Design and Implementation of Bio-inspired Underwater Electrosense

Underwater electrosense, manipulating underwater electric field for sensing purpose, is a growing technology bio-inspired by weakly electric fish that can navigate in dark or cluttered water. We studied its theoretical foundations and developed sophisticated sensing algorithms including some first-introduced techniques such as discrete dipole approximation (DDA) and convolutional neural networks (CNN), which were tested and validated by simulation and a planar sensor prototype. This work pave a solid way to applications on practical underwater robots

Localization, Mapping and SLAM in Marine and Underwater Environments

The use of robots in marine and underwater applications is growing rapidly. These applications share the common requirement of modeling the environment and estimating the robots’ pose. Although there are several mapping, SLAM, target detection and localization methods, marine and underwater environments have several challenging characteristics, such as poor visibility, water currents, communication issues, sonar inaccuracies or unstructured environments, that have to be considered. The purpose of this Special Issue is to present the current research trends in the topics of underwater localization, mapping, SLAM, and target detection and localization. To this end, we have collected seven articles from leading researchers in the field, and present the different approaches and methods currently being investigated to improve the performance of underwater robots

An underwater electrosensor for identifying objects of similar volume and aspect ratio using convolutional neural network

© 2017 IEEE. Underwater electrosense is bio-inspired by weakly electric fishes that use an electric field to see the objects in the water. Current studies on engineering electrosense focus on designing sophisticated sensors and algorithms for emulating biological functions including localization and identification. This work aimed to develop a planar sensor equipped with a dense electrode array that is capable of providing accurate and dense data for identifying objects of similar volume and aspect ratio, which has been a challenge in underwater sensing. After sensor design and implementation were presented, convolutional neural networks (CNN), which are widely used in digital image recognition, was trained using both simulation and experimental data. In the simulation, the overall success rate on identifying the sphere, cube, and rod is 92.6% by a 28 × 28 electrode array. In the preliminary experimental tests, a sensor with 16 × 16 electrode array achieved an overall success rate of 90.4% on identifying a sphere and a rod