Time-Optimal Online Replanning for Agile Quadrotor Flight
Angel Romero, Robert Penicka, Davide Scaramuzza
Abstract
In this letter, 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, on the 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on-the-fly</i> . 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.
BibTeX
@inproceedings{ral2022_timeoptimalonlin,
title = {Time-Optimal Online Replanning for Agile Quadrotor Flight},
author = {Angel Romero and Robert Penicka and Davide Scaramuzza},
booktitle = {RA-L 2022},
year = {2022}
}