We are a robotics lab at Queen's University building aerial robots that work where autonomy is hardest: over open water, at night and in wind. Our research spans learning-based model predictive control, GNSS-denied navigation, and teams of aerial, surface and underwater vehicles. We develop the algorithms and the systems that run them, from simulation to the field.
We are recruiting MASc and PhD students — deadlines and how to apply.
Fishing for robots... with drones?!
CUDA MPC: a GPU-native solver for model predictive control
Two Robora Lab papers at the IFAC World Congress 2026
UAV See, UGV Do: Aerial Imagery and Virtual Teach Enabling Zero-Shot Ground Vehicle Repeat - IROS 25
Tiny Learning-Based MPC for Multirotors: Solver-Aware Learning for Embedded Control - Mechatronics 26
Bistable SMA Driven Engine for Pulse Jet Locomotion in Soft Aquatic Robots - Robosoft 25
A Time and Place to Land: Learning Distributed MPC for Multirotor Landing on Surface Vessel in Waves - ICUAS 25
Distributed Model Predictive Control for Cooperative Landing on Uncrewed Surface Vessel in Waves - ICUAS 24
Fly Out The Window: Exploiting Discrete-Time Flatness for Fast Vision-Based Multirotor Flight - ICRA 22
