Abstract
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: this https URL.
Abstract (translated)
微型飞行器（MAV）在未知且复杂环境中的自主飞行由于保守的机动策略，对于时间关键任务来说仍然具有挑战性。本文提出了一种集成规划与控制框架，用于在复杂环境中进行高速、时间最优的MAV自主飞行。在每次重新规划周期（100 Hz）中，生成一条基于多项式的时序最优轨迹作为参考，并通过模仿学习加速了时间分配过程。随后，一个时序最优模型预测轮廓控制器（MPCC）结合可变视距步骤的安全飞行走廊（SFC）约束，实现激进且安全的机动操作，同时充分利用MAV的动力学特性。 我们在一款自定义构建的LiDAR基MAV平台上广泛验证了所提出的框架。模拟结果显示，在与现有技术相比时表现出更强的激进性，而实际实验则在复杂环境中实现了18米/秒的峰值速度，并从多个起点连续成功进行了十次试验。视频可在此链接查看：[此URL]（请将"this https URL"替换为实际提供的URL）。
URL
https://arxiv.org/abs/2601.07434
