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.

翻译：自主无人机竞速作为探索敏捷飞行极限的研究课题已引起日益广泛的关注。然而，现有研究主要集中于无障碍物的赛道，而障碍物带来的感知与动态挑战尚未得到充分探索，这往往导致实际飞行中的成功率较低且鲁棒性有限。为此，我们提出了一种新颖的基于视觉的课程强化学习框架，用于训练能够处理无人机竞速中未见障碍物的鲁棒控制器。我们结合多阶段课程学习、领域随机化以及多场景更新策略，以应对避障与穿越门框这两个相互冲突的挑战。我们的端到端控制策略通过单一网络实现，使四旋翼无人机能够在具有可变障碍物的环境中进行高速飞行。硬件在环实验和真实世界实验均表明，与现有方法相比，我们的方法实现了更快的单圈时间和更高的成功率，有效推动了障碍物密集环境下的无人机竞速研究。视频与代码发布于：https://github.com/SJTU-ViSYS-team/CRL-Drone-Racing。