Efficient and Robust Time-Optimal Trajectory Planning and Control for Agile Quadrotor Flight

TL;DR

This paper introduces a fast, robust method for planning and tracking time-optimal trajectories for agile quadrotors, enabling quick response and high accuracy in dynamic environments.

Contribution

It proposes a novel trajectory planning approach with waypoint constraints and adaptive sampling, combined with a time-adaptive model predictive control scheme for improved tracking.

Findings

Significantly accelerates trajectory planning compared to traditional methods.

Enhances tracking robustness and accuracy through adaptive control.

Successfully validated in simulations and real-world experiments.

Abstract

Agile quadrotor flight relies on rapidly planning and accurately tracking time-optimal trajectories, a technology critical to their application in the wild. However, the computational burden of computing time-optimal trajectories based on the full quadrotor dynamics (typically on the order of minutes or even hours) can hinder its ability to respond quickly to changing scenarios. Additionally, modeling errors and external disturbances can lead to deviations from the desired trajectory during tracking in real time. This letter proposes a novel approach to computing time-optimal trajectories, by fixing the nodes with waypoint constraints and adopting separate sampling intervals for trajectories between waypoints, which significantly accelerates trajectory planning. Furthermore, the planned paths are tracked via a time-adaptive model predictive control scheme whose allocated tracking time can be adaptively adjusted on-the-fly, therefore enhancing the tracking accuracy and robustness. We evaluate our approach through simulations and experimentally validate its performance in dynamic waypoint scenarios for time-optimal trajectory replanning and trajectory tracking.