Robotic Fish Fin Shows What Underwater AI Needs

A robotic fish hitting 1.66 body lengths per second and turning within a 0.86 body-length radius isn’t just a neat lab demo—it’s a clue about where underwater robotics is headed. The surprising part isn’t the speed. It’s how it gets there: a flexible electromagnetic fin that flaps like muscle instead of relying on bulky motor-driven linkages.

Most companies trying to automate in water start with the same assumption: “We’ll adapt our ground robot and waterproof it.” That approach works for a narrow set of inspection jobs. But the moment you need quiet motion, close-proximity interaction with fragile environments, or tight maneuvering in currents, traditional actuation starts fighting physics.

This new fin design (built around coils, magnets, and an elastic joint) matters to the broader AI in Robotics & Automation story because it highlights a simple truth: AI doesn’t compensate for the wrong body. If the platform can’t move efficiently and predictably, autonomy is stuck playing defense—burning energy, overcorrecting, and limiting mission time.

The real bottleneck in underwater robotics isn’t “AI”—it’s actuation

Underwater robots fail in the field for boring reasons: power budgets collapse, seals wear out, thrusters snag debris, or the robot simply can’t hold position in turbulent flow. Those failures show up as “navigation problems,” but they often start with the propulsion and control surfaces.

Here’s the trade-off that’s haunted aquatic robots for years:

• Motor-driven fins and joints can produce strong thrust, but they’re typically rigid, heavy, and mechanically complex.
• Soft actuators (pneumatics, elastomers, smart materials) give you compliance and safer interaction, but they’re often too weak or too hard to model for reliable control.

The fin described in the source research aims for the middle: compact and powerful like motors, flexible like soft robotics. That combination is exactly what underwater autonomy needs, because the ocean isn’t a controlled factory aisle. It’s a shifting, high-drag environment where even small improvements in propulsion efficiency and controllability compound into longer missions and better data.

What’s different about a flexible electromagnetic fin

The fin’s core idea is straightforward: use electromagnetism to oscillate a flexible tail, instead of a mechanical drivetrain.

The researchers built a fin with:

• Two small coils
• Spherical magnets
• An elastic joint that reduces friction and returns the fin to a neutral position when not driven

When alternating current runs through the coils, it produces an oscillating magnetic field that drives the fin back and forth—creating thrust in a fish-like way.

Why that matters for real deployments

Less mechanical complexity is a big deal underwater. Every shaft, gear, and seal is a reliability risk. Electromagnetic actuation can reduce moving parts, which can mean fewer leak paths and less maintenance.

Compliance is equally important. If you’re inspecting infrastructure near biofouling, maneuvering through kelp, or operating near coral reefs, a rigid propulsor is more likely to snag or cause damage. A flexible fin can yield on contact and still recover its motion.

And there’s a third benefit that’s easy to miss: it’s a better match for modern control. When your actuator’s dynamics are consistent, AI control policies become simpler, safer, and more data-efficient.

Modeling: the underappreciated feature that makes AI control practical

One line from the underlying report is the most operationally relevant: the team built a mathematical model connecting electrical input to hydrodynamic thrust output.

That’s rare in soft robotics, and it’s not academic trivia. It’s the difference between:

• “We trained a controller that works in our pool,” and
• “We can predict behavior, set limits, and plan missions with confidence.”

How this helps autonomy (even before fancy AI)

If thrust can be predicted from input current, you get immediate wins:

1. Model-based control becomes viable: You can use MPC-style approaches where the robot plans fin inputs to follow trajectories while respecting power limits.
2. Simulation gets more realistic: Better simulators reduce real-world testing time and speed up iteration.
3. Energy budgeting becomes explicit: The robot can plan routes and behaviors based on remaining battery and expected drag.

And when you do bring AI into the loop—reinforcement learning for maneuvering, adaptive control for currents, or perception-driven planning—you have a stable foundation.

A useful rule: the more predictable your actuator-to-thrust mapping is, the less “heroic” your AI has to be.

Performance numbers that actually mean something

Lab demos often brag about speed without context. These results are more meaningful because they include maneuverability and force output.

Reported experimental figures:

• Speed: 405 mm/s (about 1.66 body lengths per second)
• Turning radius: about 0.86 body lengths
• Peak thrust: 0.493 N
• Fin mass: 17 g

That thrust-to-weight relationship is one reason this design is interesting. A small fin producing close to half a newton of thrust points to a platform that can be scaled into multi-fin configurations—which is where underwater robots start behaving less like torpedoes and more like agile animals.

The energy problem isn’t a footnote—it’s the product

The researchers are direct about the limitation: electromagnetic coils draw a lot of current, shortening swim duration.

That’s not a minor issue. Underwater robotics is an energy game. Batteries are heavy, recharging is inconvenient, and mission time is the difference between a commercially viable system and a nice prototype.

What “energy efficiency” means for an electromagnetic fin

You don’t fix coil inefficiency with a bigger battery; you fix it with engineering and control choices that respect power constraints:

• Coil geometry optimization: Better magnetic coupling can reduce current draw for the same torque.
• Energy recovery circuits: If the fin’s oscillation can feed energy back during parts of the cycle, you can extend runtime.
• Smart control strategies: The biggest near-term win is often software—driving the fin only as hard as needed, not continuously at max excitation.

This is where AI in robotics becomes practical rather than flashy. A well-designed autonomy stack can:

• Detect when the robot is fighting a current and choose a more efficient heading
• Switch gait patterns (oscillation frequency/amplitude) depending on mission phase
• Use perception to reduce unnecessary station-keeping and “twitchy” corrections

Put bluntly: the best underwater robots will treat energy like money—budgeted, tracked, and optimized in real time.

Where AI-powered robotic fish make business sense

A fish-like underwater robot isn’t a replacement for every ROV or AUV. Thrusters still dominate for many tasks. But there are environments where biomimetic propulsion plus AI autonomy is the better tool.

1) Ecological monitoring without disturbing habitats

Environmental teams increasingly need repeatable surveys—reef health, fish population sampling, invasive species detection. A quieter, more maneuverable robot that can operate close to sensitive structures is a strong fit.

AI adds value by enabling:

• Onboard species detection (vision models tuned for local ecosystems)
• Adaptive transects (change survey paths when visibility drops or targets appear)
• Anomaly alerts (bleaching, disease patterns, pollution indicators)

2) Underwater inspection in cluttered, complex geometry

Ports, aquaculture farms, sea walls, and some energy assets include nets, cables, and growth that create entanglement risk. Flexible propulsion surfaces can reduce snags.

AI adds value by enabling:

• 3D mapping and change detection across repeat inspections
• Autonomous close passes while keeping safe standoff distances
• Predictive maintenance signals from visual and sonar data

3) Underwater logistics (the contrarian case)

“Underwater logistics” sounds exotic, but it’s becoming more relevant as offshore infrastructure expands. Not every delivery needs a high-speed AUV; some need a small, precise, safe vehicle that can move around assets without damaging them.

Fish-like robots could serve as:

• Local runners between subsea nodes
• Sensor deployment units for short-range placement tasks
• Persistent sentinels that patrol at low speed, conserving energy

I’m bullish on this category, but only if the platform solves two things: energy efficiency and repeatable control. This fin design directly targets controllability, and it’s honest about energy as the next hurdle.

“People also ask” (and what I tell teams evaluating this tech)

Can robotic fish replace thruster-driven AUVs?

Not broadly. Thrusters are simple and effective for long transits. Robotic fish excel when you need agility, low disturbance, and safe interaction near structures or wildlife.

Why do soft robotics designs struggle underwater?

Many soft actuators are hard to model and can be power-limited. Underwater drag amplifies those issues. If you can’t predict thrust, you can’t plan reliably.

What role does AI play if the fin already moves well?

AI turns good mechanics into operational capability: energy-aware planning, adaptive gait control, robust navigation in currents, and mission-level decision-making.

What to watch next (and what it signals for automation)

The most credible “next step” mentioned is multi-fin coordinated motion. That’s the path from “a fast swimmer” to “a controllable platform” that can:

• brake precisely,
• hold position,
• slide laterally,
• and navigate tight spaces.

Coordinated fins also create a richer control problem—perfect for AI, but only if the system is instrumented well (current sensing, IMU, pressure, possibly flow sensors) and the modeling remains stable.

For the AI in Robotics & Automation series, this is a useful pattern to remember: real-world autonomy improves fastest when hardware teams reduce uncertainty. Better actuators and better models aren’t separate from AI—they’re what makes AI work outside the lab.

The open question I’d put to any product team considering biomimetic underwater robots is this: If your robot had to run twice as long on the same battery, what would you change first—mechanics, electronics, or control policy?