Fish-inspired tracking of underwater turbulent plumes

Autonomous ocean-exploring vehicles have begun to take advantage of onboard sensor measurements of water properties such as salinity and temperature to locate oceanic features in real time. Such targeted sampling strategies enable more rapid study of ocean environments by actively steering towards areas of high scientific value. Inspired by the ability of aquatic animals to navigate via flow sensing, this work investigates hydrodynamic cues for accomplishing targeted sampling using a palm-sized robotic swimmer. As proof-of-concept analogy for tracking hydrothermal vent plumes in the ocean, the robot is tasked with locating the center of turbulent jet flows in a 13,000-liter water tank using data from onboard pressure sensors. To learn a navigation strategy, we first implemented RL on a simulated version of the robot navigating in proximity to turbulent jets. After training, the RL algorithm discovered an effective strategy for locating the jets by following transverse velocity gradients sensed by pressure sensors located on opposite sides of the robot. When implemented on the physical robot, this gradient following strategy enabled the robot to successfully locate the turbulent plumes at more than twice the rate of random searching. Additionally, we found that navigation performance improved as the distance between the pressure sensors increased, which can inform the design of distributed flow sensors in ocean robots. Our results demonstrate the effectiveness and limits of flow-based navigation for autonomously locating hydrodynamic features of interest.

Hydrodynamic pressure sensing for a biomimetic robotic fish caudal fin integrated with a resistive pressure sensor

Micro-sensors, such as pressure and flow sensors, are usually adopted to attain actual fluid information around swimming biomimetic robotic fish for hydrodynamic analysis and control. However, most of the reported micro-sensors are mounted discretely on body surfaces of robotic fish and it is impossible to analyzed the hydrodynamics between the caudal fin and the fluid. In this work, a biomimetic caudal fin integrated with a resistive pressure sensor is designed and fabricated by laser machined conductive carbon fibre composites. To analyze the pressure exerted on the caudal fin during underwater oscillation, the pressure on the caudal fin is measured under different oscillating frequencies and angles. Then a model developed from Bernoulli equation indicates that the maximum pressure difference is linear to the quadratic power of the oscillating frequency and the maximum oscillating angle. The fluid disturbance generated by caudal fin oscillating increases with an increase of oscillating frequency, resulting in the decrease of the efficiency of converting the kinetic energy of the caudal fin oscillation into the pressure difference on both sides of the caudal fin. However, perhaps due to the longer stability time of the disturbed fluid, this conversion efficiency increases with the increase of the maximum oscillating angle. Additionally, the pressure variation of the caudal fin oscillating with continuous different oscillating angles is also demonstrated to be detected effectively. It is suggested that the caudal fin integrated with the pressure sensor could be used for sensing thein situflow field in real time and analyzing the hydrodynamics of biomimetic robotic fish.

Biomimetic Hydrodynamic Sensor with Whisker Array Architecture and Multidirectional Perception Ability

The rapid development of ocean exploration and underwater robot technology has put forward new requirements for underwater sensing methods, which can be used for hydrodynamic characteristics perception, underwater target tracking, and even underwater cluster communication. Here, inspired by the specialized undulated surface structure of the seal whisker and its ability to suppress vortex-induced vibration, a multidirectional hydrodynamic sensor based on biomimetic whisker array structure and magnetic 3D self-decoupling theory is introduced. The magnetic-based sensing method enables wireless connectivity between the magnetic functional structures and electronics, simplifying device design and endowing complete watertightness. The 3D self-decoupling capability enables the sensor, like a seal or other organisms, to perceive arbitrary whisker motions caused by the action of water flow without complex calibration and additional sensing units. The whisker sensor is capable of detecting a variety of hydrodynamic information, including the velocity (RMSE < 0.061 m s-1) and direction of the steady flow field, the frequency (error < 0.05 Hz) of the dynamic vortex wake, and the orientation (error < 7°) of the vortex wake source, demonstrating its extensive potential for underwater environmental perception and communication, especially in deep sea conditions.

Real-time position and pose prediction for a self-propelled undulatory swimmer in 3D space with artificial lateral line system

This study aims to investigate the feasibility of using an artificial lateral line (ALL) system for predicting the real-time position and pose of an undulating swimmer with Carangiform swimming patterns. We established a 3D computational fluid dynamics simulation to replicate the swimming dynamics of a freely swimming mackerel under various motion parameters, calculating the corresponding pressure fields. Using the simulated lateral line data, we trained an artificial neural network to predict the centroid coordinates and orientation of the swimmer. A comprehensive analysis was further conducted to explore the impact of sensor quantity, distribution, noise amplitude and sampling intervals of the ALL array on predicting performance. Additionally, to quantitatively assess the reliability of the localization network, we trained another neural network to evaluate error magnitudes for different input signals. These findings provide valuable insights for guiding future research on mutual sensing and schooling in underwater robotic fish.

The role of hydrodynamics in collective motions of fish schools and bioinspired underwater robots

Collective behaviour defines the lives of many animal species on the Earth. Underwater swarms span several orders of magnitude in size, from coral larvae and krill to tunas and dolphins. Agent-based algorithms have modelled collective movements of animal groups by use of social forces, which approximate the behaviour of individual animals. But details of how swarming individuals interact with the fluid environment are often under-examined. How do fluid forces shape aquatic swarms? How do fish use their flow-sensing capabilities to coordinate with their schooling mates? We propose viewing underwater collective behaviour from the framework of fluid stigmergy, which considers both physical interactions and information transfer in fluid environments. Understanding the role of hydrodynamics in aquatic collectives requires multi-disciplinary efforts across fluid mechanics, biology and biomimetic robotics. To facilitate future collaborations, we synthesize key studies in these fields.

An Ultrahighly Pressure Sensitive Electronic Fish Skin for Underwater Wave Sensing

Underwater flexible sensors have a future for wide application, which is promising for attaching them to underwater creatures to monitor vital signals and biomechanical analysis of their motion and perceive tiny environmental disturbances. However, the pressure waves induced by biological swimming are extremely weak and susceptible to undercurrents, making them difficult to sense. Here, we report an ultrahighly sensitive biomimetic electronic fish skin designed by embedding an artificial pseudocapacitive-based hair cell into a simulated canal neuromast encapsulation structure, in which the artificial hair cell, as the key sensitive unit, is assembled from hybrid film electrodes and polyurethane-acidic electrolyte foam. Such a film is prepared by inter-cross-linking MXene and holey reduced graphene oxide with the assistance of l-cysteine, effectively increasing the interfacial capacitance and alleviating the oxidation issues of MXene. Meanwhile, the acidic foam with high porosity shows great compressibility to adapt to a high-pressure underwater environment. Consequently, the device exhibits ultrahighly sensitivity (maximum sensitivity ∼173688 kPa^-1) over a wide range of depths (0-100 m) and remains stable after 10000 repeated tests. As an example case, the device is integrated as a motion monitoring system to identify the minor disturbances triggered by instantaneous postural changes of fish. The electronic fish skin is expected to demonstrate enormous potentials in flow field monitoring, ocean current detecting, and even seismic waves warning.

Lateral line morphology, sensory perception and collective behaviour in African cichlid fish

The lateral line system of fishes provides cues for collective behaviour, such as shoaling, but it remains unclear how anatomical lateral line variation leads to behavioural differences among species. Here we studied associations between lateral line morphology and collective behaviour using two morphologically divergent species and their second-generation hybrids. We identify collective behaviours associated with variation in canal and superficial lateral line morphology, with closer proximities to neighbouring fish associated with larger canal pore sizes and fewer superficial neuromasts. A mechanistic understanding of the observed associations was provided by hydrodynamic modelling of an artificial lateral line sensor, which showed that simulated canal-based neuromasts were less susceptible to saturation during unidirectional movement than simulated superficial neuromasts, while increasing the canal pore size of the simulated lateral line sensor elevated sensitivity to vortices shed by neighbouring fish. Our results propose a mechanism behind lateral line flow sensing during collective behaviour in fishes.

Force Optimization of Elongated Undulating Fin Robot Using Improved PSO-Based CPG

Biorobotic fishes have a huge impact on the development of underwater devices due to both fast swimming speed and great maneuverability. In this paper, an enhanced CPG model is investigated for locomotion control of an elongated undulating fin robot inspired by black knife fish. The proposed CPG network includes sixteen coupled Hopf oscillators for gait generation to mimic fishlike swimming. Furthermore, an enhanced particle swarm optimization (PSO), called differential particle swarm optimization (D-PSO), is introduced to find a set of optimal parameters of the modified CPG network. The proposed D-PSO-based CPG network is not only able to increase the thrust force in order to make the faster swimming speed but also avoid the local maxima for the enhanced propulsive performance of the undulating fin robot. Additionally, a comparison of D-PSO with the traditional PSO and genetic algorithm (GA) has been performed in tuning the parametric values of the CPG model to prove the superiority of the introduced method. The D-PSO-based optimization technique has been tested on the actual undulating fin robot with sixteen fin-rays. The obtained results show that the average propulsive force of the untested material is risen 5.92%, as compared to the straight CPG model.

Real-time parametric estimation of periodic wake-foil interactions using bioinspired pressure sensing and machine learning

Periodic wake-foil interactions occur in the collective swimming of bio-inspired robots. Wake interaction pattern estimation (and control) is crucial to thrust enhancement and propulsive efficiency optimization. In this paper, we study the wake interaction pattern estimation of two flapping foils in tandem configurations. The experiments are conducted at a Reynolds number of 1.41 × 10⁴in a water channel. A modified wake-foil phase parameter Φ, which unifies the influences of inter-foil distanceL_x, motion phase difference Δφand wake convection velocityU_v, is introduced to describe the wake interaction patterns parametrically. We use a differential pressure sensor on the downstream foil to capture wake interaction characteristics. Data sets at different tandem configurations are collected. The wake-foil phase Φ is used to label the pressure signals. A one-dimensional convolutional neural networks (1D-CNN) model is used to learn an end-to-end mapping between the raw pressure measurements and the wake-foil phase Φ. The trained 1D-CNN model shows accurate estimations (average error 3.5%) on random wake interaction patterns and is fast enough (within 40 ms). Then the trained 1D-CNN model is applied to online thrust enhancement control of a downstream foil swimming in a periodic wake. Synchronous force monitoring and flow visualization demonstrate the effectiveness of the 1D-CNN model. The limitations of the model are discussed. The proposed approach can be applied to the online estimation and control of wake interactions in the collective swimming and flying of biomimetic robots.

Bioinspired Adaptive, Elastic, and Conductive Graphene Structured Thin-Films Achieving High-Efficiency Underwater Detection and Vibration Perception

Underwater exploration has been an attractive topic for understanding the very nature of the lakes and even deep oceans. In recent years, extensive efforts have been devoted to developing functional materials and their integrated devices for underwater information capturing. However, there still remains a great challenge for water depth detection and vibration monitoring in a high-efficient, controllable, and scalable way. Inspired by the lateral line of fish that can sensitively sense the water depth and environmental stimuli, an ultrathin, elastic, and adaptive underwater sensor based on Ecoflex matrix with embedded assembled graphene sheets is fabricated. The graphene structured thin film is endowed with favourable adaptive and morphable features, which can conformally adhere to the structural surface and transform to a bulged state driven by water pressure. Owing to the introduction of the graphene-based layer, the integrated sensing system can actively detect the water depth with a wide range of 0.3-1.8 m. Furthermore, similar to the fish, the mechanical stimuli from land (e.g. knocking, stomping) and water (e.g. wind blowing, raining, fishing) can also be sensitively captured in real time. This graphene structured thin-film system is expected to demonstrate significant potentials in underwater monitoring, communication, and risk avoidance.

Creating underwater vision through wavy whiskers: a review of the flow-sensing mechanisms and biomimetic potential of seal whiskers

Seals are known to use their highly sensitive whiskers to precisely follow the hydrodynamic trail left behind by prey. Studies estimate that a seal can track a herring that is swimming as far as 180 m away, indicating an incredible detection apparatus on a par with the echolocation system of dolphins and porpoises. This remarkable sensing capability is enabled by the unique undulating structural morphology of the whisker that suppresses vortex-induced vibrations (VIVs) and thus increases the signal-to-noise ratio of the flow-sensing whiskers. In other words, the whiskers vibrate minimally owing to the seal's swimming motion, eliminating most of the self-induced noise and making them ultrasensitive to the vortices in the wake of escaping prey. Because of this impressive ability, the seal whisker has attracted much attention in the scientific community, encompassing multiple fields of sensory biology, fluid mechanics, biomimetic flow sensing and soft robotics. This article presents a comprehensive review of the seal whisker literature, covering the behavioural experiments on real seals, VIV suppression capabilities enabled by the undulating geometry, wake vortex-sensing mechanisms, morphology and material properties and finally engineering applications inspired by the shape and functionality of seal whiskers. Promising directions for future research are proposed.

Design and analysis of a novel tendon-driven continuum robotic dolphin

In this paper, a novel continuum robotic dolphin termed 'ConRoDolI' is proposed and developed. The biomimetic robot features dual tendon driving continuum mechanisms that are utilized to replicate the twisting and bending motions of the dolphin's caudal vertebrae and thoracic vertebrae. More importantly, a central pattern generator based kinematics is analyzed to yield stable dolphin-like swimming. In the meantime, the relationship between the backbone shape and both the tendon length as well as position and orientation are explored. Furthermore, multimodal swimming gaits are designed to pave the way for a three-dimensional (3D) swimming decoupling solution, involving forwarding swimming, multiple yaw patterns, and multiple pitch patterns. All of these endow the robotic dolphin with 3D maneuverability. Finally, extensive experiments demonstrate the feasibility of the proposed biomimetic mechatronic design and control approach. The forward swimming speed is 0.44 body lengths per second (BL/s). The steering radius of the robot is about 0.11 BL with an angular velocity of 10°/s and the diving speed is about 0.13 BL/s. The average propulsion efficiency is about 0.6 with the maximum is over 0.8. The obtained results shed light on the improvement of aquatic maneuverability associated with new-concept underwater vehicles.

Artificial lateral line based relative state estimation between an upstream oscillating fin and a downstream robotic fish

The lateral line enables fish to efficiently sense the surrounding environment, thus assisting flow-related fish behaviors. Inspired by this phenomenon, varieties of artificial lateral line systems (ALLSs) have been developed and applied to underwater robots. This article focuses on using the pressure sensor arrays based ALLS-measured hydrodynamic pressure variations (HPVs) for estimating the relative states between an upstream oscillating fin and a downstream robotic fish. The HPVs and relative states are measured in flume experiments in which the oscillating fin and the robotic fish have been locate with upstream-downstream formation in a flume. The relative states include the relative oscillating frequency, amplitude, and offset of the upstream oscillating fin to the downstream robotic fish, the relative vertical distance, the relative yaw angle, the relative pitch angle, and the relative roll angle between the upstream oscillating fin and the downstream robotic fish. Regression models between the ALLS-measured and the mentioned relative states are investigated, and regression models-based relative state estimations are conducted. Specifically, two criteria are proposed firstly to investigate not only the sensitivity of each pressure sensor to the variations of relative state but also the insufficiency and redundancy of the pressure sensors. And thus the pressure sensors used for regression analysis are determined. Then four typical regression methods, including random forest (RF) algorithm, support vector regression, back propagation neural network, and multiple linear regression method are used for establishing regression models between the ALLS-measured HPVs and the relative states. Then regression effects of the four methods are compared and discussed. Finally, the RF-based method, which has the best regression effect, is used to estimate the relative yaw angle and oscillating amplitude using the ALLS-measured HPVs and exhibits excellent estimation performance.