A Cooperative Hunting Method for Multi-USVs Based on Trajectory Prediction by OR-LSTM

Multi-unmanned surface vessel (USV) cooperative hunting is widely used in dynamic target hunting in the presence of obstacles, due to its high efficiency. This paper proposes a cooperative hunting method for multi-USVs based on trajectory prediction by obstacle relation long short-term memory (ORLSTM). First, the historical target trajectory is preprocessed by univariate nonlinear regression, and the obstacle information is input to OR-LSTM to predict the trajectory of the target, with improved accuracy and convergence. A bionic-based cooperative hunting strategy is proposed, by which multi-USVs ambush the predicted trajectory of the target by simulating the hunting strategy of lions. When the target is close to the ambush position, the multi-USVs start to hunt it. Two groups of simulation experiments were implemented on the Unity 3D platform to validate the proposed algorithm, which had higher accuracy and faster convergence in target trajectory prediction than the traditional algorithm, and higher efficiency in target hunting.