动态环境中自主无人机竞速的基于多项式的在线规划
Polynomial-based Online Planning for Autonomous Drone Racing in Dynamic Environments
AI总结：
针对动态环境无人机竞速的在线重规划难题，提出基于多项式轨迹表示的优化框架，通过硬约束保障航点穿越精度，并行多拓扑规划避免局部最优，在DJI Robomaster赛事中夺冠且完赛时间不到第二名一半。
摘要：
近年来，自主无人机竞速取得了显著进展。然而，主要关注点在于追求执行时间，而对动态环境的挑战关注甚少。竞速场景的高速特性，加上不可预见的环境变化，对在线重规划及其时效性提出了严格要求。针对动态环境中的竞速问题，我们提出了一种具有高效多项式轨迹表示的在线重规划框架。我们基于优化方法在激进速度与灵活避障之间进行权衡。此外，为了在穿越中间竞速航点时确保安全性和精确性，我们在规划过程中将该需求设定为硬约束。对于动态障碍物，基于工程考量设计了并行多拓扑轨迹规划，以防止因局部最优而导致竞速时间损失。该框架被集成到一个四旋翼系统中，并在DJI Robomaster Intelligent UAV Championship中成功演示，在该比赛中它成功完成了赛道并获得第一名，完赛时间不到第二名的一半。
英文摘要：
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.
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。
