2024/06/28 by Wei Xiao, Xiao, Wei, Zhaohan Feng +9 · 1 citation
Engineering · #Aeroelasticity and Vibration Control #Aerospace and Aviation Technology #Biomimetic flight and propulsion mechanisms #FOS: Computer and information sciences #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2406.19646
openalex publication_date 2024/06/28 · openalex created_date 2024/07/02 · openalex updated_date 2026/07/28
This paper addresses the problem of guiding a quadrotor through a predefined sequence of waypoints in cluttered environments, aiming to minimize the flight time while avoiding collisions. Previous approaches either suffer from prolonged computational time caused by solving complex non-convex optimization problems or are limited by the inherent smoothness of polynomial trajectory representations, thereby restricting the flexibility of movement. In this work, we present a safe reinforcement learning approach for autonomous drone racing with time-optimal flight in cluttered environments. The reinforcement learning policy, trained using safety and terminal rewards specifically designed to enforce near time-optimal and collision-free flight, outperforms current state-of-the-art algorithms. Additionally, experimental results demonstrate the efficacy of the proposed approach in achieving both minimum flight time and obstacle avoidance objectives in complex environments, with a commendable 66.7% success rate in unseen, challenging settings.