Autonomous Drone Racing: Time-Optimal Spatial Iterative Learning Control within a Virtual Tube

TL;DR

This paper introduces a model-free, iterative learning control method for autonomous drone racing that optimizes race time by online trajectory learning, outperforming existing methods in simulations and real-world tests.

Contribution

The paper presents a novel, model-free iterative learning control approach that learns optimal trajectories online for drone racing, reducing training time and computational complexity.

Findings

Achieves near-optimal racing time in simulations and real experiments.

Outperforms state-of-the-art methods on a benchmark drone racing platform.

Demonstrates effectiveness with real quadcopter experiments.

Abstract

It is often necessary for drones to complete delivery, photography, and rescue in the shortest time to increase efficiency. Many autonomous drone races provide platforms to pursue algorithms to finish races as quickly as possible for the above purpose. Unfortunately, existing methods often fail to keep training and racing time short in drone racing competitions. This motivates us to develop a high-efficient learning method by imitating the training experience of top racing drivers. Unlike traditional iterative learning control methods for accurate tracking, the proposed approach iteratively learns a trajectory online to finish the race as quickly as possible. Simulations and experiments using different models show that the proposed approach is model-free and is able to achieve the optimal result with low computation requirements. Furthermore, this approach surpasses some state-of-the-art methods in racing time on a benchmark drone racing platform. An experiment on a real quadcopter is also performed to demonstrate its effectiveness.