The sensing, state-estimation, and control behind the winning entry to the 2019 Artificial Intelligence Robotic Racing Competition

Abstract

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.