Autonomous drone racing has gained attention for its potential to push the boundaries of drone navigation technologies. While much of the existing research focuses on racing in obstacle-free environments, few studies have addressed the complexities of obstacle-aware racing, and approaches presented in these studies often suffer from overfitting, with learned policies generalizing poorly to new environments. This work addresses the challenge of developing a generalizable obstacle-aware drone racing policy using deep reinforcement learning. We propose applying domain randomization on racing tracks and obstacle configurations before every rollout, combined with parallel experience collection in randomized environments to achieve the goal. The proposed randomization strategy is shown to be effective through simulated experiments where drones reach speeds of up to 70 km/h, racing in unseen cluttered environments. This study serves as a stepping stone toward learning robust policies for obstacle-aware drone racing and general-purpose drone navigation in cluttered environments. Code is available at https://github.com/ErcBunny/IsaacGymEnvs.

翻译：自主无人机竞速因其推动无人机导航技术边界的潜力而备受关注。现有研究大多聚焦于无障碍环境下的竞速，针对障碍感知竞速复杂性的研究较少，且这些研究中提出的方法常存在过拟合问题，所学策略在新环境中的泛化能力较差。本研究致力于利用深度强化学习开发一种可泛化的障碍感知无人机竞速策略。我们提出在每次轨迹生成前对竞速赛道和障碍物配置进行领域随机化，并结合在随机化环境中的并行经验收集来实现目标。通过仿真实验证明，所提出的随机化策略是有效的：无人机在未见过的杂乱环境中以高达70公里/小时的速度完成竞速。本研究为学习障碍感知无人机竞速及杂乱环境中通用无人机导航的鲁棒策略奠定了基础。代码发布于 https://github.com/ErcBunny/IsaacGymEnvs。