Flexible pressure sensors have attracted a considerable amount of attention in various fields including robotics and healthcare applications, among others. However, it remains significantly challenging to design and fabricate a flexible capacitive pressure sensor with a quite broad linearity detection range due to the nonlinear stress–strain relation of the hyperelastic polymer-based dielectric material. Along these lines, in this work, a novel flexible capacitive pressure sensor with microstructured composite dielectric layer (MCDL) is demonstrated. The MCDL was prepared by enforcing a solvent-free planetary mixing and replica molding method, while the performances of the flexible capacitive pressure sensor were characterized by performing various experimental tests. More specifically, the proposed capacitive pressure sensor with 4.0 wt % cone-type MCDL could perceive external pressure loads with a broad detection range of 0–1.3 MPa, which yielded a high sensitivity value of 3.97 × 10⁻³ kPa⁻¹ in a relative wide linear range of 0–600 kPa. Moreover, the developed pressure sensor exhibited excellent repeatability during the application of 1000 consecutive cycles and a fast response time of 150 ms. Finally, the developed sensor was utilized for wearable monitoring and spatial pressure distribution sensing applications, which indicates the great perspectives of our approach for potential use in the robotics and healthcare fields.

Robotic devices with integrated tactile sensors can accurately perceive the contact force, pressure, sliding, and other tactile information, and they have been widely used in various fields, including human–robot interaction, dexterous manipulation, and object recognition. To address the challenges associated with the initial value drift, and to improve the durability and accuracy of the tactile detection for a robotic dexterous hand, in this study, a flexible tactile sensor is designed with high repeatability by introducing a supporting layer for pre-separation. The proposed tactile sensor has a detection range of 0–5 N with a resolution of 0.2 N, and the repeatability error is as relatively small as 1.5%. In addition, the response time of the proposed tactile sensor under loading and unloading conditions are 80 ms and 160 ms, respectively. Moreover, a three-dimensional force decoupling detection method is developed by distributing tactile sensor units on a non-coplanar robotic fingertip. Finally, using a backpropagation neural network, the classification and recognition processes of nine types of objects with different shapes and categories are realized, achieving an accuracy higher than 95%. The results show that the proposed three-dimensional force tactile sensing system could be beneficial for the delicate manipulation and recognition for robotic dexterous hands.