A novel biomimetic sensor system for vibration source perception of autonomous underwater vehicles based on artificial lateral lines. (23rd October 2018)

• Record Type: Journal Article
• Authors: Liu, Guijie; Gao, Shuxian; Sarkodie-Gyan, Th; Li, Zhixiong
• Is Part Of: Measurement science & technology. Volume 29:Number 12(2018:Dec.)
• Publication Date: 2018-10-23
• Subjects: biomimetic sensor -- artificial lateral line -- vibration source perception -- autonomous underwater vehicles -- neural networks; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/aae128
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: The perception of vibration sources can be used to detect, classify, locate, and track autonomous underwater vehicles (AUVs), which is of great importance for ocean scientific research and naval applications. The artificial lateral lines system (ALLS) is a promising technique to sense underwater vibration sources. However, most current ALLS research focuses on perception mechanism and biomimetic sensor design. The design of a systematic ALLS that is ready for practical applications is still an unsolved problem. To this end, a novel biomimetic sensor system is proposed in this work for the purpose of developing a practical ALLS for AUVs. In order to determine the distribution of the developed biomimetic sensors in the AUVs, hydromechanics modelling and simulation of the artificial lateral lines were implemented to investigate the pressure response mechanisms of the AUVs in terms of the position, frequency and amplitude of the vibration source(s). Subsequently, an experimental AUV was equipped with biomimetic sensors to evaluate the performance of the vibration source perception. Experimental tests were conducted to analyze the relationship between the measured AUV pressure and the distance, frequency and amplitude of the vibration source. Analysis results demonstrate that the experimental measurements were consistent with simulation results. Based on the relationship between the sensor measurements and the vibration source, a neural network model was used to identify the coordinates, frequency and amplitude of the vibration source, producing an identification accuracy of 93%. Hence, the proposed ALLS is effective for vibration source perception of AUVs.