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arXiv · 2103.08624

Autonomous Drone Racing with Deep Reinforcement Learning

Yunlong Song·Mats Steinweg·Elia Kaufmann·Davide Scaramuzza
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Abstract

In many robotic tasks, such as autonomous 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 time-optimal trajectory, which is typically solved by assuming perfect knowledge of the waypoints to pass in advance. The resulting solution is 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 near-time-optimal trajectory generation for quadrotors is presented. Leveraging deep reinforcement learning and relative gate observations, our approach can compute near-time-optimal trajectories and adapt the trajectory to environment changes. Our method exhibits computational advantages 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 60 km/h with a physical quadrotor.

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Yunlong Song, Mats Steinweg, Elia Kaufmann, Davide Scaramuzza. 2021-03-15. Autonomous Drone Racing with Deep Reinforcement Learning. https://arxiv.org/abs/2103.08624

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