Learning Time-Optimal Online Replanning for Distributed Model Predictive Contouring Control of Quadrotors

Abstract

Ahstract-Achieving time-optimal flight in real time for multi-drone systems presents significant challenges, particularly in scenarios requiring rapid responses or aggressive maneuvers. This paper introduces a novel framework that bridges the gap between time-optimal polynomial trajectory generation and optimal control, facilitating efficient online replanning (100 Hz onboard) for multiple quadrotors. Specifically, the proposed method leverages a neural network to learn optimal time allocations for polynomial trajectories, which are then integrated with Model Predictive Contouring Control to fully exploit the dynamics of quadrotors. We further extend this approach to multi-drone systems, enabling collaborative high-speed flight with reciprocal collision avoidance. We benchmark the time-optimal performance and computational efficiency of our method in a drone racing scenario and demonstrate its effectiveness in agile cooperative flight within more constrained simulation and real-world environments. The results demonstrate that the proposed method achieves agile waypoint traverse at a speed of up to 19 m/s in simulation and up to 9 m/s in two-drone real-world scenario. [video<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup>https://www.youtube.com/watch?v=KE97sKwYpAs]