# Time-Optimal Online Replanning for Agile Quadrotor Flight

arXiv: https://arxiv.org/abs/2203.09839 · HTML: https://mlsanity.com/paper/2203.09839/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2022.

## Built on by (most cited first, 8 of 8)

- [Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2409.16720/) (2024)
- [Efficient Online Learning with Memory via Frank-Wolfe Optimization: Algorithms with Bounded Dynamic Regret and Applications to Control](https://mlsanity.com/paper/2301.00497/) (2023)
- [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)
- [Mastering Diverse, Unknown, and Cluttered Tracks for Robust Vision-Based Drone Racing](https://mlsanity.com/paper/2512.09571/) (2025)
- [Variable Time-Step MPC for Agile Multi-Rotor UAV Interception of Dynamic Targets](https://mlsanity.com/paper/2503.14184/) (2025)
- [Towards Task-Oriented Flying: Framework, Infrastructure, and Principles](https://mlsanity.com/paper/2504.15129/) (2025)
- [Bridging Performance and Generalization in Reinforcement Learning for Agile Flight](https://mlsanity.com/paper/2606.27348/) (2026)

## Abstract (arXiv)

In this paper, we tackle the problem of flying a quadrotor using time-optimal control policies that can be replanned online when the environment changes or when encountering unknown disturbances. This problem is challenging as the time-optimal trajectories that consider the full quadrotor dynamics are computationally expensive to generate (order of minutes or even hours). We introduce a sampling-based method for efficient generation of time-optimal paths of a point-mass model. These paths are then tracked using a Model Predictive Contouring Control approach that considers the full quadrotor dynamics and the single rotor thrust limits. Our combined approach is able to run in real-time, being the first time-optimal method that is able to adapt to changes on-the-fly. We showcase our approach's adaption capabilities by flying a quadrotor at more than 60 km/h in a racing track where gates are moving. Additionally, we show that our online replanning approach can cope with strong disturbances caused by winds of up to 68 km/h.