Posted in | Biomimetic Robotics

Biomimetic Robot Reveals Swimming Techniques Used by Fish

A school of fish is a remarkable display of synchronicity. But even years of research have not resolved a fundamental question—do fish swim in schools to save energy?

Robot-like fish provide insight into how fish can save energy by swimming in schools. Image Credit: Dr. Liang Li, Max Planck Institute of Animal Behavior (MPI-AB).

According to a research team from the Max Planck Institute of Animal Behavior (MPI-AB), the University of Konstanz, and Peking University, the answer is yes. This finding provides an answer that has been suspected for a long time but never irrefutably supported by experiments.

With the help of biomimetic fish-like robots, the team demonstrated that fish can apply a simple behavioral rule to leverage the swirls of water created by those in front.

By modifying their tail beat in relation to near neighbors—an approach known as vortex phase matching—the researchers demonstrated that robots benefit hydrodynamically from a proximal neighbor, regardless of their position with respect to that specific neighbor.

The formerly unknown rule, disclosed by the robots, was later demonstrated to be an approach employed by free-swimming fish. The study was published in the Nature Communications journal on October 26th, 2020.

Fish schools are highly dynamic, social systems. Our results provide an explanation for how fish can profit from the vortices generated by near neighbours without having to keep fixed distances from each other.

Iain Couzin, Study Senior Author and Director, Max Planck Institute of Animal Behavior

Couzin also co-directs the Cluster of Excellence “Centre for the Advanced Study of Collective Behaviour” at the University of Konstanz.

Robotic Solution

To answer the long-held question of whether or not energy is saved by fish by swimming with others, the energy expenditure of these marine creatures needs to be measured. To date, it has been impossible to precisely measure the energy expenditure in free-swimming fish, and hence previous studies have looked for ways to answer this fundamental question rather than through predictions and theoretical models.

But the latest study has overcome this obstacle faced in experimental testing. The team created a 3D robotic fish that features a soft tail fin and swims with an undulating movement that precisely imitates the movement of a real fish.

However, the robots are different from their live counterparts and enable the researchers to directly measure the energy consumption related to swimming together versus alone.

We developed a biomimetic robot to solve the fundamental problem of finding out how much energy is used in swimming. If we then have multiple robots interacting, we gain an efficient way to ask how different strategies of swimming together impact the costs of locomotion.

Liang Li, Study First Author and Postdoctoral Fellow, Max Planck Institute of Animal Behavior

A Simple Rule for Swimming in a School

The team investigated the robotic fish swimming in pairs against those swimming alone. They ran more than 10,000 trials and tested the follower fish in each possible position in relation to leaders—and subsequently compared the energy consumption with solo swimming.

The outcomes revealed a clear difference in energy use for robots that swam in pairs against those that swam alone. The researchers discovered that the reason for this difference in energy consumption is the way the fish in front affect the hydrodynamics of the fish behind.

Two factors determine the energy used by a follower fish—that is, its distance behind the leader fish and also the relative timing of the tail beats of the follower fish with regard to that of the leader.

To put this in simple terms, it matters whether the follower fish is placed proximal to the front or far behind the leader fish and how the follower fish alters its tail beats to manipulate the vortices generated by the leader.

In an effort to save energy, it was discovered that the secret lies in synchronization. In other words, the follower fish should match their tail beat to that of the leader fish with a particular time lag on the basis of the spatial position—an approach dubbed as “vortex phase matching” by the researchers.

When the follower fish are close to the leader fish, the most energetically effective thing for the former fish to do is to synchronize their tail beats with the leader. However, as the follower fish fall behind, they must go out of synch having progressively more lag as compared to the tail beats of the leader fish.

Visualizing Vortices

To view the hydrodynamics, the team created minute hydrogen bubbles into the water and captured them with a laser—a method that made the vortices provided by the swimming movement of the robots perceptible.

This demonstrated that the leader fish shed the vortices that move downstream. It also demonstrated that robots can use such vortices in different ways.

It’s not just about saving energy. By changing the way they synchronise, followers can also use the vortices shed by other fish to generate thrust and help them accelerate,” stated Mate Nagy, the study’s co-author and head of the Collective Behaviour “Lendület” Research Group in the Hungarian Academy of Sciences and Eötvös University. Nagy carried out the study when he was a postdoctoral fellow at the MPI-AB.

The Result in Real Fish

Nonetheless, do real fish actually utilize the technique of vortex phase matching to save power? To answer that question, the team developed a basic hydrodynamic model that estimates what real fish must do if they are applying the strategy of vortex phase matching.

By using AI-assisted analysis of body posture of goldfish swimming together, the researchers found that the same approach is indeed used in nature.

We discovered a simple rule for synchronising with neighbours that allows followers to continuously exploit socially-generated vortices. But before our robotic experiments, we simply didn’t know what to look for, and so this rule has been hidden in plain sight.

Iain Couzin, Study Senior Author and Director, Max Planck Institute of Animal Behavior

Video Credit: University of Konstanz.

Journal reference:

Li, L., et al. (2020) Vortex phase matching as a strategy for schooling in robots and in fish. Nature Communications. doi.org/10.1038/s41467-020-19086-0.

Source: https://www.uni-konstanz.de/en/

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