Bio-inspired underwater electrolocation through adaptive system identification

Abstract

Electrolocation is a method of sensing and navigating around nearby objects by probing the environment with a series of electrical pulses and measuring the response. This method, found in several species of electric fish, has the potential for faster response times and reduced scanning overheads when compared to traditional underwater location methods such as sonar. This work describes a biology-inspired model and process method for emulating this sensing modality. Previous work in this area uses parametric models, requiring the learning of many time-varying physical parameters. This limits the usability and adaptability of these methods. Instead of relying on complex physical models, we propose in this paper, a dynamic non-parametric model for underwater electrolocation which can be identified using existing system identification techniques. We further describe ways in which results from adaptive filtering and machine learning can be used to process incoming sensory information for electrolocation. We demonstrate the performance of the proposed improvements using an experimental aquatic testbed. Our experiments shows a 3× increase in the detection range.

Model based object localization and shape estimation using electric sense on underwater robots

“…The first, because turbidity decreases the visibility, the second because multiple echoes make the measured signals difficult to interpret. Few artificial electric sensors inspired by weakly electric fish exist today [Bai et al, 2012, Servagent et al, 2013, Dimble et al, 2014, Truong et al, 2015. In the paper we will use the device presented in [Servagent et al, 2013].…”