让微型无人机在复杂环境高速飞行，实现安全与速度兼顾的实时自主导航。
LOONG: Online Time-Optimal Autonomous Flight for MAVs in Cluttered Environments
- 用模仿学习加速时间最优轨迹生成，每秒100次重规划。
- 实测最高时速达18米/秒，在复杂环境中连续10次成功飞行。
- 适合需要高速、高动态飞行的无人机研发与竞赛场景。
微型飞行器（MAV）在未知、复杂环境中的自主飞行对时间敏感任务仍具挑战性，主要因保守的机动策略。本文提出一种集成规划与控制的框架，实现高动态、时间最优的自主飞行。每个重规划周期（100 Hz）内，通过多项式表示生成时间最优轨迹，并利用模仿学习加速时间分配过程。随后，采用时间最优模型预测轮廓控制（MPCC），在可变控制时域中引入安全飞行走廊（SFC）约束，实现激进且安全的机动，充分挖掘飞行器动力学性能。我们在自建的基于激光雷达的MAV平台上全面验证该框架。仿真结果表明，相比现有方法更具攻击性；真实实验达到18米/秒的峰值速度，从不同起点连续10次成功完成飞行任务。
原文摘要 · Abstract (English)
Autonomous flight of micro air vehicles (MAVs) in unknown, cluttered environments remains challenging for time-critical missions due to conservative maneuvering strategies. This article presents an integrated planning and control framework for high-speed, time-optimal autonomous flight of MAVs in cluttered environments. In each replanning cycle (100 Hz), a time-optimal trajectory under polynomial presentation is generated as a reference, with the time-allocation process accelerated by imitation learning. Subsequently, a time-optimal model predictive contouring control (MPCC) incorporates safe flight corridor (SFC) constraints at variable horizon steps to enable aggressive yet safe maneuvering, while fully exploiting the MAV's dynamics. We validate the proposed framework extensively on a custom-built LiDAR-based MAV platform. Simulation results demonstrate superior aggressiveness compared to the state of the art, while real-world experiments achieve a peak speed of 18 m/s in a cluttered environment and succeed in 10 consecutive trials from diverse start points. The video is available at the following link: https://youtu.be/vexXXhv99oQ.
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