Model Predictive Contouring Control for Time-Optimal Quadrotor Flight (TRO 2022)

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.
Reference:
A. Romero, S. Sun, P. Foehn, D. Scaramuzza
Model Predictive Contouring Control for Time-Optimal Quadrotor Flight
IEEE Transactions on Robotics, 2022
PDF: rpg.ifi.uzh.ch/...
Our research in Drone Racing:
rpg.ifi.uzh.ch/...
Our research in Agile Drone Flight:
rpg.ifi.uzh.ch/...
Affiliations:
A. Romero, S. Sun, P. Foehn, D. Scaramuzza are with the Robotics and Perception Group, Dep. of Informatics, University of Zurich, and Dep. of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland
rpg.ifi.uzh.ch/
Music Credits: scottholmesmusic.com under Free Creative Commons License

[@snakehaihai]

nice. So human lost in Go chess, then lost in DOTA2, and now lost in drone racing

[@majdwardeh3698]

Not yet! the state estimation of the drone here is provided through the Opti-Track system (as shown at the beginning of the video).

[@maximeg3659]

Congrats for pushing the limit 💪 🔥 Now waiting for on-board full state estimation for our dream to be complete ^^

[@LeRainbow]

Nice!! Congratulations It works! :D Incredible how fast you pump out research after research. Now we need a way the drone knows its position and orientation in 3D space without the motion tracking cameras making it able to race through literally anything, anywhere.

[@flwi]

That's a really fast GPU - almost 60km/h ;-) Very impressive result!

[@JoePelusoMedia]

So freaking cool! Very excited to learn more about this