自主无人机竞速：虚拟管道内的时间最优空间迭代学习控制
Autonomous Drone Racing: Time-Optimal Spatial Iterative Learning Control within a Virtual Tube
AI总结：
针对无人机竞速中训练和比赛时间长的问题，提出一种模仿顶尖车手经验的时间最优空间迭代学习控制方法，在线迭代学习轨迹以无模型、低计算成本实现最优竞速，并在仿真和真实四旋翼实验中超越现有最先进方法。
摘要：
无人机通常需要在最短时间内完成配送、摄影和救援以提高效率。许多自主无人机竞速赛提供了平台，以寻求为上述目的尽快完成比赛的算法。遗憾的是，现有方法往往无法在无人机竞速比赛中保持较短的训练和比赛时间。这促使我们通过模仿顶尖赛车手的训练经验来开发一种高效的学习方法。与用于精确跟踪的传统迭代学习控制方法不同，所提出的方法在线迭代学习轨迹以尽快完成比赛。使用不同模型的仿真和实验表明，所提出的方法是无模型的，并且能够以低计算要求实现最优结果。此外，该方法在基准无人机竞速平台上的比赛时间方面超越了一些最先进的方法。还在真实四旋翼飞行器上进行了实验以证明其有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
