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Autonomous Drone Racing with Deep Reinforcement Learning

2021-03-15 18:05:49
Yunlong Song, Mats Steinweg, Elia Kaufmann, Davide Scaramuzza
arXiv_AI
             
Abstract

In many robotic tasks, such as drone racing, the goal is to travel through a set of waypoints as fast as possible. A key challenge for this task is planning the minimum-time trajectory, which is typically solved by assuming perfect knowledge of the waypoints to pass in advance. The resulting solutions are either highly specialized for a single-track layout, or suboptimal due to simplifying assumptions about the platform dynamics. In this work, a new approach to minimum-time trajectory generation for quadrotors is presented. Leveraging deep reinforcement learning and relative gate observations, this approach can adaptively compute near-time-optimal trajectories for random track layouts. Our method exhibits a significant computational advantage over approaches based on trajectory optimization for non-trivial track configurations. The proposed approach is evaluated on a set of race tracks in simulation and the real world, achieving speeds of up to 17 m/s with a physical quadrotor.

Abstract (translated)

URL

https://arxiv.org/abs/2103.08624

PDF

https://arxiv.org/pdf/2103.08624.pdf

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