学习面向障碍感知的自主无人机竞速的泛化策略
Learning Generalizable Policy for Obstacle-Aware Autonomous Drone Racing
AI总结：
本研究利用深度强化学习，通过域随机化和并行经验收集，开发了一种可泛化的障碍感知无人机竞速策略，在模拟实验中实现最高70公里/小时的速度，并开源代码。
摘要：
自主无人机竞速因其推动无人机导航技术发展的潜力而受到关注。虽然现有研究大多聚焦于无障碍环境中的竞速，但很少有研究涉及障碍感知竞速的复杂性，且这些研究提出的方法往往存在过拟合问题，导致学习到的策略在新环境中泛化能力差。本研究利用深度强化学习，致力于开发一种可泛化的障碍感知无人机竞速策略。我们提出在每次环境重置前对竞速赛道和障碍物配置进行域随机化，并结合在随机化环境中的并行经验收集来实现这一目标。通过模拟实验验证了所提出的随机化策略的有效性，实验中无人机在未见过的杂乱环境中竞速，速度最高可达70公里/小时。这项研究为学习障碍感知无人机竞速以及杂乱环境中通用无人机导航的鲁棒策略奠定了基础。代码可在https://github.com/ErcBunny/IsaacGymEnvs获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
