Optimal Flow Sensing for Schooling Swimmers

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.

Intelligent Fish-Inspired Foraging of Swarm Robots with Sub-Group Behaviors Based on Neurodynamic Models

This paper proposes a novel intelligent approach to swarm robotics, drawing inspiration from the collective foraging behavior exhibited by fish schools. A bio-inspired neural network (BINN) and a self-organizing map (SOM) algorithm are used to enable the swarm to emulate fish-like behaviors such as collision-free navigation and dynamic sub-group formation. The swarm robots are designed to adaptively reconfigure their movements in response to environmental changes, mimicking the flexibility and robustness of fish foraging patterns. The simulation results show that the proposed approach demonstrates improved cooperation, efficiency, and adaptability in various scenarios. The proposed approach shows significant strides in the field of swarm robotics by successfully implementing fish-inspired foraging strategies. The integration of neurodynamic models with swarm intelligence not only enhances the autonomous capabilities of individual robots, but also improves the collective efficiency of the swarm robots.

A Circular Formation Method for Biomimetic Robotic Fish Inspired by Fish Milling

Circular motion phenomena, akin to fish milling, are prevalent within the animal kingdom. This paper delineates two fundamental mechanisms underlying such occurrences: forward following and circular topological communication. Leveraging these pivotal concepts, we present a multi-agent formation circular model based on a second-order integrator. This model engenders the attainment of homogeneous intelligence convergence along the circumferential trajectory. The convergence characteristics are intricately linked to the number of agents and the model parameters. Consequently, we propose positive and negative solutions for ascertaining the convergent circle property and model parameters. Furthermore, by integrating our proposed formation control methodology with a robotic fish dynamics model, we have successfully implemented simulations and experiments, demonstrating the circular formation of multiple biomimetic robotic fish. This study provides a mathematical explication for the circular motion observed in animal groups and introduces a novel approach to achieving circular formation in multiple robots inspired by biological phenomena.

Deep-reinforcement-learning-based self-organization of freely undulatory swimmers

It is fascinating that fish groups spontaneously form different formations. The collective locomotions of two and multiple undulatory self-propelled foils swimming in a fluid are numerically studied and the deep reinforcement learning (DRL) is applied to control the locomotion. We explored whether typical patterns emerge spontaneously under the driven two DRL strategies. One strategy is that only the following fish gets hydrodynamic advantages. The other is that all individuals in the group take advantage of the interaction. In the DRL strategy, we use swimming efficiency as the reward function, and the visual information is included. We also investigated the effect of involving hydrodynamic force information, which is an analogy to that detected by the lateral line of fish. Each fish can adjust its undulatory phase to achieve the goal. Under the two strategies, collective patterns with different characteristics, i.e., the staggered-following, tandem-following phalanx and compact modes emerge. They are consistent with the results in the literature. The hydrodynamic mechanism of the above high-efficiency collective traveling modes is analyzed by the vortex-body interaction and thrust. We also found that the time sequence feature and hydrodynamic information in the DRL are essential to improve the performance of collective swimming. Our research can reasonably explain the controversial issue observed in the relevant experiments. The paper may be helpful for the design of bionic fish.

Hydrodynamical Fingerprint of a Neighbour in a Fish Lateral Line

For fish, swimming in group may be favorable to individuals. Several works reported that in a fish school, individuals sense and adjust their relative position to prevent collisions and maintain the group formation. Also, from a hydrodynamic perspective, relative-position and kinematic synchronisation between adjacent fish may considerably influence their swimming performance. Fish may sense the relative-position and tail-beat phase difference with their neighbors using both vision and the lateral-line system, however, when swimming in dark or turbid environments, visual information may become unavailable. To understand how lateral-line sensing can enable fish to judge the relative-position and phase-difference with their neighbors, in this study, based on a verified three-dimensional computational fluid dynamics approach, we simulated two fish swimming adjacently with various configurations. The lateral-line signal was obtained by sampling the surface hydrodynamic stress. The sensed signal was processed by Fast Fourier Transform (FFT), which is robust to turbulence and environmental flow. By examining the lateral-line pressure and shear-stress signals in the frequency domain, various states of the neighboring fish were parametrically identified. Our results reveal that the FFT-processed lateral-line signals in one fish may potentially reflect the relative-position, phase-differences, and the tail-beat frequency of its neighbor. Our results shed light on the fluid dynamical aspects of the lateral-line sensing mechanism used by fish. Furthermore, the presented approach based on FFT is especially suitable for applications in bioinspired swimming robotics. We provide suggestions for the design of artificial systems consisting of multiple stress sensors for robotic fish to improve their performance in collective operation.

Learning efficient navigation in vortical flow fields

Efficient point-to-point navigation in the presence of a background flow field is important for robotic applications such as ocean surveying. In such applications, robots may only have knowledge of their immediate surroundings or be faced with time-varying currents, which limits the use of optimal control techniques. Here, we apply a recently introduced Reinforcement Learning algorithm to discover time-efficient navigation policies to steer a fixed-speed swimmer through unsteady two-dimensional flow fields. The algorithm entails inputting environmental cues into a deep neural network that determines the swimmer's actions, and deploying Remember and Forget Experience Replay. We find that the resulting swimmers successfully exploit the background flow to reach the target, but that this success depends on the sensed environmental cue. Surprisingly, a velocity sensing approach significantly outperformed a bio-mimetic vorticity sensing approach, and achieved a near 100% success rate in reaching the target locations while approaching the time-efficiency of optimal navigation trajectories.