ARDUPILOT QUADCOPTER

I designed, assembled, and successfully flew a quadcopter powered by the Ardupilot open-source flight stack, integrating GPS-based waypoint navigation and real-time telemetry. The project involved a full hardware–software pipeline, from configuring the flight controller to tuning control loops for stable flight.

Key Features & Implementation:

• Flight Control: Leveraged the Ardupilot firmware for stabilization, autonomous navigation, and safety failsafes.

• Navigation & GPS: Implemented waypoint-based autonomous missions via Mission Planner, allowing the drone to follow predefined routes with return-to-home capability.

• Telemetry & Communication: Experimented with HC-05 Bluetooth modules for telemetry before transitioning to a dedicated 433 MHz telemetry module for reliable ground-to-air data transfer.

• Hardware Stack:

  • Standard quadcopter frame with brushless DC motors (BLDCs) and electronic speed controllers (ESCs).

  • Ardupilot-compatible flight controller (Pixhawk / APM).

  • GPS + magnetometer module for position hold and navigation.

  • Power distribution board and LiPo battery system.

Challenges & Lessons Learned:

• Gained hands-on understanding of PID tuning by observing real-world effects of proportional, integral, and derivative changes during flight.

• Discovered the limitations of makeshift telemetry solutions like HC-05, reinforcing the importance of proper communication modules.

• Understood how reference frames (NED vs. body-fixed frames) directly affect flight stability, especially during takeoff.

• Developed practical drone assembly skills, from wiring and ESC calibration to propeller balancing.

Why This Project Matters:
This project was my first deep dive into UAV systems and open-source autopilot software, giving me exposure to both the mechanical integration of drone components and the software configurations needed for autonomous flight. It sparked my long-term interest in aerial robotics, autonomous navigation, and control systems.

Next Steps:

• Upgrade to a companion computer setup (e.g., Raspberry Pi + ROS) for more advanced autonomy and mission planning.

• Integrate computer vision modules (cameras + OpenCV) for vision-based stabilization or object tracking.

• Add failsafe redundancy with multiple telemetry channels and backup power.

• Expand the system to test beyond-visual-line-of-sight (BVLOS) missions with advanced safety protocols.