Autonomous Drone Racing with Deep Reinforcement Learning

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.

arXiv commentThis paper has been accepted for publication at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, 2021. Copyright @ IEEE

References in corpus (5)

• Optimization-Based Autonomous Racing of 1:43 Scale RC Cars
• Time-Optimal Planning for Quadrotor Waypoint Flight
• FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation
• AirSim Drone Racing Lab
• CPC: Complementary Progress Constraints for Time-Optimal Quadrotor Trajectories

Cited by in corpus (4)

• Towards Time-Optimal Tunnel-Following for Quadrotors
• Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning
• Fast-Racing: An Open-source Strong Baseline for SE(3) Planning in Autonomous Drone Racing
• Robustifying Reinforcement Learning Policies with ${\mathcal{L}}_1$ Adaptive Control