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Software Installation Guide

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Overview

This guide covers the installation and configuration of all software components for the AI drone system.

Prerequisites

Assembled drone hardware
Raspberry Pi Zero 2 WH with SD card (16GB+ recommended)
Computer with SD card reader
Internet connection
USB keyboard and mouse (for initial Pi setup)

Operating System Setup

1. Install Raspbian OS

Download Raspberry Pi Imager:

# On macOS
brew install --cask raspberry-pi-imager

# On Linux
sudo apt install rpi-imager

Flash SD Card:

Insert SD card into computer
Launch Raspberry Pi Imager
Choose “Raspberry Pi OS Lite (64-bit)”
Select your SD card
Click “Write”

Enable SSH (Optional):

# After flashing, mount the boot partition
touch /Volumes/boot/ssh

Configure WiFi (Optional): Create wpa_supplicant.conf in boot partition:

country=DE
ctrl_interface=DIR=/var/run/wpa_supplicant GROUP=netdev
update_config=1

network={
    ssid="YOUR_WIFI_SSID"
    psk="YOUR_WIFI_PASSWORD"
    key_mgmt=WPA-PSK
}

2. First Boot Configuration

# SSH into Pi (if connected via network)
ssh pi@raspberrypi.local
# Default password: raspberry

# Update system
sudo apt update
sudo apt upgrade -y

# Configure system
sudo raspi-config
# - Change password
# - Set hostname to "ai-drone"
# - Enable Camera interface
# - Expand filesystem
# - Reboot

Python Environment Setup

Install Python Dependencies

# Install Python 3 and pip
sudo apt install python3 python3-pip python3-venv -y

# Install system dependencies
sudo apt install -y \
    git \
    cmake \
    libatlas-base-dev \
    libopencv-dev \
    python3-opencv \
    libhdf5-dev \
    libhdf5-serial-dev \
    libjpeg-dev \
    libpng-dev \
    libtiff-dev \
    libavcodec-dev \
    libavformat-dev \
    libswscale-dev \
    libv4l-dev \
    libxvidcore-dev \
    libx264-dev

# Create virtual environment
python3 -m venv ~/drone-env
source ~/drone-env/bin/activate

# Upgrade pip
pip install --upgrade pip

Install AI Framework

# Install TensorFlow Lite
pip install tflite-runtime

# Install supporting libraries
pip install numpy opencv-python pillow

# For Google Coral
echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | \
sudo tee /etc/apt/sources.list.d/coral-edgetpu.list
curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -
sudo apt update
sudo apt install libedgetpu1-std python3-pycoral

Flight Controller Software

INAV Configuration

Install INAV Configurator on Computer:

Download from github.com/iNavFlight/inav-configurator
Connect flight controller via USB
Flash latest INAV firmware

Basic Configuration:

1. Setup Tab:
   - Select quadcopter/fixed-wing configuration
   - Set correct board alignment

2. Ports Tab:
   - Enable MSP on UART for Raspberry Pi connection
   - Enable Serial RX on appropriate UART
   - Enable GPS if using

3. Configuration Tab:
   - Set ESC/Motor protocol
   - Configure receiver (ELRS, CRSF, etc.)
   - Enable features: GPS, OSD, TELEMETRY

4. PID Tuning:
   - Load default PID profile
   - Adjust based on drone weight and flight characteristics

5. Receiver Tab:
   - Configure channel mapping
   - Set failsafe values
   - Map AUX8 for camera recording trigger

6. Modes Tab:
   - Configure ARM switch
   - Set flight modes (Angle, Horizon, Acro)
   - Optional: Configure autonomous modes (RTH, WP, etc.)

7. Motors Tab:
   - Test motor direction (props OFF!)
   - Verify motor order: 1-Front Left, 2-Front Right,
                         3-Back Right, 4-Back Left

Note: INAV is used for this project due to its superior autonomous flight and GPS navigation capabilities, which are essential for systematic road inspection missions.

ArduPilot Installation (Alternative)

# For autonomous flight capabilities
# Install on companion computer (Raspberry Pi)
cd ~
git clone https://github.com/ArduPilot/ardupilot.git
cd ardupilot
git submodule update --init --recursive

# Install MAVProxy (Ground Station Software)
sudo apt install python3-dev python3-opencv python3-wxgtk4.0 \
                 python3-pip python3-matplotlib python3-lxml python3-pygame
pip3 install MAVProxy

Camera Configuration

Test Camera Module

# Enable camera
sudo raspi-config
# Interface Options -> Camera -> Enable

# Test camera
raspistill -o test.jpg
raspivid -o test.h264 -t 10000

# Install picamera library
pip install picamera

Video Streaming Setup

# Install streaming software
sudo apt install -y gstreamer1.0-tools \
                    gstreamer1.0-plugins-good \
                    gstreamer1.0-plugins-bad

# Test video stream
raspivid -t 0 -w 1280 -h 720 -fps 30 -o - | \
gst-launch-1.0 -v fdsrc ! h264parse ! rtph264pay \
config-interval=1 pt=96 ! udpsink host=192.168.1.100 port=5000

AI Application Setup

Install YOLO for Object Detection

# Clone YOLO repository
cd ~/
git clone https://github.com/ultralytics/yolov5
cd yolov5
pip install -r requirements.txt

# Download pre-trained model
wget https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.pt

# Test detection
python detect.py --source 0  # Use webcam

Convert Model for Edge TPU

# Install Edge TPU Compiler
curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -
echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | \
sudo tee /etc/apt/sources.list.d/coral-edgetpu.list
sudo apt update
sudo apt install edgetpu-compiler

# Convert TensorFlow model to TFLite
# (Conversion steps depend on your specific model)
edgetpu_compiler model.tflite

Ground Control Station

Install QGroundControl (on Computer)

macOS:

brew install --cask qgroundcontrol

Linux:

sudo usermod -a -G dialout $USER
sudo apt-get remove modemmanager -y
wget https://d176tv9ibo4jno.cloudfront.net/latest/QGroundControl.AppImage
chmod +x QGroundControl.AppImage
./QGroundControl.AppImage

Windows: Download from qgroundcontrol.com

Testing Installation

System Test Script

# test_system.py
import sys
import cv2
import numpy as np

def test_camera():
    """Test camera functionality"""
    try:
        cap = cv2.VideoCapture(0)
        ret, frame = cap.read()
        cap.release()
        if ret:
            print("✓ Camera: OK")
            return True
        else:
            print("✗ Camera: FAILED")
            return False
    except Exception as e:
        print(f"✗ Camera: ERROR - {e}")
        return False

def test_coral():
    """Test Google Coral accelerator"""
    try:
        from pycoral.utils import edgetpu
        interpreters = edgetpu.list_edge_tpus()
        if interpreters:
            print(f"✓ Coral: OK - {len(interpreters)} device(s) found")
            return True
        else:
            print("✗ Coral: No devices found")
            return False
    except Exception as e:
        print(f"✗ Coral: ERROR - {e}")
        return False

def test_opencv():
    """Test OpenCV installation"""
    try:
        print(f"✓ OpenCV: OK - Version {cv2.__version__}")
        return True
    except Exception as e:
        print(f"✗ OpenCV: ERROR - {e}")
        return False

def main():
    print("=== AI Drone System Test ===\n")
    results = []

    results.append(test_opencv())
    results.append(test_camera())
    results.append(test_coral())

    print("\n" + "="*30)
    if all(results):
        print("All tests passed! ✓")
        sys.exit(0)
    else:
        print("Some tests failed! ✗")
        sys.exit(1)

if __name__ == "__main__":
    main()

Run the test:

python3 test_system.py

Next Steps

Camera Control System - Learn about the integrated recording & AI system
Configure Flight Controller
Set up AI Applications
Begin Training AI Models

Troubleshooting

Common Issues

Camera not detected:

# Check camera connection
vcgencmd get_camera
# Should show: supported=1 detected=1

# Enable camera interface
sudo raspi-config

Coral not detected:

# Check USB connection
lsusb | grep "Google"

# Reinstall driver
sudo apt install --reinstall libedgetpu1-std

Permission errors:

# Add user to video group
sudo usermod -a -G video $USER

# Add user to dialout group (for serial)
sudo usermod -a -G dialout $USER

# Reboot
sudo reboot

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AI Applications →