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This paper introduces LOONG, a novel planning and control framework for time-optimal MAV flight in cluttered environments. The framework combines imitation learning to accelerate time allocation for polynomial trajectory generation with a time-optimal model predictive contouring control (MPCC) that incorporates safe flight corridor (SFC) constraints. Experimental results on a LiDAR-based MAV platform demonstrate superior aggressiveness and a peak speed of 18 m/s in real-world cluttered environments, showcasing the framework's robustness.
Achieve time-optimal MAV flight at 18 m/s in cluttered environments by combining imitation learning with model predictive contouring control.
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.
