AeroMed Quadcopter Team Logo
Summary: 

Urban traffic and traditional delivery methods often delay the arrival of critical medications, especially during peak hours. Currently, pharmaceutical logistics rely on pollutant-emitting vehicles that group multiple orders together, leading to slow delivery times and unnecessary human contact. There is a clear need for a faster, greener, and more isolated delivery method to ensure patients receive 1lb payloads safely and efficiently.

This project addresses these issues by developing an autonomous drone capable of delivering medical supplies directly to a client’s landing pad. By using aerial navigation, the system bypasses road congestion, reduces transportation costs, and eliminates tailpipe emissions. This matters because it provides a reliable, "no-contact" service that is essential for time-sensitive healthcare.

The project directly benefits pharmacists, delivery operators, and local businesses by streamlining their distribution. Most importantly, it supports homebound or disabled clients who require secure, direct access to medication. It also considers the public and city technicians by ensuring a safe and quiet flight path through the urban environment.

Technical Approach/Methodology: 

To solve the problem of slow and pollutant-heavy medical deliveries, we are developing an autonomous quadcopter designed for speed, precision, and safety. Our approach combines custom hardware with advanced flight software to move 1lb of medication from a pharmacy directly to a patient's doorstep.

Building the Drone

We utilized 3D printing technology to create custom parts, such as payload containers and specialized base plates, allowing us to prototype and test our designs rapidly. To ensure the drone can safely lift its 5lb total weight, we used Blade Element Momentum Theory—a mathematical method that helps us select the perfect combination of motors, propellers, and batteries for maximum efficiency and thrust.

Smart Flight Systems

The "brain" of our drone is the HolyBro 6C Flight Controller paired with a high-accuracy M9N GPS, which keeps the drone on course within 2 meters of its target. For the final, critical stage of delivery, we integrated a precision landing system. A small Raspberry Pi 4 computer and an Arducam camera act as the drone's eyes, spotting a unique "ArUco" (barcode-like) landing pad. The computer talks to the flight controller to align the drone perfectly with the pad for a safe, centered landing.

Mission Control & Safety

We use Mission Planner and ArduPilot software to program the autonomous mission, which includes commands for takeoff, cruising, and returning home. This allows an operator to monitor the drone's health from a distance, even when it is out of sight. To stay safe and legal, the drone is equipped with an FAA Remote ID Module, which broadcasts its location to tracking apps, ensuring we comply with all flight regulations for drones over 250g. Once the drone reaches the pad, a servo-driven mechanism (MG996R) releases the medical payload, completing a secure, no-contact delivery.

Outcomes: 

By the end of the project, our team successfully developed and demonstrated a fully autonomous drone system capable of transporting a 1-lb payload over a 50-meter distance.

Over the span of two academic quarters, our team designed, assembled, and tested the complete drone platform. As part of this process, we developed a full Bill of Materials (BOM) and assembly documentation, detailing all structural, electrical, and control components required to build and operate the drone system. This documentation provides a comprehensive reference for the drone’s configuration and enables future teams to reproduce or expand upon the platform.

In addition to the hardware development, our team conducted Finite Element Analysis (FEA) on critical structural components of the drone. This analysis evaluated stresses and structural performance under expected loading conditions, including the 1-lb payload, verifying that the selected components and structural configuration could safely support operational loads during flight.

The final system integrates several key capabilities developed throughout the project, including:

  • Autonomous flight execution using Mission Planner
  • Servo actuation mechanisms for payload interaction
  • Raspberry Pi onboard processing with a Python takeover script
  • Aruco marker landing pad detection for precision landing
  • Autonomous takeoff, navigation, and landing

These subsystems were successfully integrated into a single fully autonomous mission. During the final test flight on March 10, 2026, the drone executed the complete mission profile, demonstrating autonomous navigation, subsystem coordination, and precision landing.

Additionally, the team experimentally determined the operational and maximum communication ranges for the system, as well as precision capabilities:

  • Maximum allowable target range: 572 m
  • Operational / maximum drone range: 1144 m / 1482 m
  • GPS precision: 0.42 m
  • Landing accuracy: 2 m

Presentation Timeline

Throughout the project, the team completed the following formal presentations and design reviews:

Milestones                                                                                 Date

Project Deliverables

Project Website: 
https://projects.eng.uci.edu/projects/2025-2026/aeromed-quadcopter
Course Department: 
MAE
Academic Year: 
2025-2026
Term(s): 
Fall
Winter
Project Category: 
Competition/Extracurricular Project Sub-team
Sponsor/Mentor Name: 
Mohamed Shorbagy
Project Poster: 
Project Video: 
Teammates: 
Chuong Tran (PM)
Brandon Hsi (Engineer)
Dillon Kim (Engineer)
Justin Quan (Engineer)
Jaydon Schoonenberg (Engineer)
Alex Stefany (Engineer)