The Open Drone Platform

Created on March 11, 2026

2026   ·   projects   ·   open-drone

Ever since I was young, I’ve been fascinated by robots. Fast forward to my undergraduate and graduate studies, where I took courses in Modern Control Theory, Applied Machine Learning, Reinforcement Learning, and State Estimation. Through those experiences I developed a strong interest in the intersection of robotics, control theory, embedded systems, and simulation.

Rather than approaching these topics purely from theory or isolated experiments, I wanted to learn the way many real robotics systems are developed: by building something end-to-end.

That goal led to the start of what I’m calling the Open Drone Platform (name subject to change :]).

At its core, the project is an attempt to recreate the development pipeline used in real robotics and aerospace systems, but in a form that is accessible to individuals, students, and robotics teams.

Instead of simply building a drone, the project explores the full stack:

System Design → Physics Modeling → Simulation → Algorithm Development → Validation → Hardware Deployment

The end goal is not just to fly a drone, but to understand how autonomous systems are engineered from first principles.

How the Project Started

The project originally began as a fairly straightforward hardware experiment: build a small quadrotor using off-the-shelf components and a Raspberry Pi Pico 2 as the flight controller.

The primary motivation was to gain hands-on experience implementing sensor fusion algorithms, specifically the Multiplicative Extended Kalman Filter (MEKF) for attitude estimation.

Building the hardware was an important step, but it quickly became clear that jumping directly into hardware development makes experimentation slow and risky. Crashes destroy hardware, debugging becomes difficult, and algorithm development slows down considerably.

This is exactly why most real robotics systems rely heavily on simulation before hardware deployment.

Expanding the Project: Adding Simulation

To address this, I expanded the project to include a 6-degree-of-freedom (6DOF) flight dynamics simulator.

The simulator models the drone using rigid-body Newton–Euler equations and allows algorithms to be developed and tested in software before being deployed to the physical vehicle.

This creates a simulation-to-real (sim2real) workflow:

Simulation → Algorithm Development → Validation → Real-world Flight

With this workflow I can:

  • test estimation algorithms without risking hardware
  • validate dynamics models
  • iterate much faster
  • reproduce experiments deterministically

In practice, this approach is widely used across robotics, aerospace, and autonomous vehicle development.

Ensuring the Simulator is Correct

Writing a simulator is relatively easy.

Writing a correct simulator is much harder.

To validate the dynamics implementation, the simulator is tested against the NASA Engineering and Safety Center (NESC) 6-DOF Flight Simulation Check Cases.

These reference cases provide known trajectories for specific rigid-body systems and are commonly used to verify flight dynamics implementations.

Using these test cases helps ensure that the simulator’s equations of motion and numerical integration behave as expected.

Designing for Sim2Real

One of the guiding principles of the project is that the same algorithms should run in both simulation and hardware whenever possible.

To support this, the codebase is structured so that core components are shared across environments:

  • math libraries
  • quaternion operations
  • state estimation algorithms
  • sensor interfaces

The simulator uses these same components, which helps reduce the gap between simulation behavior and real-world performance.

Making the Project Accessible

Another goal of the project is accessibility. I’ve always enjoyed building things myself and have a strong appreciation for the principles of open-source development. I want to contribute something that others can learn from, build upon, and improve. You could say that this is my way of giving back to the community that has helped me to learn and grow my engineering skills, interests, and passions.

With that in mind, I wanted the setup process to be as painless as possible so that people could experiment with the system without spending days configuring toolchains.

To achieve this, the entire development environment is containerized. The container includes:

  • the C++ toolchain
  • the Pico SDK
  • simulation dependencies
  • Python visualization tools

Anyone should be able to clone the repository, launch the container, and immediately begin building or experimenting.

Alongside the software, I will also be publishing:

  • a full bill of materials
  • CAD files for the drone frame
  • assembly documentation

The idea is that someone could reproduce the entire platform from scratch.

Why Open?

There are many incredible open robotics platforms, but many of them assume significant prior knowledge or require complex setup.

My hope with this project is to create something that sits in the middle:

  • complex enough to reflect real engineering practices
  • simple enough to understand and build

Ideally, the platform could be useful for:

  • robotics clubs
  • university control systems courses
  • early robotics education
  • individuals learning autonomous systems

What Comes Next

There is still a lot to build.

Upcoming work includes:

  • implementing full drone actuator models
  • improving the simulation environment
  • integrating real-time visualization
  • flight control algorithms
  • hardware testing and validation

This project will likely evolve quite a bit as I continue exploring the field of autonomous systems.

For now, the goal is simple:

learn by building.



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