Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning
<|reference_start|>Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning: 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.<|reference_end|>
@article{xiao2024time-optimal,
    title={Time-optimal Flight in Cluttered Environments via Safe Reinforcement
  Learning},
    author={Wei Xiao, Zhaohan Feng, Ziyu Zhou, Jian Sun, Gang Wang, and Jie Chen},
    journal={arXiv preprint arXiv:2406.19646},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19646},
    primaryClass={cs.RO}
}