Rubbery figures: scientists create an entirely soft robot


US researchers have really put the “soft” into “soft robot”. They’ve built one out of nothing but rubber and air.

There aren’t even any conventional electronics. Silicone tubing and pressurised air do that job. According to Harvard University’s Daniel Preston, the invention could allow operators to “replicate any behaviour found on any electronic computer”.

In the case of the bobbing fish-like robot Preston and colleagues created, an environmental pressure sensor determines what action to take. 

The soft valves are programmed to react to different air pressures. The robot dives when the circuit senses low pressure at the top of the tank and surfaces when it senses high pressure at depth. It can also rise up on command if someone pushes an external soft button.

In other words, says Preston, it relies exclusively on soft digital logic – and that’s a first.{%recommended 8654%} 

The how and why are explained in a paper published in the journal PNAS.

Controlling robots without electronics isn’t new. The authors note that others have designed microfluidic circuits that use liquid and air to create non-electronic logic gates. 

The problem, they say, is that the circuits often rely on hard materials such as glass or plastics, and use such thin channels that only small amounts of air can move through at a time, slowing the robot’s motion. 

In comparison, the channels in Preston’s design are larger, enabling much faster air flow rates. Air-based grippers he created can grasp an object in a matter of seconds.

HIs circuits are also more efficient because they require no energy input when dormant. This could be critical, he suggests, in emergency situations where the robots travel far from a reliable energy source.

They could even become essentially invisible if made from a material matched to the background.

Soft robots have become more and more a part of the previously metal-dominant robot world in the past decade, because they have numerous advantages in many settings.

For Preston and colleagues, using rubber adds a valuable simplicity compared with work using machine learning and artificial intelligence. 

“There’s a lot of capability there,” he says, “but it’s also good to take a step back and think about whether or not there’s a simpler way to do things that gives you the same result, especially if it’s not only simpler, it’s also cheaper.”

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