Building an Autonomous Drone with Raspberry Pi & Pixhawk

⚙️ Core Technical Implementation

1. Hardware Integration

Created a robust communication pipeline between components:

  • RPi 4B (Mission Control):
  • GPIO 14/15 UART @57600 baud
  • Pixhawk (Flight Controller):

💡 Key Insight: Direct GPIO UART outperforms any other sort of communication, ensuring lower latency and more reliable control.

2. Software Stack

Developed a custom autonomy pipeline using DroneKit Python:

# DroneKit Waypoint Script
from dronekit import connect, Command
from pymavlink import mavutil

# Connect to the vehicle
vehicle = connect('/dev/ttyAMA0', wait_ready=True)

# Define a waypoint command
cmd = Command(0, 0, 0, mavutil.mavlink.MAV_FRAME_GLOBAL_RELATIVE_ALT,
              mavutil.mavlink.MAV_CMD_NAV_WAYPOINT, 0, 0, 0, 0, 0, 0,
              lat, lon, alt)

# Add command to mission
cmds = vehicle.commands
cmds.add(cmd)
cmds.upload()

This script demonstrates how waypoints are added and uploaded to the Pixhawk for guided missions.

Key Insight: Leveraging DroneKit and MAVProxy enables robust mission control while maintaining full programmability for future enhancements

3. 3D-Printed Mount

To integrate the Raspberry Pi into the drone’s frame, a custom 3D-printed mount was designed:

  • Material: PLA for its lightweight properties and durability.
  • Design: A snug fit to ensure stability and minimize vibrations that could affect communication.
  • Iteration Process: Two prototypes were created, adjusting for secure attachment and optimal placement of the Raspberry Pi.

Key Insight: The use of a well-designed mount reduced noise from vibrations, improved connection reliability, and ensured the Raspberry Pi remained securely in place during test flights.

4. Demo Run and Load Testing

The drone was put through a rigorous set of tests to evaluate its real-world performance:

  • Waypoint Mission Test: A pre-defined mission was uploaded to the Pixhawk, and the drone executed it flawlessly, with waypoint accuracy recorded at ±2 meters.
  • Load Testing: The drone was progressively loaded with weights to determine its payload capacity while maintaining stability.

Results:

  • Maximum payload: 500g without affecting stability.
  • Mission execution: Smooth and within expected precision thresholds, even under load.

Key Insight: Load testing validated the drone’s structural and operational integrity, proving its readiness for further enhancements.

Future Plans

This project marks the beginning of an exciting journey into autonomous drones. The next phases will focus on extending functionality and improving performance:

  1. AI-Powered Vision: Integrate computer vision for tasks like object detection and mapping, enabling the drone to autonomously identify and track litter.
  2. Obstacle Avoidance: Develop and integrate obstacle detection algorithms to enhance flight safety in complex environments.
  3. Enhanced Payload Options: Upgrade the drone to carry additional sensors and payloads, expanding its utility for various applications.

Vision for the Future: The ultimate goal is to create a modular and scalable drone platform that can be adapted for environmental monitoring, delivery services, and research applications.

Final Thoughts

This project demonstrates how accessible hardware and open-source tools can be combined to create powerful autonomous systems. From robust hardware integration to efficient software pipelines, every component plays a critical role in achieving seamless performance.

The lessons learned from this project pave the way for more advanced iterations, pushing the boundaries of what DIY drones can achieve.