Time-Optimal Online Replanning for Agile Quadrotor Flight

TL;DR

This paper presents a real-time, online replanning method for quadrotor flight that computes time-optimal trajectories efficiently and adapts to environmental changes and disturbances, enabling high-speed racing and wind resilience.

Contribution

Introduces a novel sampling-based method for fast generation of time-optimal paths and integrates it with a Model Predictive Contouring Control for full quadrotor dynamics, enabling real-time replanning.

Findings

Successfully flies at over 60 km/h on a racing track with moving gates.

Demonstrates robustness against wind disturbances up to 68 km/h.

First method to achieve real-time, adaptive time-optimal quadrotor control.

Abstract

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.