Skoltech researchers and their colleagues from ESPCI Paris, the University of Chiba and the Japan Agency for Marine and Earth Science and Technology used 3D simulation to show that small fish swimming in a school can detect the position and tail beat of their neighbors as a change in water pressure. on the side of their body. This mechanism is thought to allow fish to maximize swimming efficiency in a group, even in complete darkness, when no visual cues are available. Understanding the group movement of fish is useful for predicting fish migration and designing aquatic research robots that mimic fish behavior, either for the power-saving benefits of moving in groups or blending in with creatures oceans they study. The paper is published in Frontiers of robotics and AI.
Previous research suggests that fish swimming in groups may benefit from adopting optimal relative positions and synchronizing their movements. To keep up with neighbors even in dark or murky environments, fish clearly have to rely on more than just vision. “In this study, we simulate two rummy-nosed tetra fish swimming adjacently in various configurations in calm waters. We investigate pressure signals propagating in body water from one fish to another. Although we don’t know how the animals process them, the simulation shows that the signals reaching the tactile sensory organs are intelligible against the background noise and carry information about the position and movement of the neighbor’s tail,” commented the Study co-author Dmitry Kolomenskiy, an assistant professor at the Skoltech Center for Materials Technologies.
Further research could consider louder environments, expand to larger groups of fish, and use artificial intelligence to examine how fish might process these signals, Kolomenskiy says. An earlier study of ant whirls and bird flocks, also done at Skoltech, demonstrated the potential of AI to understand the neural processes underlying collective animal movement.
In fact, there is an emerging trend in robotics that will see more and more modular designs of smaller robots working in groups or swarms. For example, a study in the next May issue of Acta Astronautics will envision an eight-wheeled Mars rover that can function as a constellation of two-wheeled machines, maximizing exploration time.
Likewise, swarms of robotic fish equipped with pressure sensors could exploit the hydrodynamic advantages of moving in groups to replace larger underwater drones that explore historic shipwrecks – like that of the recently discovered Ernest Shackleton’s Endurance – or observe the behavior of fish. In the latter case, the added bonus is that studies have shown that fish are much less bothered by marine probes that look and feel more like fish.
Knowledge of the amount of energy ingested by food fish by optimizing their movement in groups is also important for predicting their migration patterns, which is useful to the fishing industry.
Gen Li et al, Hydrodynamic footprint of a neighbor in a fish lateral line, Frontiers of robotics and AI (2022). DOI: 10.3389/frobt.2022.825889
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