Artificial lateral line based local sensing between two adjacent robotic fish

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.

Pectoral Fin Propulsion Performance Analysis of Robotic Fish with Multiple Degrees of Freedom Based on Burst-and-Coast Swimming Behavior Stroke Ratio

The pectoral fin propulsion of a bionic robotic fish always consists of two phases: propulsion and recovery. The robotic fish moves in a burst-and-coast swimming manner. This study aims to analyze a pair of bionic robotic fish with rigid pectoral fin propulsion with three degrees of freedom and optimize the elliptical propulsion curve with the minimum recovery stroke resistance using computational fluid dynamics methods. Then, the time allocated to the propulsion and recovery phases is investigated to maximize the propulsion performance of the bionic robotic fish. The numerical simulation results show that when the time ratio of the propulsion and recovery phases is 0.5:1, the resistance during the movement of the robotic fish is effectively reduced, and the drag-reducing effect is pronounced. According to a further analysis of pressure clouds and vortex structures, the pressure difference between the upstream and downstream fins of the pectoral fin varies with different stroke ratios. The increase in recovery phase time helps to prevent premature damage to the vortex ring structure generated during the propulsion process and improves propulsion efficiency.

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.

Underwater robot coordination using a bio-inspired electrocommunication system

Due to the challenging communication and control systems, few underwater multi-robot coordination systems are currently developed. In nature, weakly electric fish can organize their collective activities using electrocommunication in turbid water. Inspired by this communication mechanism, we developed an artificial electrocommunication system for underwater robots in our previous work. In this study, we coordinate a group of underwater robots using this bio-inspired electrocommunication. We first design a time division multiple access (TDMA) network protocol for electrocommunication to avoid communication conflicts during multi-robot coordination. Then, we revise a distributed controller to coordinate a group of underwater robots. The distributed controller on each robot generates the required controls based on adjacent states obtained through electrocommunication. A central pattern generator (CPG) controller is designed to adjust the speed of individuals according to distributed control law. Simulations and experimental results show that a group of underwater robots is able to achieve coordination with the developed electrocommunication and control systems.

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.

Deep learning model inspired by lateral line system for underwater object detection

Inspired by the lateral line systems of various aquatic organisms that are capable of hydrodynamic imaging using ambient flow information, this study develops a deep learning-based object localization model that can detect the location of objects using flow information measured from a moving sensor array. In numerical simulations with the assumption of a potential flow, a two-dimensional hydrofoil navigates around four stationary cylinders in a uniform flow and obtains two types of sensory data during a simulation, namely flow velocity and pressure, from an array of sensors located on the surface of the hydrofoil. Several neural network models are constructed using the flow velocity and pressure data, and these are used to detect the positions of the hydrofoil and surrounding objects. The model based on a long short-term memory network, which is capable of learning order dependence in sequence prediction problems, outperforms the other models. The number of sensors is then optimized using feature selection techniques. This sensor optimization leads to a new object localization model that achieves impressive accuracy in predicting the locations of the hydrofoil and objects with only 40% of the sensors used in the original model.

Elytra coupling of the ladybirdCoccinella septempunctatafunctions as an energy absorber in intentional falls

Some insects, such as bees, wasps, and bugs, have specialized coupling structures to synchronize the wing motions in flight. Some others, such as ladybirds, are equipped with coupling structures that work only at rest. By locking elytra into each other, such structures provide hindwings with a protective cover to prevent contamination. Here, we show that the coupling may play another significant role: contributing to energy absorption in falls, thereby protecting the abdomen against mechanical damage. In this combined experimental, numerical and theoretical study, we investigated free falls of ladybirds (Coccinella septempunctata), and discovered that upon collision to the ground, the coupling may fail and the elytra may unlock. This unlocking of the coupling increased the energy absorption by 33%, in comparison to when the elytra remain coupled. Using micro-computed tomography scanning, we developed comparative models that enabled us to simulate impact scenarios numerically. Our results showed that unlocking of the coupling, here called elytra splitting, reduces both the peak impact force and rebound velocity. We fabricated the insect-inspired coupling mechanism using 3D printing and demonstrated its application as a damage preventing on system for quadcopters in accidental collisions.

Optimal Sensor Placement of the Artificial Lateral Line for Flow Parametric Identification

The multi-sensor artificial lateral line system (ALLS) can identify the flow-field’s parameters to realize the closed-loop control of the underwater robotic fish. An inappropriate sensor placement of ALLS may result in inaccurate flow-field parametric identification. Therefore, this paper proposes a method to optimize the sensor placement configuration of the ALLS, which mainly included three algorithms, the feature importance algorithm based on mean and variance (MVF), the feature importance algorithm based on distance evaluation (DF), and the information redundancy (IR) algorithm. The optimal sensor placement performance selected by this method is verified by simulation. In addition, further experimental verification was conducted using the ALLS. Compared with the uniform sensor placement configuration mentioned in recent studies, the experimental results suggest that the optimal sensor placement method can achieve a more effective prediction of the flow-field parameters, therefore strengthening the underwater robotic fish’s perception and control function.

Exploration of swimming performance for a biomimetic multi-joint robotic fish with a compliant passive joint

In this paper, a novel compliant joint with two identical torsion springs is proposed for a biomimetic multi-joint robotic fish, which enables imitatation of the swimming behavior of live fish. More importantly, a dynamic model based on the Lagrangian dynamic method is developed to explore the compliant passive mechanism. In the dynamic modeling, a simplified Morrison equation is utilized to analyze the hydrodynamic forces. Further, the parameter identification technique is employed to estimate numerous hydrodynamic parameters. The extensive experimental data with different situations match well with the simulation results, which verifies the effectiveness of the obtained dynamic model. Finally, motivated by the requirement for performance optimization, we firstly take advantage of a dynamic model to investigate the effect of joint stiffness and control parameters on the swimming speed and energy efficiency of a biomimetic multi-joint robotic fish. The results reveal that phase difference plays a primary role in improving efficiency and the compliant joint presents a more significant role in performance improvement when a smaller phase difference is given. Namely, at the largest actuation frequency, the maximum improvement of energy efficiency is obtained and surprisingly approximates 89%. Additionally, the maximum improvement in maximum swimming speed is about 0.19 body lengths per second. These findings demonstrate the potential of compliance in optimizing joint design and locomotion control for better performance.

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.

Localization of a leading robotic fish using a pressure sensor array on its following vehicle

The tail-flapping propulsion of a robotic fish forms a hydrodynamic pressure field that depends primarily on the flapping frequency and amplitude. In a two-robot aligned group, the tail of the front robot generates an oscillating pressure that is detectable by its follower. This paper proposes a position estimator for the follower to locate the position of the leading robotic fish. The position estimator uses the hydrodynamic pressure measured on a sensor array installed on the forefront of the following vehicle body. We derive a potential flow model to describe the pressure field of the leader in the presence of the follower. Using this pressure field model, we further derive an observability measure which is used to determine the relative positions of the leader and follower for which the position estimator will produce a reliable estimate. The position estimator employs the Levenberg-Marquardt algorithm, due to the nonlinearity of the pressure model. Results from the observability analysis show that a satisfactory estimation of the leader position is achieved when the leader is located directly ahead, on the starboard-bow, or the port-bow of the follower, similar to the formation pattern generally found in a school of fish. The observability analysis also shows that poor estimation is obtained when the leader is abeam of the follower. Tank experiments confirm the observability analysis and also demonstrate the use of the position estimator for feedback control by the follower.

Dual-sensor fusion based attitude holding of a fin-actuated robotic fish

In nature, the lateral line system (LLS) is a critical sensor organ of fish for rheotaxis in complex environments. Inspired by the LLS, numbers of artificial lateral line systems (ALLSs) have been designed to the fish-like robots for flow field perception, assisting the robots to be stable in the face of flow disturbances. However, almost all pressure sensor based ALLSs face the challenge of the low signal to noise ratio (SNR), resulting in inaccurate perception information. To solve this problem, this paper describes a dual-sensor fusion method by integrating the ALLSs with the inertial measurement unit (IMU), and shows the excellent performance by a higher precision and lower latency attitude holding of robotic fish. First, low-pass filtering is performed on ALLS data with low-SNR. Second, the ALLS data is mapped to the angle of attack based on an artificial neural network. Finally, a fusion perception method is established based on the time correlation between ALLS and IMU. To demonstrate the efficacy of our proposed method, we compare the result of attitude holding by three methods (dual-sensor fusion method, IMU based method, and ALLS based method). Furthermore, dual-sensor fusion method is tested at varied flow velocities and varied desired angles of attack, indicating that the algorithm can enable the robotic fish to perform dynamic movements in the incoming flow. This work provides a method for the attitude control of autonomous underwater vehicles (AUVs) by fusing the sensory data of ALLS and IMU, which is also applicable to other flow sensors and IMU.

Flow Field Perception of a Moving Carrier Based on an Artificial Lateral Line System

At present, autonomous underwater vehicles (AUVs) cannot perceive local environments in complex marine environments, where fish can obtain hydrodynamic information about the surrounding environment through a lateral line. Inspired by this biological function, an artificial lateral line system (ALLS) was built on a moving bionic carrier using the pressure sensor in this paper. When the carrier operated with different speeds in the flow field, the pressure distribution characteristics surrounding the carrier were analyzed by numerical simulation, where the effect of the flow angle between the fluid velocity direction and the carrier navigation direction was considered. The flume experiment was carried out in accordance with the simulation conditions, and the analysis results of the experiment were consistent with those in the simulation. The relationship between pressure and fluid velocity was established by a fitting method. Subsequently, the pressure difference method was investigated to establish a relationship model between the pressure difference on both sides of the carrier and the flow angle. Finally, a back propagation neural network model was used to predict the fluid velocity, flow angle, and carrier speed successfully in the unknown fluid environment. The local fluid environment perception by moving carrier carrying ALLS was studied which may promote the engineering application of the artificial lateral line in the local perception, positioning, and navigation on AUVs.

A pressure difference sensor inspired by fish canal lateral line

It is of interest to exploit the insight from the lateral line system of fish for flow sensing applications. In this paper, a novel fish canal lateral line-inspired pressure difference sensor is proposed by embedding an ionic polymer-metal composite (IPMC) sensor within a canal filled with viscous fluid. Such a sensor could be used by underwater robots and vehicles for object detection, angle of attack measurement, and source localization. Unlike the biological counterpart that has open ends on the surface of the body, the proposed sensor has two pores covered with a latex membrane, which prevents the canal fluid from mixing with the ambient fluid. The design and fabrication of the sensor are presented, where the sensor is integrated with a fish-like body. The sensor output is experimentally characterized as the fish-like body is rotated with respect to a dipole source, which confirms that the sensor is capable of capturing the pressure difference between the two pores. Finite element modeling and simulation that capture fluid-structure interactions and IPMC physics are conducted to shed light on the sensor behavior. Finally, the utility of the sensor in underwater robotics is illustrated via orienting the fish-like body towards the dipole source using feedback from the proposed sensor.

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 object's 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^-1. Results show that, in a 62 cm [Formula: see text] 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.