Abstract：To improve the real-time performance of trajectory planning for unmanned helicopters with tail rotor jamming, a flight trajectory planning method based on imitation learning is proposed. A nonlinear Model Predictive Control (MPC) trajectory planner is used to solve the optimal landing trajectory and is treated as the expert strategy. This planner is used to collect expert demonstration data in various scenarios to construct an imitation learning database. Subsequently, a deep neural network is built to perform behavior cloning, with data aggregation methods applied to enhance the network's performance, thereby enabling the imitation of the expert strategy. The proposed method can plan reasonable trajectories in various complex simulation scenarios, and the behavior cloning network has a shorter planning time compared to the nonlinear MPC trajectory planner, indicating better real-time performance.
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