Autonomous Drone Racing with Deep Reinforcement Learning
- URL: http://arxiv.org/abs/2103.08624v1
- Date: Mon, 15 Mar 2021 18:05:49 GMT
- Title: Autonomous Drone Racing with Deep Reinforcement Learning
- Authors: Yunlong Song, Mats Steinweg, Elia Kaufmann, and Davide Scaramuzza
- Abstract summary: 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 is planning the minimum-time trajectory, which is typically solved by assuming perfect knowledge of the waypoints to pass in advance.
In this work, a new approach to minimum-time trajectory generation for quadrotors is presented.
- Score: 39.757652701917166
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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