Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing

TL;DR

This paper introduces a time-optimal drone racing trajectory planner that models gate configurations to improve lap times, demonstrating real-world effectiveness and computational efficiency in complex race tracks.

Contribution

The paper presents a novel planner that incorporates gate configurations into trajectory optimization, outperforming previous waypoint-based methods in efficiency and lap time reduction.

Findings

Reduces lap times by considering gate configurations.

Computes trajectories within seconds for complex tracks.

Validated through real-world drone racing experiments.

Abstract

In drone racing, the time-minimum trajectory is affected by the drone's capabilities, the layout of the race track, and the configurations of the gates (e.g., their shapes and sizes). However, previous studies neglect the configuration of the gates, simply rendering drone racing a waypoint-passing task. This formulation often leads to a conservative choice of paths through the gates, as the spatial potential of the gates is not fully utilized. To address this issue, we present a time-optimal planner that can faithfully model gate constraints with various configurations and thereby generate a more time-efficient trajectory while considering the single-rotor-thrust limits. Our approach excels in computational efficiency which only takes a few seconds to compute the full state and control trajectories of the drone through tracks with dozens of different gates. Extensive simulations and experiments confirm the effectiveness of the proposed methodology, showing that the lap time can be further reduced by taking into account the gate's configuration. We validate our planner in real-world flights and demonstrate super-extreme flight trajectory through race tracks.