Mechano-Graded Contact-Electrification Interfaces Based Artificial Mechanoreceptors for Robotic Adaptive Reception

Abstract

Triboelectrification-based artificial mechanoreceptors (TBAMs) is able to convert mechanical stimuli directly into electrical signals, realizing self-adaptive protection and human-machine interactions of robots. However, traditional contact-electrification interfaces are prone to reaching their deformation limits under large pressures, resulting in a relatively narrow linear range. In this work, we fabricated mechano-graded microstructures to modulate the strain behavior of contact-electrification interfaces, simultaneously endowing the TBAMs with a high sensitivity and a wide linear detection range. The presence of step regions within the mechanically graded microstructures helps contact-electrification interfaces resist fast compressive deformation and provides a large effective area. The highly sensitive linear region of TBAM with 1.18 V/kPa can be effectively extended to four times of that for the devices with traditional interfaces. In addition, the device is able to maintain a high sensitivity of 0.44 V/kPa even under a large pressure from 40 to 600 kPa. TBAM has been successfully used as an electronic skin to realize self-adaptive protection and grip strength perception for a commercial robot arm. Finally, a high angle resolution of 2° and an excellent linearity of 99.78% for joint bending detection were also achieved. With the aid of a convolutional neural network algorithm, a data glove based on TBAMs realizes a high accuracy rate of 95.5% for gesture recognition in a dark environment.