Robotics is the next frontier in the progress of Artificial Intelligence (AI), as the real world in which robots operate represents an enormous, complex, continuous state space with inherent real-time requirements. One extreme challenge in robotics is currently formed by autonomous drone racing. Human drone racers can fly through complex tracks at speeds of up to 190 km/h. Achieving similar speeds with autonomous drones signifies tackling fundamental problems in AI under extreme restrictions in terms of resources. In this article, we present the winning solution of the first AI Robotic Racing (AIRR) Circuit, a competition consisting of four races 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 their mental model of the drone's dynamics to achieve fast control. Our approach has a large focus on gate detection with an efficient deep neural segmentation network and active vision. Further, we make contributions to robust state estimation and risk-based control. This allowed us to reach speeds of ~9.2m/s in the last race, unrivaled by previous autonomous drone race competitions. Although our solution was the fastest and most robust, it still lost against one of the best human pilots, Gab707. The presented approach indicates a promising direction to close the gap with human drone pilots, forming an important step in bringing AI to the real world.
翻译:机器人是人工智能(AI)进步的下一个前沿,因为机器人操作的真正世界代表着一个庞大、复杂、连续的、具有内在实时要求的国家空间。机器人目前面临的一个极端挑战是由自主无人机赛跑形成的。人类无人机驾驶员可以以高达190公里/小时的速度飞过复杂的轨道。实现自主无人机的类似速度意味着在资源极端限制下解决AI的根本问题。在文章中,我们介绍了首个AI机器人运行的真正世界,这是一个由四种种族组成的竞赛,所有参与的团队都使用同样的无人机,而他们使用这种无人机的机会有限。我们的方法的核心在于人类飞行员如何把对赛门的杂音与无人机动态的智能模型结合起来,以快速控制的速度。我们的方法在很大程度上侧重于门的探测,同时有一个高效的深度神经分解网络和积极的视野。此外,我们为稳健的国家估计和基于风险的控制做出了贡献。这让我们得以在最后一场比赛中达到~9.2米/秒的速度,所有参加比赛的团队都使用同样的无人机,而他们只能有限地进入。我们的方法的核心是人类驾驶机,这是人类驾驶机最有希望的飞行,而最有希望的试的机向最有力的方向。