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.

翻译：在未知杂乱环境中，微小型飞行器（MAV）的自主飞行对于时间关键型任务而言仍具挑战性，这主要源于保守的机动策略。本文提出了一种集成规划与控制框架，用于在杂乱环境中实现MAV的高速、时间最优自主飞行。在每个重规划周期（100 Hz）中，生成多项式表示下的时间最优轨迹作为参考，并通过模仿学习加速时间分配过程。随后，一种时间最优模型预测轮廓控制（MPCC）在可变预测步长中结合安全飞行走廊（SFC）约束，以实现激进且安全的机动，同时充分利用MAV的动态性能。我们在定制的基于激光雷达的MAV平台上对所提框架进行了广泛验证。仿真结果表明，相较于现有技术，该方法展现出更优的激进性；同时，真实世界实验在杂乱环境中达到了18 m/s的峰值速度，并在10次不同起点的连续试验中均取得成功。视频可通过以下链接获取：https://youtu.be/vexXXhv99oQ。