An underwater electrosensory membrane bio-inspired by weakly electric fish

Artificial sensory system is promising to navigate in cluttered and turbid underwater environment by achieving similar functions of biological electrosense of weakly electric fish. In this paper we designed an electrosensory membrane that can operate in a full 3-dimensional mode. Algorithms on the object localization were also designed and tested based on numerical methods of electric field forward simulation. We combined the statistic learning method by training a multi-layer neural network and a probabilistic approach by applying a constrained unscented Kalman filter (CUKF). This exploits the merits of fast estimation and precise signal marching process. Experimental results showed that the detection and localization with the reported sensor were quick and accurate, with errors of around 10 mm using one-step neural network mapping and about 5 mm in close-range using CUKF. This work demonstrated the effectiveness of proposed electrosensory membrane and algorithms.