Green LED : Success/Setup complete
• Red LED : Wall detection/Stop
• Blue LED : Mode 4 activated

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🔄 Development Workflow

1. Simulate algorithm in Webots (Python)
2. Port to ESP32 firmware (C++)
3. Calibrate sensors + PID parameters
4. Test on physical maze
5. Optimize speed runs

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Enjoy solving mazes autonomously!