An end-to-end neural network for UUV autonomous collision avoidance

This paper proposes a 3D autonomous collision avoidance method based on convolutional gated recurrent units to improve the autonomy of Unmanned Underwater Vehicle (UUV). The state equations of the UUV autonomous collision avoidance system are constructed by studying its mechanism and integrating dynamic/static obstacle recognition, dynamic obstacle motion prediction, collision risk assessment, and collision avoidance. Then a multi-input single-output neural network architecture that integrates static feature extraction, dynamic time sequence modeling, and feature integration is proposed based on Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to describe the state space. CNNs extract features from sonar observation data to improve the accuracy of obstacle recognition. GRUs are combined with CNNs to capture the correlation of longdistance features and extract dynamic features. The spatial and temporal invariance of the neural network architecture enhances the fault tolerance of the UUV collision avoidance system for inputs and adaptability to observation noise and environments. Finally, simulation results show that this method is adaptable to sonar observation noise and unknown environments to solve the problem of forward-looking sonar-based UUV collision avoidance in unknown complex ocean environments.