In recent years, there is a noteworthy advancement in autonomous drone racing. However, the primary focus is on attaining execution times, while scant attention is given to the challenges of dynamic environments. The high-speed nature of racing scenarios, coupled with the potential for unforeseeable environmental alterations, present stringent requirements for online replanning and its timeliness. For racing in dynamic environments, we propose an online replanning framework with an efficient polynomial trajectory representation. We trade off between aggressive speed and flexible obstacle avoidance based on an optimization approach. Additionally, to ensure safety and precision when crossing intermediate racing waypoints, we formulate the demand as hard constraints during planning. For dynamic obstacles, parallel multi-topology trajectory planning is designed based on engineering considerations to prevent racing time loss due to local optimums. The framework is integrated into a quadrotor system and successfully demonstrated at the DJI Robomaster Intelligent UAV Championship, where it successfully complete the racing track and placed first, finishing in less than half the time of the second-place.

翻译：近年来，自主无人机竞速领域取得了显著进展。然而，现有研究主要聚焦于缩短执行时间，对动态环境挑战的关注较少。竞速场景的高速特性与潜在的环境不可预见变化，对在线重规划及其时效性提出了严苛要求。针对动态环境下的竞速任务，我们提出了一种基于高效多项式轨迹表征的在线重规划框架，通过优化方法在激进速度与灵活避障之间寻求平衡。此外，为确保穿越中间竞速航点时的安全性与精准性，我们将该需求建模为规划过程中的硬约束。针对动态障碍物，基于工程考量设计了并行多拓扑轨迹规划策略，以规避局部最优导致的竞速时间损失。该框架已集成至四旋翼无人机系统，并在大疆RoboMaster智能无人机锦标赛中成功验证：系统以不足第二名一半的用时完成赛道并夺得冠军。