Open Source Summit + Embedded Linux Conference North America 2026

Many flight‑control and autonomy programs still begin with hardware prototyping, only to discover late in development that controller tuning, transition behavior, and system coupling are difficult to resolve without a reliable model. This session presents a practical simulation‑to‑flight workflow based on recent university flight‑test research, demonstrating why starting with simulation is critical for reducing risk and accelerating development while helping teams avoid costly UAV crashes and hardware damage. Using a subscale eVTOL case study, we show how aerodynamic modeling, propulsion modeling, and six‑degree‑of‑freedom dynamics are integrated into a digital twin that directly informs control‑law design, hardware deployment, and flight testing. The workflow culminates in direct PX4 implementation and a comparison of simulation predictions against real flight‑test data across hover, transition, and forward flight, highlighting close agreement between model and reality. The talk emphasizes how a simulation‑first approach enables faster iteration, safer testing, and more predictable flight performance for the broader aerospace and UAS community.

A team of aerial and ground robots operating in a coordinated way is the key to large-scale operations in kilometer-scale environments. Nonetheless, significant challenges, such as orchestration, intermittent communications, and command-and-control of the team, need to be solved.
This talk will explore an application where a team of ground robots performs a search mission, with an UAV acting as an eye in the sky and a data mule between the different ground robots. We will focus on the challenges for this task and how PX4 can be used in a heterogeneous team of aerial and ground robots. We will show examples of the system in large-scale urban and rural environments. Finally, we will mention how large foundational models can enable more complex tasks for a robot team.

This session introduces the open-source Unified Autonomy Stack (https://github.com/ntnu-arl/unified_autonomy_stack), a containerized, system-level solution enabling robust autonomy across diverse aerial and ground robot morphologies. The architecture centers on three modules -multi-modal perception, multi-stage planning, and multi-layered safety mechanisms- that together deliver end-to-end mission autonomy. Resulting behaviors include safe navigation into unknown regions, exploration of complex environments, and efficient inspection planning. The stack has been validated on multiple multirotor platforms and legged robots operating in GNSS-denied and perceptually degraded environments, demonstrating resilient performance in demanding conditions. To facilitate ease of adoption and extension, we additionally release a reference hardware design that integrates a full multi-modal sensing suite, time-synchronization electronics, and high-performance compute capable of running the entire ROS-based stack while leaving headroom for further development. Strategically, we aim to expand the Unified Autonomy Stack to cover most robot configurations across air, land, and sea.

PX4-based drones are powerful, but interacting with them typically requires specialized ground control software and trained operators. This talk presents a new interaction model: controlling and querying a PX4 drone using natural language from a standard mobile phone.

The system combines a Model Context Protocol (MCP)–style interface (inspired by ROS 2 MCP implementations) to expose PX4 capabilities as structured, machine-readable commands, with OpenAI’s real-time ChatGPT API to interpret user intent. A phone call or voice interaction—handled via Twilio—becomes the primary user interface, allowing operators to issue commands such as “take off to 10 meters,” “orbit that location,” or “what’s your battery state?” and receive immediate spoken feedback.

The talk will cover:

* How PX4 commands, telemetry, and state are exposed through an MCP-like abstraction
* Real-time bidirectional communication between phone, AI model, and drone using Twilio and RealTime API
* Safety considerations, command validation, and constraints when using AI-mediated control
* Practical use cases, fly missions, object detection all using hands free control
* Lessons learned, what worked and didn't work

PX4's EKF2 replay module allows developers to tune estimator performance by re-running the EKF on prerecorded logs. This is useful for EKF2 development or for testing the impact of different parameters on performance. KEF robotics has patched the replay module so that replay progress can be controlled by an external program, enabling deterministic *integration* testing. We are using this patched replay module to test the integration of PX4 with an external vision navigation system.

We will cover:
- Using the replay module to assess performance of different EKF2 parameters.
- Development and testing considerations for a visual navigation system that integrates with PX4.
- Modifying the replay module so that it can be deterministically 'stepped' in sync with an external program
- Results from integration testing with the modified replay module

The audience will get a better understanding of the replay system, technical details on modifying the replay system for integration testing, and the benefits of integration testing with regard to visual navigation development with PX4. We will also share the patch we made to the replay system.