为随机障碍物的四旋翼赛车设计的课程强化学习
Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles
AI总结：
本文提出一种课程强化学习框架，通过多阶段学习和领域随机化提升四旋翼赛车在随机障碍物环境中的鲁棒性和速度
摘要：
自主无人机赛车已成为探索敏捷飞行极限的研究课题。然而，现有研究主要集中在无障碍物的赛道上，而障碍物引入的感知和动态挑战仍被忽视，往往导致现实飞行中的低成功率和有限的鲁棒性。为此，我们提出了一种新的基于视觉的课程强化学习框架，用于训练一个能够应对未见障碍物的稳健控制器。我们结合多阶段课程学习、领域随机化和多场景更新策略，以解决避障和通过门的冲突挑战。我们的端到端控制策略作为单一网络实现，允许在具有可变障碍物的环境中实现高速飞行。硬件在环和现实世界实验表明，我们的方法在赛车时间上比现有方法更快，成功率更高，有效推进了障碍物丰富的无人机赛车。视频和代码可在：https://github.com/SJTU-ViSYS-team/CRL-Drone-Racing获取。
英文摘要：
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.
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。
