Hexmind 🕷️

An advanced, AI-powered Hexapod robotics project integrating custom YOLO vision models and Arduino-based kinematics.

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📸 The Hexapod

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🚀 Project Overview

Hexmind combines the mechanical agility of a hexapod with the intelligence of modern computer vision. The system is split into two primary components:

1. AI Brain (hexapod_ai/):

   • A Python-based intelligence module running custom YOLOv8 object detection.
   • Depth estimation and real-time processing capabilities (depth_estimator.py, hexapod_brain.py).
   • Custom training pipelines and dashboard GUI for monitoring.

2. Hardware Control (Hexapod_Code/):

   • Arduino (.ino) scripts handling the low-level transmitter and receiver logic.
   • Precise multi-servo coordination to translate AI commands into physical locomotion.

📁 Repository Structure

Hexamind/
├── Hexapod_Code/            # Arduino sketches for Tx/Rx communication
│   ├── hexapod_receiver.ino
│   └── hexapod_transmitter.ino
├── hexapod_ai/              # Python vision and brain scripts
│   ├── hexapod_brain.py     # Main AI logic controller
│   ├── depth_estimator.py   # Monocular depth estimation
│   ├── gui_dashboard.py     # User interface for monitoring
│   └── train_custom.py      # Custom model training scripts
└── README.md

🛠️ Getting Started

Prerequisites

• Hardware: Arduino microcontrollers, servo controllers, and a hexapod chassis.
• Software: Python 3.8+, Arduino IDE.

AI Setup

Navigate to the hexapod_ai directory and install the necessary requirements:

cd hexapod_ai
pip install -r requirements.txt

Microcontroller Setup

Flash the hexapod_receiver.ino to the hexapod's onboard controller and hexapod_transmitter.ino to your remote control module using the Arduino IDE.

🧠 Model Training

The hexapod_ai module supports training custom YOLOv8 models. Place your datasets inside the hexapod_ai/dataset/ directory (these are excluded from Git to save space) and run the train_custom.py script.

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Created by Divyanshu-2907