Autonomous Drone Racing with Deep Reinforcement Learning (IROS 2021) скачать в хорошем качестве

Autonomous Drone Racing with Deep Reinforcement Learning (IROS 2021)

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. Reference: Y. Song*, M. Steinweg*, E. Kaufmann, D. Scaramuzza "Autonomous Drone Racing with Deep Reinforcement Learning" IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, 2021. PDF: http://rpg.ifi.uzh.ch/docs/IROS21_Yun... For more information about our research, visit these pages: 1. Drone Racing: http://rpg.ifi.uzh.ch/research_drone_... 2. Aggressive Flight: http://rpg.ifi.uzh.ch/aggressive_flig... 3. Machine Learning: http://rpg.ifi.uzh.ch/research_learni... Affiliations: Y. Song, M. Steinweg, E. Kaufmann, D. Scaramuzza are with the Robotics and Perception Group, Dep. of Informatics, University of Zurich, and Dep. of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland. M. Steinweg is also with RWTH Aachen University, Germany. Music Credits: Epic Inspiration from AShamaluevMusic