Workflow

1) Benchmark & evidence → establish repeatable tests and measurement pipelines (motion capture/sensors)

2) Model & co-simulate → build control-oriented models and co-simulation loops for evaluation.

3) Design & integrate → implement guidance/control and connect the system stack end-to-end.

4) Validate & iterate → verify real-time performance, document interfaces, and enable adoption.

Integration surfaces

I provide technical leadership on integration and interoperability across:

1) Flight controllers ↔ ground stations, including motion-tracking pipelines and real-time data distribution.

2) Robotic software environments and connected autonomy stacks.

3) Communication layers and protocols, ensuring robust real-time interoperability.

4) Multi-sensor configurations, integrating sensing into validation and deployment workflows

Experimental infrastructure

• Motion capture systems and tracker workflows (real-time measurement pipelines).

• Connectivity and local network design for multi-machine real-time interoperability.

• Parallel/accelerated computing workflows where required for high-throughput analysis.

Modelling, simulation, and CAx

• Mathematical modelling and control architecture toolchains (guidance, navigation & control workflows).

• CAx environments for CAD/CAE/CFD, electrical routing/wiring, and motion simulation.

• Integrated co-simulation loops for airframe analysis and control-loop design.

Programming & data

• Python-based tooling for automation, integration, and analysis.

• Database foundations for structured experimentation and traceability (MySQL basics).

• Messaging/data transport foundations for connected systems (ZeroMQ).

Flight enablers and embedded integration

• Flight controllers, propulsion systems, GPUs.

• Sensor settings for vision and localisation systems.

• Flight sensor calibration.

• LiDARs and depth cameras for sensing-driven validation and constrained deployment.