Quantum mechanics

Robot

Process (computing)

Engineering

Microfluidics

Biomedical engineering

Materials science

Soft robotics

Control engineering

Embedded system

Operating system

Calibration

Computer science

Mechanical engineering

Physics

Hysteresis

Nonlinear system

Nanotechnology

Wearable computer

Characterization (materials science)

Artificial intelligence

Deep learning

Pressure sensor

Artificial neural network

Soft sensor

Grippers

IEEE Robotics and Automation Letters

Soft sensors made of highly deformable materials are one of the enabling technologies to various soft robotic systems, such as soft mobile robots, soft wearable robots, and soft grippers. However, major drawbacks of soft sensors compared with traditional sensors are their nonlinearity and hysteresis in response, which are common especially in microfluidic soft sensors. In this research, we propose to address the above issues of soft sensors by taking advantage of deep learning. We implemented a hierarchical recurrent sensing network, a type of recurrent neural network model, to the calibration of soft sensors for estimating the magnitude and the location of a contact pressure simultaneously. The proposed approach in this letter is not only able to model the nonlinear characteristic with hysteresis of the pressure response, but also find the location of the pressure.

Use of Deep Learning for Characterization of Microfluidic Soft Sensors