Top-down UAV frame of a paddock at 40 m with the 16 target bowls boxed Blue calibration mat with sixteen white bowls, seen from a UAV at 15 m

Edge AI · Aerial autonomy · Brisbane

I build edge AI and autonomy for small UAVs, from the detector to the airframe.

Software engineer in Brisbane, Australia. MPhil in aerial robotics at QUT Centre for Robotics, thesis under examination, after 16 years of shipping software, 12 of them on mobile.

About

Perception that has to run on the aircraft

My work sits where computer vision meets flight hardware. I train detectors on aerial imagery, quantise them to run on a small NPU at the edge, and fly them on PX4 and ArduPilot airframes, including the fixed-wing and multirotor UAVs I print and build myself. My MPhil asked a practical question: when should a UAV search from high altitude and verify low, and what does that trade-off cost in mission time? The answer became a decision framework, a simulation and a published paper.

Before robotics I spent twelve years in commercial mobile development, ending as Principal Software Engineer and iOS Development Lead at CBRE Asia Pacific, where I led a 65-app suite and its release pipeline. I hold a Bachelor of Information Technology (First Class Honours) from QUT and a CASA Remote Pilot Licence to 25 kg.

More on GitHub
Sixteen white bowls on a blue mat, seen from a UAV hovering at 5 m Sixteen white bowls laid out on grass beside a tree shadow, seen from a UAV at 15 m

Selected work

Recent work

All repositories on GitHub
Edge AIPyTorch · ONNX · Hailo

DeepWeeds edge classifier

Rangeland weed classifier taken all the way to silicon: 96% top-1 at 2,200 FPS on a Raspberry Pi 5 with a Hailo-8 NPU, within a point of the FP32 baseline, after an INT8 quantisation study that forced a change of backbone.

Publications

Peer-reviewed

All publications on ORCID

Updates

Recent milestones

  • Fly High or Fly Low? is out, open access, with Frederic Maire, Juan Sandino and Felipe Gonzalez.

  • MPhil thesis lodged for examination

    Altitude and Density Trade-offs in High-Recall UAV Object Detection: A Quantitative Framework for Survey Strategy Selection, at QUT Centre for Robotics.

  • 96% top-1 at 2,200 FPS on a Raspberry Pi 5, within a point of the FP32 baseline.

Contact

Working on perception or autonomy for small UAVs?

I'm open to collaborations and new projects across industry, research and government, especially around on-aircraft perception, survey planning and PX4 and ArduPilot systems.