自主无人机竞速：综述
Autonomous Drone Racing: A Survey
AI总结：
该综述系统梳理自主无人机竞速中基于模型与基于学习的感知、规划、控制和状态估计方法，并总结高速飞行挑战及未来开放问题。
摘要：
在过去十年中，自主无人机系统在测绘、搜救以及最后一公里配送中的使用呈指数级增长。随着这些应用的兴起，迫切需要高度鲁棒、安全关键的算法，使无人机能够在复杂且不确定的环境中运行。此外，快速飞行可使无人机覆盖更大范围，从而提高生产率并进一步增强其应用价值。开发高速导航算法的一个替代任务是自主无人机竞速：研究人员通过编程，让无人机利用机载传感器和有限的计算能力，尽可能快地飞越一连串门障并避开障碍物。其速度和加速度分别超过80 kph和4 g，给感知、规划、控制和状态估计带来了重大挑战。为实现最高性能，系统需要实时算法，并且这些算法必须能够应对运动模糊、高动态范围、模型不确定性、空气动力学扰动以及往往不可预测的对手。本综述涵盖自主无人机竞速在基于模型方法和基于学习方法方面的发展历程。我们概述了该领域及其多年来的演进，并在最后总结未来需要面对的最大挑战和开放性问题。
英文摘要：
Over the last decade, the use of autonomous drone systems for surveying, search and rescue, or last-mile delivery has increased exponentially. With the rise of these applications comes the need for highly robust, safety-critical algorithms which can operate drones in complex and uncertain environments. Additionally, flying fast enables drones to cover more ground which in turn increases productivity and further strengthens their use case. One proxy for developing algorithms used in high-speed navigation is the task of autonomous drone racing, where researchers program drones to fly through a sequence of gates and avoid obstacles as quickly as possible using onboard sensors and limited computational power. Speeds and accelerations exceed over 80 kph and 4 g respectively, raising significant challenges across perception, planning, control, and state estimation. To achieve maximum performance, systems require real-time algorithms that are robust to motion blur, high dynamic range, model uncertainties, aerodynamic disturbances, and often unpredictable opponents. This survey covers the progression of autonomous drone racing across model-based and learning-based approaches. We provide an overview of the field, its evolution over the years, and conclude with the biggest challenges and open questions to be faced in the future.
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。
