Learning Model Predictive Control for Quadrotors Minimum-Time Flight in Autonomous Racing Scenarios

Abstract

In this paper, we design a Learning Model Predictive Control (LMPC) algorithm for quadrotors autonomous racing. The proposed algorithm allows to define a highly customizable 3D race track, in which multiple types of obstacles can be inserted. The controller is then able to autonomously find the best trajectory minimizing the quadrotor lap time, by learning from data coming from previous flights within the track, ensuring also the avoidance of all the obstacles therein. We also present novel relaxation approaches for the LMPC optimization problem, that allow to reduce it from a mixed-integer nonlinear program to a quadratic program. The LMPC algorithm is tested via several software-in-the-loop simulations, showing that the algorithm has learned to fly the quadrotor aggressively and dexterously, managing to both find the minimum-time trajectory and avoid the obstacles inside the track.