Davide Celestini — accepted AI-conference papers

2024

Transformer-Based Model Predictive Control: Trajectory Optimization via Sequence Modeling

Davide Celestini, Daniele Gammelli, Tommaso Guffanti, Simone D'Amico, Elisa Capello, Marco Pavone

RA-L 2024

Model predictive control (MPC) has established itself as the primary methodology for constrained control, enabling general-purpose robot autonomy in diverse real-world scenarios. However, for most problems of interest, MPC relies on the recursive solution of highly non-convex trajectory optimization

Cited by 41 Source ↗Scholar ↗

2022

Trajectory Planning for UAVs Based on Interfered Fluid Dynamical System and Bézier Curves

Davide Celestini, Stefano Primatesta, Elisa Capello

RA-L 2022

In this paper, a 3D trajectory planner for Unmanned Aerial Vehicles (UAVs) based on Interfered Fluid Dynamical System (IFDS) and Bézier curves is introduced. The proposed strategy joints the potentialities of IFDS with the use of Bézier curves to obtain optimized trajectories with continuous curvatu

Cited by 21 Source ↗Scholar ↗