Underwater robots equipped with artificial electric sense for the exploration of unconventional aquatic niches

International audienceThis article presents different use of the electric field perception in the context of underwater robot navigation. To illustrate the developed navigation behaviours we will introduce a recently launched european project named subCULTron and will show some simulation and experimentation results. The project sub- CULTron aims at achieving long-term collective robot exploration and monitoring of underwater environments. The demonstration will take place in the lagoon of Venice, a large shallow embayment composed of salt turbib water that represents a challenging environment for underwater robots as common sensor like vision or acoustic are difficult to handle. To overcome turbidity and confinement problems our robots will be equipped with artificial electric sensors that will be used as the main sensorial modality for navigation. Electric sense is a bio-inspired sense that has been developed by several species of fish living in turbib and confined underwater environment. In this paper, many different robotic behaviours based on the electric field perception will be presented, in particular we will address reactive navigation, object/robots detection, and object localization and estimation

First results on a sensor bio-inspired by electric fish

This article presents the first results of a work which aims at designing an active sensor inspired by the electric fish. Its interest is its potential for robotics underwater navigation and exploration tasks in conditions where vision and sonar would meet difficulty. It could also be used as a complementary omnidirectional, short range sense to vision and sonar. Combined with a well defined engine geometry, this sensor can be modeled analytically. In this article, we focus on a particular measurement mode where one electrode of the sensor acts as a current emitter and the others as current receivers. In spite of the high sensitivity required by electric sense, the first results show that we can obtain a detection range of the order of the sensor length, which suggests that this sensor principle could be used in future for robotics obstacle avoidance

Development of a multi-modal tactile force sensing system for deep-sea applications

With the increasing demand for autonomy in robotic systems, there is a rising need for sensory data sensed via different modalities. In this way system states and the aspects of unstructured environments can be assessed in the most detailed fashion possible, thus providing a basis for making decisions regarding the robotâ s task. Com- pared to other sensing modalities, the sense of touch is underrepresented in todayâ s robots. That is where this thesis comes in. A tactile sensing system is developed that combines several modalities of contact sensing. The use of the tactile sense in robotic grippers is of great relevance especially for robotic systems in the deep sea. Up to now manipulation systems in master-slave control mode have been used in this area of application. An operator performing the manipulation task has to rely on visual feedback coming from cameras. Working on the oceanâ s seafloor means having to cope with conditions of limited visibility caused by swirled-up sediment

Sensor model for the navigation of underwater vehicles by the electric sense

International audienceWe present an analytical model of a sensor for the navigation of underwater vehicles by the electric sense. This model is inspired from the electroreception structure of the electric fish. In our model, that we call the poly-spherical model (PSM), the sensor is composed of n spherical electrodes. Some electrodes play the role of current-emitters whereas others play the role of current-receivers. By imposing values of the electrical potential on each electrode we create an electric field in the vicinity of the sensor. The region where the electric field is created is considered as the bubble of perception of the sensor. Each object that enters this bubble is electrically polarized and creates in return a perturbation. This perturbation induces a variation of the measured current by the sensor. The model is tested on objects for which the expression of the polarizability is known. A unique off-line calibration of the poly-spherical model permits to predict the measured current of a real immersed sensor in an aquarium. Comparisons in a basic scene between the predicted current given by the poly-spherical model and the measured current given by our test bed show a very good agreement, which confirms the interest of using such fast analytical models for the purpose of navigation

Human factors in space telepresence

The problems of interfacing a human with a teleoperation system, for work in space are discussed. Much of the information presented here is the result of experience gained by the M.I.T. Space Systems Laboratory during the past two years of work on the ARAMIS (Automation, Robotics, and Machine Intelligence Systems) project. Many factors impact the design of the man-machine interface for a teleoperator. The effects of each are described in turn. An annotated bibliography gives the key references that were used. No conclusions are presented as a best design, since much depends on the particular application desired, and the relevant technology is swiftly changing

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

GRAINS: Proximity Sensing of Objects in Granular Materials

Proximity sensing detects an object's presence without contact. However, research has rarely explored proximity sensing in granular materials (GM) due to GM's lack of visual and complex properties. In this paper, we propose a granular-material-embedded autonomous proximity sensing system (GRAINS) based on three granular phenomena (fluidization, jamming, and failure wedge zone). GRAINS can automatically sense buried objects beneath GM in real-time manner (at least ~20 hertz) and perceive them 0.5 ~ 7 centimeters ahead in different granules without the use of vision or touch. We introduce a new spiral trajectory for the probe raking in GM, combining linear and circular motions, inspired by a common granular fluidization technique. Based on the observation of force-raising when granular jamming occurs in the failure wedge zone in front of the probe during its raking, we employ Gaussian process regression to constantly learn and predict the force patterns and detect the force anomaly resulting from granular jamming to identify the proximity sensing of buried objects. Finally, we apply GRAINS to a Bayesian-optimization-algorithm-guided exploration strategy to successfully localize underground objects and outline their distribution using proximity sensing without contact or digging. This work offers a simple yet reliable method with potential for safe operation in building habitation infrastructure on an alien planet without human intervention.Comment: 35 pages, 5 figures,2 tables. Videos available at https://sites.google.com/view/grains2/hom