Model Predictive Contouring Control for Time-Optimal Quadrotor Flight

TL;DR

This paper introduces a real-time Model Predictive Contouring Control approach for quadrotors that efficiently generates near time-optimal trajectories through multiple waypoints, outperforming existing methods and human pilots.

Contribution

It proposes a novel MPCC method that concurrently solves time allocation and control, enabling real-time, near time-optimal quadrotor flight planning and execution.

Findings

Achieves real-time trajectory generation within minutes.

Outperforms state-of-the-art methods in lap time and speed.

Outperforms human pilots in speed and efficiency.

Abstract

We tackle the problem of flying time-optimal trajectories through multiple waypoints with quadrotors. State-of-the-art solutions split the problem into a planning task - where a global, time-optimal trajectory is generated - and a control task - where this trajectory is accurately tracked. However, at the current state, generating a time-optimal trajectory that considers the full quadrotor model requires solving a difficult time allocation problem via optimization, which is computationally demanding (in the order of minutes or even hours). This is detrimental for replanning in presence of disturbances. We overcome this issue by solving the time allocation problem and the control problem concurrently via Model Predictive Contouring Control (MPCC). Our MPCC optimally selects the future states of the platform at runtime, while maximizing the progress along the reference path and minimizing the distance to it. We show that, even when tracking simplified trajectories, the proposed MPCC results in a path that approaches the true time-optimal one, and which can be generated in real-time. We validate our approach in the real world, where we show that our method outperforms both the current state-of-the-art and a world-class human pilot in terms of lap time achieving speeds of up to 60 km/h.