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Designing underwater robots that can move efficiently and adapt to changing currents remains a complex challenge. Traditional systems rely on external sensors and pre-programmed responses, which can struggle in dynamic environments such as turbulent waters or confined spaces. Replicating the fluid, energy-efficient movement of marine life, while also sensing the surrounding environment, has proven difficult with conventional engineering approaches.
According to TechXplore, recent research offers a different direction by looking deeper into how fish actually move. Instead of focusing only on body shape or motion, researchers examined the electrical signals generated within fish muscles. Using a custom-built 16-channel system, they recorded electromyography (EMG) signals from freely swimming fish and paired them with detailed motion tracking under varying flow conditions. A neural network was then able to translate these signals into precise body posture, effectively reconstructing how the fish moved in real time.
Beyond motion tracking, the same system was able to identify environmental conditions such as flow type and swimming speed. This suggests that muscle activity carries embedded information not just about movement, but also about the surrounding water. Further experiments revealed an additional layer: in turbulent conditions, external water forces could influence the fish’s body before muscle activation occurred. This indicates that muscles may function not only as actuators but also as part of a sensing mechanism, responding to environmental feedback.
These insights were then applied to robotics. By training a model on biological data alone, researchers created a system capable of predicting movement in a robotic fish without requiring additional tuning. The model captured key physical parameters such as delay, damping, and natural frequency, allowing it to outperform more conventional deep learning approaches when transferred to a mechanical platform.
From a defense and security perspective, such advancements could support the development of more capable underwater systems for surveillance, inspection, and operations in contested maritime environments. Robots that can both move efficiently and “feel” their surroundings through internal signals may operate more quietly and adaptively, reducing reliance on bulky external sensors.
The findings point toward a shift in bio-inspired robotics – moving beyond imitation of form toward replication of the underlying control and sensing principles found in nature.
The information is based on several research papers – 1, 2, 3.
