Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917708065406976 |
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| author | Xiao, Wei Feng, Zhaohan Zhou, Ziyu Sun, Jian Wang, Gang Chen, Jie |
| author_facet | Xiao, Wei Feng, Zhaohan Zhou, Ziyu Sun, Jian Wang, Gang Chen, Jie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19646 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning Xiao, Wei Feng, Zhaohan Zhou, Ziyu Sun, Jian Wang, Gang Chen, Jie Robotics 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. |
| title | Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.19646 |