This Optical Fiber Knot Serves as a Low-Cost, High-Sensitivity Sensor for Dexterous Robotics
Entirely non-metallic and able to double as a signal path for data transmission, these low-cost sensors allow for tricky manipulation.
Researchers from Zhejian University and the Research Center for Humanoid Sensing have created low-cost optical sensors which provide robots with slip detection and friction measurement — taking inspiration from the world of knots.
"Friction plays a critical role in dexterous robotic manipulation. However, realizing friction sensing remains a challenge due to the difficulty in designing sensing structures to decouple multi-axial forces," the researchers explain in the abstract to their paper. "Inspired by the topological mechanics of knots, we construct optical fiber knot (OFN) sensors for slip detection and friction measurement."
The sensors themselves are surprisingly simple, being made from polymer optical fibers using simple manufacturing techniques means the sensors should be mass-producible at a very low cost. They're entirely non-metallic, meaning no risk of electromagnetic interference, and the fibers can double as a data transmission medium to help reduce bulk in future robotics.
The sensors work by, effectively, tying a knot in the optical fiber which places an uneven strain across it. As the material in which the knotted fiber is deformed through pressure, the amount of light which comes through the fiber is reduced — and this reduction can be measured and quantified.
Using an array of five such knots, located a robot's fingertips like the nerves of a human finger, it's possible to measure pressure, friction, and slippage — as proven by the researchers' demonstrations, which included having a knot-equipped robot dexterously grip moving objects and handle a knife and a key with a two-fingered gripper.
"Such a tactile finger allows a robotic hand to manipulate human tools dexterously," the team notes of its experiments. "This work could provide a straightforward and cost-effective strategy for promoting adaptive grasping, dexterous manipulation, and human-robot interaction with tactile sensing."
The team's work is published in the journal Opto-Electronic Advances under open-access terms.