The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition
The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition
复制标题
2019 AI 机器人赛车大赛获奖作品背后的人工智能
DOI：
10.55417/fr.2022042
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
G. D. Croon
中科院分区：
文献类型：
--
作者：
C. D. Wagter;F. Paredes;N. Sheth;G. D. Croon
无人机竞赛目前是机器人技术的一个极端挑战。虽然人类无人机赛车手可以以高达190公里/小时（53米/秒）的速度在复杂的赛道上飞行，但自主无人机在达到同样的适应性和速度之前，仍然需要在资源方面的严重限制下解决人工智能中的几个基本问题。在本文中，我们介绍了第一个AI机器人赛车（AIRR）赛道的获胜解决方案，这是一个自主无人机竞赛，所有参赛队伍都使用同一架无人机，他们只能有限地使用。我们的方法的核心是受到人类飞行员如何将联合收割机对比赛大门的嘈杂观察与无人机动态的心理模型相结合的启发。导航是基于门检测与有效的深度神经分割网络和主动视觉。结合对鲁棒状态估计和基于风险的控制的贡献，我们的解决方案能够达到1.33 km/h（9.2 m/s）的速度，从而是之前无人机竞赛中速度的三倍多。这项工作分析了每个组件的性能，并讨论了训练时间有限的高性能现实世界AI应用程序的影响。
Autonomous drone racing currently forms an extreme challenge in robotics. While human drone racers can fly through complex tracks at speeds of up to 190 km/h (53 m/s), autonomous drones still need to tackle several fundamental problems in AI under severe restrictions in terms of resources before they reach the same adaptability and speed. In this article, we present the winning solution of the first AI Robotic Racing (AIRR) Circuit, an autonomous drone race competition in which all participating teams used the same drone, to which they had limited access. The core of our approach is inspired by how human pilots combine noisy observations of the race gates with a mental model of the drone’s dynamics. The navigation is based on gate detection with an efficient deep neural segmentation network and active vision. Combined with contributions to robust state estimation and risk-based control, our solution was able to reach speeds of ≈33 km/h (9.2m/s) and hereby more than triple the speeds seen in previous autonomous drone race competitions. This work analyses the performance of each component and discusses the implications for high-performance real-world AI applications with limited training time.
影响因子：
8.3
作者：
Thrun, Sebastian;Montemerlo, Mike;Mahoney, Pamela
通讯作者：
Mahoney, Pamela