Graph-Informed MPC for Drone Swarms
Collaborative multi-drone payload transport with learned residual modeling
I worked on a research project focused on collaborative multi-drone payload transport using model predictive control. The goal was to improve stability and reduce swing during coordinated transport by combining classical control with learned correction terms.
What I built
- Designed a model predictive control framework for collaborative drone swarms.
- Trained a graph neural network to predict nonlinear MPC residuals and cable tension forces.
- Generalized the learned model across variable swarm sizes and payload weights.
- Improved robustness of transport trajectories under dynamic load conditions.
Why it matters
This work sits at the intersection of robotics, control, and machine learning. The learned graph-based component helps the controller adapt to changing topology and physical conditions, which is especially important for aerial multi-agent systems.