Artificial lateral line canal for hydrodynamic detection

Fish use their lateral line system to detect minute water motions. The lateral line consists of superficial neuromasts and canal neuromasts. The response properties of canal neuromasts differ from those of superficial ones. Here, we report the design, fabrication, and characterization of an artificial lateral line canal system. The characterization was done under various fluid conditions, including dipolar excitation and turbulent flow. The experimental results with dipole excitation match well with a mathematical model. Canal sensors also demonstrate significantly better noise immunity compared with superficial ones. Canal-type artificial lateral lines may become important for underwater flow sensing

Artificial lateral line canal for hydrodynamic detection

Fish use their lateral line system to detect minute water motions. The lateral line consists of superficial neuromasts and canal neuromasts. The response properties of canal neuromasts differ from those of superficial ones. Here, we report the design, fabrication, and characterization of an artificial lateral line canal system. The characterization was done under various fluid conditions, including dipolar excitation and turbulent flow. The experimental results with dipole excitation match well with a mathematical model. Canal sensors also demonstrate significantly better noise immunity compared with superficial ones. Canal-type artificial lateral lines may become important for underwater flow sensing

Flow Vision for Autonomous Underwater Vehicles via an Artificial Lateral Line

Most fish have the capability of sensing flows and nearby movements even in dark or murky conditions by using the lateral line organs. This enables them to perform a variety of underwater activities, such as localizing prey, avoiding predators, navigating in narrow spaces, and schooling. To emulate this capability for Autonomous Underwater Vehicles, we developed an artificial lateral line using an array of Micro-Electro-Mechanical-Systems (MEMS) flow sensors. The signals collected via the artificial lateral line are then processed by an adaptive beamforming algorithm developed from Capon\u27s method. The system produces 3D images of source locations for different hydrodynamic activities, including the vibration of a dipole source and the movement of a tail-flicking crayfish. A self-calibration algorithm provides the capability of self-adaptation to different environments. Lastly, we give a Cramer-Rao bound on the theoretical performance limit which is consistent with experimental results

Artificial lateral-line system for imaging dipole sources using Beamforming techniques

AbstractIn nature, fish have the ability to localize prey, school, navigate, etc. using the lateral-line organ [1]. Here we present the use of biomimetic artificial hair-based flow-sensors arranged as lateral-line system in combination with beamforming techniques for dipole source localization in air. Modelling and measurement results show the artificial lateral-line ability to image the position of dipole sources accurately. Such systems open possibilities for flow-based near-field environment mapping which can be beneficial e.g. to robot guidance applications

Performance of Neural Networks in Source Localization using Artificial Lateral Line Sensor Configurations

Artificial lateral lines (ALL) are used to detect the movement and locations of sources underwater, and are based on the lateral line organ found in fish and amphibians. Experiments have been performed to evaluate if the localization performance of neural networks, trained on simulated ALL sensor data, can be improved through adjustments of the internal ALL sensor positions. A Cramér-Rao lower bound analysis was performed on a subset of handpicked sensor configurations to estimate the likely performance of various configurations. The best and worst configurations were used to generate simulated datasets with which extreme learning machines (ELMs) and convolutional neural networks (CNNs) were trained and tested on their location accuracy. Simulated datasets consisted of two sources in a three-dimensional basin and the sensor readings of 16 ALL sensors. Results show that the best performing configuration consists of improved ELM and CNN localization performances, while also demonstrating that ELMs are capable of localizing multiple sources in three-dimensional aquatic environments, with comparable if not better results than CNNs

Recurrent neural networks for hydrodynamic imaging using a 2D-sensitive artificial lateral line

The lateral line is a mechanosensory organ found in fish and amphibians that allows them to sense and act on their near-field hydrodynamic environment. We present a 2D-sensitive Artificial lateral line (ALL) comprising eight all-optical flow sensors, which we use to measure hydrodynamic velocity profiles along the sensor array in response to a moving object in its vicinity. We then use the measured velocity profiles to reconstruct the objects location, via two types of neural networks: feed-forward and recurrent. Several implementations of feed-forward neural networks for ALL source localisation exist, while recurrent neural networks may be more appropriate for this task. The performance of a recurrent neural network (the Long Short-Term Memory, LSTM) is compared to that of a feed-forward neural network (the Online-Sequential Extreme Learning Machine, OS-ELM) via localizing a 6 cm sphere moving at 13 cm/s. Results show that, in a 62 cm × 9.5 cm area of interest, the LSTM outperforms the OS-ELM with an average localisation error of 0.72 cm compared to 4.27 cm respectively. Furthermore, the recurrent network is relatively less affected by noise, indicating that recurrent connections can be beneficial for hydrodynamic object localisation

Vortex sensing and energy expenditure of fish exposed to unsteady flow and biomimetic transfer of noise filter and signal amplification techniques

Biomimetics is a promising field of research in which natural processes and structures are transfered to technical applications. Part one and two of this work investigate the behavior and the hydrodynamic sense of fish to uncover strategies for locomotory cost reduction and navigation in unsteady flow. Fish detect unsteady flow with their lateral line organ. Trout and rudd were exposed to an unsteady flow signature caused by submerged cylinders. Trout preferred distinct three dimensional regions relative to the cylinder and showed less muscle activity than in non-preferential regions. Interestingly one of those regions had a three dimensional flow signature compared to the almost two dimensional flow pattern of a vortex street. Navigation in complex flow requires sensors and smart signal processing. This work shows that the activity of central neurons of rudd involved in the processing of hydrodynamic information correlated with unsteady flow signatures in terms of spike pattern or spike rate or both. It would be advantageous for robots to use an artificial hydrodynamic sense together with natural inspired signal processing to reduce locomotory costs and also navigate in complex hydrodynamic habitats. In sensory systems it is crucial to separate meaningful signals from noise which may occur in complex habitats. Nature has developed a stunning diversity of lateral line morphologies. The function is not well understood. Part three and four of this work investigate several lateral line morphologies with respect to noise filtering and signal amplification mechanisms. To do so an artificial lateral line sensor was used. Therefore it was possible to gain control of all parameters. An artificial lateral line design was found which significantly enhances signal to noise ratio and therefore this work uncovers the function of this natural morphological occurrence. Additionally a finite element model with fluid structure interaction was used to develop a signal enhancing design of artificial neuromasts for fabrication with microelectromechanical systems. Interestingly a similar structure occurs in nature. Last but not least in part five this work shows an eight channel artificial lateral line canal sensor available with automatic fabrication techniques