A tactile sensor is a necessary means for intelligent equipment to acquire external environment information and improve the performance of human–robot interaction. Although high‐performance tactile sensor array is widely studied, the large number of wires required to transmit data from numerous arrays is still a major obstacle in large‐area application. In this study, a large‐area, low‐cost, stretchable, textile‐based tactile sensor, which is sensitive for contact position, is proposed. The sensor has a simple three‐layer structure and four external wires due to the use of a novel double‐faced effect functional knitted textile with “self‐uniformity” characteristic. The porous polyurethane foam with a large pore size is first reported as touch switch material. It not only has excellent touch switch function, but also makes the sensor have a soft and elastic touch and good impact buffering. In addition, the application of radial basis function neural network makes the sensor have self‐learning “intelligence,” which makes the sensor flexibly and quickly arrange even on the surface of a complex 3D object. Finally, the potential applications of the sensor are demonstrated. This study shows that the sensor has great potential in the fields of wearable devices, robot interaction control, and human–computer interface.

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Electronic skin is an important means through which robots can obtain external information. A novel flexible tactile sensor capable of simultaneously detecting the contact position and force was proposed in this paper. The tactile sensor had a three-layer structure. The upper layer was a specially designed conductive film based on indium-tin oxide polyethylene terephthalate (ITO-PET), which could be used for detecting contact position. The intermediate layer was a piezoresistive film used as the force-sensitive element. The lower layer was made of fully conductive material such as aluminum foil and was used only for signal output. In order to solve the inconsistencies and nonlinearity of the piezoresistive properties for large areas, a Radial Basis Function (RBF) neural network was used. This includes input, hidden, and output layers. The input layer has three nodes representing position coordinates, X, Y, and resistor, R. The output layer has one node representing force, F. A sensor sample was fabricated and experiments of contact position and force detection were performed on the sample. The results showed that the principal function of the tactile sensor was feasible. The sensor sample exhibited good consistency and linearity. The tactile sensor has only five lead wires and can provide the information support necessary for safe human—computer interactions.

This paper presents a novel capacitive pressure sensor that is capable of making highly accurate measurements at low pressure. Different from the method that many researchers have successfully used to improve the sensitivity of capacitive sensors by using a micro-structured dielectric layer (such as the micro-structured PDMS film), this paper creatively used an elastic metallized sponge (nickel-plated polyurethane sponge) as the elastic porous electrode of the capacitive sensor, so that it can detect very low pressure (such as a 0.2 g soybean). Compared with the traditional capacitive sensor using an insulative polyurethane sponge as the dielectric, the baseline capacitance of the sensor of the same size can increase by 10 times, and it has a better signal-to-noise ratio. In addition, the sensor has good robustness due to the good mechanical properties of the nickel-plated polyurethane sponge. The fabrication process of the sensor is extremely simple, the cost is low, and it can be made into any planar shape. In this paper, we describe the structure, principle and manufacture of the sensor, and present the application on robotic grasping.

Based on the current situation of high precision and comparatively low APA (absolute positioning accuracy) in industrial robots, a calibration method to enhance the APA of industrial robots is proposed. In view of the "hidden" characteristics of the RBCS (robot base coordinate system) and the FCS (flange coordinate system) in the measurement process, a comparatively general measurement and calibration method of the RBCS and the FCS is proposed, and the source of the robot terminal position error is classified into three aspects: positioning error of industrial RBCS, kinematics parameter error of manipulator, and positioning error of industrial robot end FCS. The robot position error model is established, and the relation equation of the robot end position error and the industrial robot model parameter error is deduced. By solving the equation, the parameter error identification and the supplementary results are obtained, and the method of compensating the error by using the robot joint angle is realized. The Leica laser tracker is used to verify the calibration method on ABB IRB120 industrial robot. The experimental results show that the calibration method can effectively enhance the APA of the robot.