Control-Oriented Real-Time Trajectory Planning for Heterogeneous UAV Formations

Aiming at the trajectory planning problem for heterogeneous UAV formations in complex environments, a trajectory prediction model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTM) is designed, and a real-time trajectory planning method is proposed based on this model. By pre-training trajectory prediction networks for various types of UAVs, the traditional physics-based models are replaced for flight trajectory prediction. Inspired by Model Predictive Control (MPC), in the trajectory planning stage, the method generates multi-step trajectory points using an improved artificial potential field (APF) method, estimates the actual formation trajectory using the prediction network, and optimizes the trajectory through a multi-objective particle swarm optimization (MOPSO) algorithm after evaluating the planning costs. During actual flight, the optimized parameters generate trajectory points for the formation to follow. Unlike conventional path planning based on simple constraints, the proposed method directly plans trajectory points based on trajectory tracking performance, ensuring high feasibility for the formation to follow. Experimental results show that the CNN-LSTM network outperforms other networks in both short-term and long-term trajectory prediction. The proposed trajectory planning method demonstrates significant advantages in formation maintenance, trajectory tracking, and real-time obstacle avoidance, ensuring flight stability and safety while maintaining high-speed flight.