用分阶段强化学习让无人机在有随机障碍的赛道上又快又稳地飞行。
Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles
- 分阶段训练+动态环境随机化，逐步提升避障与穿越能力。
- 真实飞行实验中速度更快、成功率更高，优于现有方法。
- 适合想做无人机自主飞行或强化学习应用的研究者。
自主无人机竞速因探索敏捷飞行极限而受到广泛关注。然而，现有研究多聚焦于无障碍赛道，对障碍带来的感知与动态挑战关注不足，导致实际飞行中成功率低、鲁棒性差。为此，我们提出一种基于视觉的分阶段强化学习框架，训练出能在未知障碍环境中稳健飞行的控制器。通过多阶段课程学习、领域随机化和多场景更新策略，有效应对避障与穿越门框之间的冲突挑战。端到端控制策略采用单一网络实现，支持四旋翼在含变障碍物环境中高速飞行。软硬件联合测试及真实世界实验表明，该方法在完成时间与成功率上均优于现有方法，显著提升了复杂环境下的无人机竞速性能。视频与代码已公开：https://github.com/SJTU-ViSYS-team/CRL-Drone-Racing。
原文摘要 · Abstract (English)
Autonomous drone racing has attracted increasing interest as a research topic for exploring the limits of agile flight. However, existing studies primarily focus on obstacle-free racetracks, while the perception and dynamic challenges introduced by obstacles remain underexplored, often resulting in low success rates and limited robustness in real-world flight. To this end, we propose a novel vision-based curriculum reinforcement learning framework for training a robust controller capable of addressing unseen obstacles in drone racing. We combine multi-stage cu rriculum learning, domain randomization, and a multi-scene updating strategy to address the conflicting challenges of obstacle avoidance and gate traversal. Our end-to-end control policy is implemented as a single network, allowing high-speed flight of quadrotors in environments with variable obstacles. Both hardware-in-the-loop and real-world experiments demonstrate that our method achieves faster lap times and higher success rates than existing approaches, effectively advancing drone racing in obstacle-rich environments. The video and code are available at: https://github.com/SJTU-ViSYS-team/CRL-Drone-Racing.
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