The development of launch and recovery technology is key for the application to the unmanned surface vehicle (USV). Also, a launch and recovery system (L&RS) based on a pneumatic ejection mechanism has been developed in our previous study. To improve the launch accuracy and reduce the influence of the sea waves, we propose a stacking model of one-dimensional convolutional neural network and long short-term memory neural network predicting the attitude of the USV. The data from experiments by "Jinghai VII" USV developed by Shanghai University, China, under levels 1-4 sea conditions are used to train and test the network. The results show that the stabilized platform with the proposed prediction method can keep the launching angle of the launching mechanism constant by regulating the pitching joint and rotation joint under the random influence from the wave. Finally, the efficiency and effectiveness of the L&RS are demonstrated by the successful application in actual environments.
Keywords: RS); amp; attitude prediction; convolutional neural network (CNN); launch and recovery system (L& long short-term memory (LSTM) neural network; unmanned surface vehicle (USV).