# Model Predictive Contouring Control for Time-Optimal Quadrotor Flight

arXiv: https://arxiv.org/abs/2108.13205 · HTML: https://mlsanity.com/paper/2108.13205/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2021.

## Built on by (most cited first, 21 of 21)

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- [Reactive Aerobatic Flight via Reinforcement Learning](https://mlsanity.com/paper/2505.24396/) (2025)
- [Multi-Fidelity Reinforcement Learning for Time-Optimal Quadrotor Re-planning](https://mlsanity.com/paper/2403.08152/) (2024)
- [Agile Flight Emerges from Multi-Agent Competitive Racing](https://mlsanity.com/paper/2512.11781/) (2025)
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- [Mastering Diverse, Unknown, and Cluttered Tracks for Robust Vision-Based Drone Racing](https://mlsanity.com/paper/2512.09571/) (2025)
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- [Non-Equilibrium MAV-Capture-MAV via Time-Optimal Planning and Reinforcement Learning](https://mlsanity.com/paper/2503.06578/) (2025)
- [Learning Agile Gate Traversal via Analytical Optimal Policy Gradient](https://mlsanity.com/paper/2508.21592/) (2025)
- [Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles](https://mlsanity.com/paper/2602.24030/) (2026)
- [Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control](https://mlsanity.com/paper/2211.07389/) (2022)
- [Agile Robotics: Optimal Control, Reinforcement Learning, and Differentiable Simulation](https://mlsanity.com/paper/2407.01568/) (2024)
- [RESC: A Reinforcement Learning Based Search-to-Control Framework for Quadrotor Local Planning in Dense Environments](https://mlsanity.com/paper/2408.00275/) (2024)
- [MAVEN: A Meta-Reinforcement Learning Framework for Varying-Dynamics Expertise in Agile Quadrotor Maneuvers](https://mlsanity.com/paper/2603.10714/) (2026)
- [CA-AC-MPC: CUDA-Accelerated Actor-Critic Model Predictive Control](https://mlsanity.com/paper/2605.29155/) (2026)
- [Bridging Performance and Generalization in Reinforcement Learning for Agile Flight](https://mlsanity.com/paper/2606.27348/) (2026)
- [Conditional Invertible Neural Networks for Data-Driven UAV Control: A 2-D Proof of Concept](https://mlsanity.com/paper/2607.13703/) (2026)
- [Precision at Speed: Sample-Efficient Online Model-Based Reinforcement Learning for Hydraulic Excavator Control](https://mlsanity.com/paper/2609.31025/) (2026)

## Abstract (arXiv)

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.