Design a Multifunctional Soft Tactile Sensor Enhanced by Machine Learning Approaches

Abstract Tactile sensors are essential to robot hands that deal with various objects and interact with the environment. Soft tactile sensors are especially important to capture tactile information about delicate, irregular-shaped, or unknown objects. This paper introduces a soft tactile sensor that can simultaneously estimate the contact force, contact feature, and contact point. Inspired by multifunctional human skin, the proposed design has a dual-layer structure and is multifunctional. The top layer consists of a group of sensing elements that detect the contact location and contact feature enhanced by the bagging classifier based on the k-nearest neighbors. The sensor elements were biomimetically and analytically designed as a pyramid shape that mimicked the mountain ridge-like structure in human skin to improve sensitivity. The bottom layer was made by a piece of Velostat sandwiched between conductive fabrics that can measure the contact force. The relationship between the sensing voltage and the contact force was modeled by the Nadaraya–Watson regressor. The performance of the proposed sensor was verified by a repeatability test. Furthermore, we demonstrated the effectiveness of the proposed sensor on a robotic gripper. The experimental results show that this sensor is able to detect contact information of fragile objects.