Mechano-Graded Contact-Electrification\nInterfaces\nBased Artificial Mechanoreceptors for Robotic Adaptive Reception

Triboelectrification-based artificial mechanoreceptors\n(TBAMs)\nis able to convert mechanical stimuli directly into electrical signals,\nrealizing self-adaptive protection and human–machine interactions\nof robots. However, traditional contact–electrification interfaces\nare prone to reaching their deformation limits under large pressures,\nresulting in a relatively narrow linear range. In this work, we fabricated\nmechano-graded microstructures to modulate the strain behavior of\ncontact–electrification interfaces, simultaneously endowing\nthe TBAMs with a high sensitivity and a wide linear detection range.\nThe presence of step regions within the mechanically graded microstructures\nhelps contact–electrification interfaces resist fast compressive\ndeformation and provides a large effective area. The highly sensitive\nlinear region of TBAM with 1.18 V/kPa can be effectively extended\nto four times of that for the devices with traditional interfaces.\nIn addition, the device is able to maintain a high sensitivity of\n0.44 V/kPa even under a large pressure from 40 to 600 kPa. TBAM has\nbeen successfully used as an electronic skin to realize self-adaptive\nprotection and grip strength perception for a commercial robot arm.\nFinally, a high angle resolution of 2° and an excellent linearity\nof 99.78% for joint bending detection were also achieved. With the\naid of a convolutional neural network algorithm, a data glove based\non TBAMs realizes a high accuracy rate of 95.5% for gesture recognition\nin a dark environment.