تقرير
Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning
| العنوان: | Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning |
|---|---|
| المؤلفون: | Xiao, Wei, Feng, Zhaohan, Zhou, Ziyu, Sun, Jian, Wang, Gang, Chen, Jie |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics |
| الوصف: | 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. Comment: 7 pages, 3 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.19646 |
| رقم الأكسشن: | edsarx.2406.19646 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |