Research on Multi-sensor Information Fusion Method of Underwater Robot Based on Elman Neural Network

The precise positioning of underwater robots is the premise and foundation to complete other operations. Since a global positioning system (GPS) cannot be used underwater, and the positioning method of the underwater robot based on inertial navigation could cause significant errors, a multi-sensor information fusion method based on Elman neural network is proposed to solve these problems. The network is trained by taking data of doppler velocity log (DVL) and inertial measurement unit (IMU) as input and GPS as output. In the underwater area without GPS, the training network is used to predict the real-time position error of the acquired DVL and IMU data. The method can realize dynamic training and learning to improve the accuracy of the system. The experimental results show that the proposed method has lower positioning error than the traditional method, effectively inhibits the accumulation error of positioning, and improves underwater robots' positioning accuracy.