Texture Identification of Objects Using a Robot Fingertip Module with Multimodal Tactile Sensing Capability - Bo-Gyu Bok; Jin-Seok Jang; Min-Seok Kim (Applied Sciences, 2021)
Modern robots fall behind humans in terms of the ability to discriminate between textures of objects. This is due to the fact that robots lack the ability to detect the various tactile modalities that are required to discriminate between textures of objects. Hence, our research team developed a robot fingertip module that can discriminate textures of objects via direct contact. This robot fingertip module is based on a tactile sensor with multimodal (3-axis force and temperature) sensing capabilities. The multimodal tactile sensor was able to detect forces in the vertical (Z-axis) direction as small as 0.5 gf and showed low hysteresis error and repeatability error of less than 3% and 2% in the vertical force measurement range of 0–100 gf, respectively. Furthermore, the sensor was able to detect forces in the horizontal (X- and Y-axes) direction as small as 20 mN and could detect 3-axis forces with an average cross-talk error of less than 3%. In addition, the sensor demonstrated its multimodal sensing capability by exhibiting a near-linear output over a temperature range of 23–35 °C. The module was mounted on a motorized stage and was able to discriminate 16 texture samples based on four tactile modalities (hardness, friction coefficient, roughness, and thermal conductivity).