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Self-aware 3D printed soft robots are self-aware and adaptable


On August 5, 2019, the reporter learned from foreign media that researchers from the Delft University of Technology (TU Delft) in the Netherlands created multi-color 3D printed sensors to help soft robots achieve self-awareness and adaptability.

Self-aware 3D printed soft robots are self-aware and adaptable

As the first author of the study Rob Scharff stated in the paper, soft robots can bend, stretch and twist at the same time, making existing sensors inappropriate. Through the flexible embedded 3D printing sensing method, various deformations can be detected and the interaction between the robot and the object can be increased. Scharff commented: “These are important features of robots that interact closely with humans or fragile objects, such as nursing robots or grippers that handle fruits and vegetables of various sizes. Because the interactions that occur here are very unpredictable, robots need to be able to Perceive the position of your fingers and body."

The soft robot is made of highly flexible materials and can perform natural movements similar to living creatures. Currently, NASA is using 3D printed silicone mold actuators to build soft robots designed for space exploration. Other applications have also been studied in medicine and zoology.

TU Delft researchers used Stratasys PolyJet 3D printers and Agilus Black, VeroCyan and VeroMagenta materials to create bending actuators. According to Scharff: “Inflating the air chamber causes the bellows at the top to expand, while the bottom remains the same length, creating a bending motion.”

In order to increase the "sensing" capability, Scharff added, "We print color patterns in these top bellows, and observe these color patterns with a color sensor on the inextensible bottom of the actuator. When the actuator is inflated, the sensor starts to appear. The color will gradually disappear. We use this color change and intensity change to predict the shape of the performer."

The sensor uses a feedforward neural network (FNN) to operate. The 1000 sensor value samples collected in the FNN correspond to the actuator shape. The shape of the actuator is represented by six marks on the non-stretchable layer, which are tracked by the camera. The input of the network is the reading of 4 color sensors, with 4 channels (red, green, blue, white).

"Our method can predict the position of each marker with an error of usually between 0.025 and 0.075mm," Scharff added.

"Unlike existing sensors in soft robots, we are able to measure the exact shape of the fixture bending around the object. Therefore, this work is a big step towards being able to accurately move and grasp objects with soft robots."

Link to this article: Self-aware 3D printed soft robots are self-aware and adaptable

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