• Arduino Uno/Nano - Main servo controller
• Raspberry Pi 4 - High-level control and vision processing
• Adafruit PCA9685 16-Channel PWM Driver - Servo control board
• 5x MG90S Servo Motors - Arm joints and gripper
• USB Camera - Vision system
• Power Supply - 5V/6V for servos (minimum 3A)
• Jumper Wires - Connections
• Breadboard or PCB - Circuit assembly

• External Power Supply for servos (recommended for stable operation)
• Level Shifter (if using 3.3V Raspberry Pi with 5V Arduino)
• Heat Sinks for servos under heavy load

Arduino Uno    →    PCA9685
GND           →    GND
5V            →    VCC
A4 (SDA)      →    SDA
A5 (SCL)      →    SCL

Servo Function    →    PCA9685 Channel
Base Rotation     →    Channel 0
Shoulder Joint    →    Channel 1
Elbow Joint       →    Channel 2
Wrist Rotation    →    Channel 3
Gripper           →    Channel 4

• Connect servo power (5V/6V) to PCA9685 V+ terminal
• Ensure adequate current supply (each MG90S draws ~200mA under load)
• Use separate power supply for servos if drawing >500mA total

Raspberry Pi    →    Arduino
GND            →    GND
GPIO 14 (TXD)  →    Pin 0 (RX)
GPIO 15 (RXD)  →    Pin 1 (TX)

Or use USB connection for serial communication

1. Install Arduino IDE
2. Install Adafruit PWM Servo Driver Library:

   Sketch → Include Library → Manage Libraries
   Search: "Adafruit PWM Servo Driver"
   Install the library by Adafruit
   

3. Upload the Arduino servo control code

1. Enable Serial Communication:

   sudo raspi-config
   # Navigate to Interface Options → Serial Port
   # Enable serial port hardware: Yes
   # Enable serial console: No

2. Install Python Dependencies:

   sudo apt update
   sudo apt install python3-pip python3-opencv
   pip3 install pyserial numpy opencv-python

3. Set up project directory:

   mkdir ~/robotic_arm
   cd ~/robotic_arm
   # Copy the Python files here

1. Base Servo (Channel 0): Mount horizontally for base rotation
2. Shoulder Servo (Channel 1): Mount vertically for up/down movement
3. Elbow Servo (Channel 2): Connect to shoulder arm for bending
4. Wrist Servo (Channel 3): Mount for end-effector rotation
5. Gripper Servo (Channel 4): Control gripper open/close mechanism

• Use servo horns and brackets for secure mounting
• Ensure adequate clearance between moving parts
• Balance the arm to reduce servo load
• Consider gear reduction for heavier loads

// Test individual servo movement
// Upload Arduino code and use serial monitor
// Send commands: M0090, M1090, etc.
// Verify each servo moves to correct position

# Run the vision system
python3 opencv_vision_system.py
# Choose option 3 for camera calibration
# Use printed chessboard pattern

• Define physical working area boundaries
• Calibrate pixel-to-world coordinate conversion
• Test pick and place accuracy

1. Start Arduino: Upload and run servo control code
2. Start Raspberry Pi Controller:

   python3 raspberry_pi_controller.py

3. Start Vision System:

   python3 opencv_vision_system.py

H    - Home position
P    - Pick position  
D    - Drop position
G1   - Open gripper
G0   - Close gripper
M0090 - Move servo 0 to 90 degrees

1. Place colored objects in camera view
2. Start autonomous mode in vision system
3. System will automatically detect and pick objects

Servos not moving:

• Check power supply voltage and current
• Verify PCA9685 connections
• Test with basic servo sweep code

Erratic servo movement:

• Insufficient power supply
• Loose connections
• Interference from nearby electronics

Camera not detected:

• Check USB connection
• Verify camera device ID (usually 0 or 1)
• Test with: ls /dev/video*

Serial communication issues:

• Check baud rate matches (9600)
• Verify RX/TX connections
• Test with simple echo program

Poor object detection:

• Adjust lighting conditions
• Tune color detection ranges
• Calibrate camera properly

# Test camera
python3 -c "import cv2; print(cv2.VideoCapture(0).read())"

# Test serial connection
python3 -c "import serial; s=serial.Serial('/dev/ttyUSB0', 9600); print('Connected')"

# Monitor Arduino output
screen /dev/ttyUSB0 9600

• Use external power supply for consistent performance
• Add capacitors to reduce electrical noise
• Implement smooth acceleration/deceleration curves

• Optimize camera resolution vs. processing speed
• Use multi-threading for real-time processing
• Implement predictive tracking algorithms

• Use separate threads for vision and control
• Implement error handling and recovery
• Add safety limits and emergency stops

• Use appropriate fuses and circuit protection
• Ensure proper grounding
• Avoid short circuits in power connections

• Implement software position limits
• Add physical end stops if needed
• Emergency stop functionality
• Gradual speed ramping to prevent damage

• Clear working area of obstacles
• Supervised operation during testing
• Proper mounting and stability

• Add force sensors for grip feedback
• Implement encoders for position feedback
• Upgrade to higher torque servos
• Add more degrees of freedom

• Machine learning for better object recognition
• Voice control integration
• Web interface for remote operation
• Path planning algorithms
• Multiple object sorting capabilities

robotic_arm/
├── arduino_servo_control.ino      # Arduino code
├── raspberry_pi_controller.py     # Raspberry Pi interface
├── opencv_vision_system.py        # Vision processing
├── camera_calibration.json        # Camera calibration data
├── config/
│   ├── servo_limits.json         # Servo position limits
│   └── vision_config.json        # Vision system settings
└── docs/
    ├── hardware_setup.md          # This guide
    └── api_reference.md           # Code documentation

This setup provides a complete foundation for a vision-guided robotic arm capable of autonomous object manipulation. Start with basic manual control, then gradually integrate the vision system for autonomous operation.