A new flexible piezoelectric tactile sensor array based on polyvinylidene fluoride (PVDF) film is proposed for measuring three-axis dynamic contact force distribution. The array consists of six tactile units arranged as a 3 × 2 matrix with spacing 8 mm between neighbor units. In each unit, a PVDF film is sandwiched between four square-shaped upper electrodes and one square-shaped lower electrode, forming four piezoelectric capacitors. A truncated pyramid bump is located above the four piezoelectric capacitors to improve force transmission. A three-axis contact force transmitted from the top of the bump will lead to the four piezoelectric capacitors underneath undergoing different charge changes, from which the normal and shear components of the force can be calculated. A series of dynamic tests have been carried out by exerting sinusoidal forces with amplitudes ranging from 0 to 0.5 N in the x-axis, 0 to 0.5 N in the y-axis, and 0 to 1.5 N in the z-axis, separately. The tactile units show good sensitivities with 14.93, 14.92, and 6.62 pC/N in the x-, y-, and z-axes, respectively. They can work with good linearity, relatively low coupling effect, high repeatability, and acceptable frequency response in the range of 5–400 Hz to both normal and shear load. In addition, dynamic three-axis force measurement has been conducted for all of the tactile units. The average errors between the applied and calculated forces are 10.68% ± 6.84%. Furthermore, the sensor array can be easily integrated onto a curved surface, such as robotic and prosthetic hands, due to its excellent flexibility.

High-density tactile sensing has been pursued for humanoid robotic hands to obtain contact force information while the elastomer skin cover is traditionally considered to impair the force discrimination. In this work, we try to utilize the diffusion effect of the elastomer cover to identify an arbitrary contact force load just based on a sparse tactile sensor array. By numerical analysis, we proved the monotonous relation between the Pearson’s correlation coefficient and the relative distance of two single-force loads. Then, we meshed the elastomer surface and conducted the calibration load process to establish the calibration database of the sensing outputs. Afterwards, we applied the correlation method to the database and the sensing output of the unknown load to determine its location and intensity. For validation tests of the proposed method, we designed and fabricated a 3 × 3 sparse tactile sensor array with flat elastomer cover and established an automatic three-axis loading system. The validation tests were implemented including 100 random points with force intensity ranging from 0.1 to 1 N. The test results show that the method has good accuracy of detecting force load with the mean location error of 0.46 mm and the mean intensity error of 0.043 N, which meets the basic requirements of tactile sensing. Therefore, it is feasible for the sparse tactile sensor array to realize high-density load detection.

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.