@article{Xiao2026, 
author = {Wei Xiao and Zhaohan Feng and Ziyu Zhou and Jian Sun and Gang Wang and Jie Chen},
title = {Time-Optimal Flight in Cluttered Environments via Safe Reinforcement Learning},
year = {2026},
journal = {Unmanned Systems},
volume = {14},
number = {2},
pages = {391-400},
keywords = {Reinforcement learning, time-optimal flight, obstacle avoidance},
url = {https://www.sciopen.com/article/10.1142/S230138502650007X},
doi = {10.1142/S230138502650007X},
abstract = {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.}
}