Object curvature plays an important role in grasping and manipulation. To be more exact, local curvature is a more useful information for grasping practically. Vision and touch are the two main methods to extract surface curvature of an object, but vision is often limited since the complete contact area is invisible during manipulation. In this paper, the authors propose an object curvature estimation method based on an artificial neural network algorithm through a lab-developed sparse tactile sensor array. The compliant layer covering on the sensor is indispensable for fitting the curved surface. Three types (plane, convex sphere, and convex cylinder) of sample and each type of sample including 30 different radiuses (1 mm to 30 mm) were used in the experiment. The overall classification accuracy was 93.1%. The average curvature radius estimating error based on an artificial neural network (ANN) algorithm was 1.87 mm. When the radius of curvature was bigger than 5 mm, the average relative error was smaller than 20%. As a comparison, the sensor array density we used in this paper was less than 9/cm², which was smaller than the density of human SAII receptors, but the discrimination result was close to the SAII receptors. Comparison with the curvature discrimination ability of the human body showed that this method has a promising application prospect.

At present, most of tactile sensors integrated in prosthetic hands are installed on the surface of the fingers, which destroys the curved and compliant nature of finger surface that is important for the manipulation of objects. To solve this problem, this paper proposes a user-friendly packaging solution with solid-liquid hybrid structure for silicon-based pressure die to develop a force sensitive artificial finger which can meet the requirements of sensor sensitivity while maintaining the geometric and mechanical features of the finger surface. The artificial finger consists of a packaging including a thumb-shaped finger stall and a certain amount of dielectric liquid, a rigid phalange, a silicon-based pressure die, a printed circuit board (PCB), and a clamp plate. When a force is applied on the pad of the artificial finger, the finger stall deforms, which compresses the liquid in the chamber and result in a resistance change of the pressure die. A prototype with the overall size of 37 mm × 32 mm × 50 mm was developed and tested. Results showed that the artificial finger has good sensing performance with force detection range of 5.7 mN to 10.5 N, sensitivity of 3.34 mV/N and 1.99 mV/N in the range of 0 - 4.0 N and 4.0 N - 10.5 N respectively, linearity of 0.99, repeatability error of 0.77%, and hysteresis error of 2.9%. Furthermore, two typical daily applications including the mouse click and the texture recognition were carried out with the prototype, proving good real-time detecting capacity and dynamic characteristic.

Surface texture information plays an important role in the cognition and manipulation of an object. Vision and touch are the two main methods for extracting an object’s surface texture information. However, vision is often limited since the viewing angle is uncertain during manipulation. In this article, we propose a fine surface texture detection method based on a stochastic resonance algorithm through a novel solid–liquid composite flexible tactile sensor array. A thin flexible layer and solid–liquid composite conduction structure on the sensor effectively reduce the attenuation of the contact force and enhance the sensitivity of the sensor. A series of ridge texture samples with different heights (0.9, 4, 10 μm), different widths (0.3, 0.5, 0.7, 1 mm), but the same spatial period (2 mm) of ridges were used in the experiment. The experimental results prove that the stochastic resonance algorithm can significantly improve the signal characteristic of the output signal of the sensor. The sensor has the capability to detect fine ridge texture information. The mean relative error of the estimation for the spatial period was 1.085%, and the ridge width and ridge height, respectively, have a monotonic mapping relationship with the corresponding model output parameters. The sensing capability to sense a fine texture of tactile senor surpasses the limit of human fingers.