Intelligent Gripper Systems Using Air Gap-Controlled Bimodal Tactile Sensors for Deformable Object Classification

Abstract

Multimodal tactile sensors have played an important role in enhancing robot intelligence by providing reliable datasets originating from their high accuracy and durability characteristics. Herein, bimodal tactile sensors capable of simultaneously recognizing the size and stiffness of grasped objects, even deformable ones, are produced. The bimodal tactile sensors are fabricated using the identical process of controlling the air gap between a flexible substrate and the sensing layer, allowing the sensorized gripper to measure pressure and bending characteristics with high accuracy and durability. The pressure sensor yields an excellent durability performance with a negligible change of ΔI/I o < 4.01% even after more than 104 pressing–releasing cycles and broad detection range (5–360 kPa) characteristics. The bending strain sensor also exhibits high sensitivity in a broad bending strain range (0–2.3%) and high durability with a change of ΔI/I o < 4.48% for 104 bending cycles. Using these devices, the sensorized gripper demonstrates that seven tomatoes with different sizes and ripeness states can be classified with high accuracy of 98.78% using an artificial neural network. Finally, the tactile feedback system is expected to be utilized in smart factories, automation systems, and humanoid robots in the near future.