Over the last two decades, considerable scientific and technological efforts have been devoted to developing tactile sensing based on a variety of transducing mechanisms, with prospective applications in many fields such as human–machine interaction, intelligent robot tactile control and feedback, and tactile sensorized minimally invasive surgery. This paper starts with an introduction of human tactile systems, followed by a presentation of the basic demands of tactile sensors. State-of-the-art tactile sensors are reviewed in terms of their diverse sensing mechanisms, design consideration, and material selection. Subsequently, typical performances of the sensors, along with their advantages and disadvantages, are compared and analyzed. Two major potential applications of tactile sensing systems are discussed in detail. Lastly, we propose prospective research directions and market trends of tactile sensing systems.

A tactile sensor is an indispensable component for electronic skin, mimicking the sensing function of organism skin. Various sensing materials and microstructures have been adopted in the fabrication of tactile sensors. Herein, we propose a highly sensitive flexible tactile sensor composed of nanocomposites with pyramid and irregularly rough microstructures and implement a comparison of piezoresistive properties of nanocomposites with varying weight proportions of multi-wall nanotubes and carbon black particles. In addition to the simple and low-cost fabrication method, the tactile sensor can reach high sensitivity of 3.2 kPa⁻¹ in the range of <1 kPa and fast dynamic response of 217 ms (loading) and 81 ms (recovery) at 40 kPa pressure. Moreover, body movement monitoring applications have been carried out utilizing the flexible tactile sensor. A sound monitoring application further indicates the potential for applications in electronic skin, human–computer interaction, and physiological detection.

Atmospheric particulate matter (PM) air-microfluidic grading chip is the premise for realizing high-precision PM online monitoring. It can be used as an indispensable basis for identifying pollution sources and controlling inhalable harmful substances. In this paper, based on aerodynamic theory and COMSOL numerical analysis, a two-stage PM air-microfluidic grading chip with cut-off diameters of 10 μm and 2.5 μm was designed. The effects of chip inlet width (W), main flow width (L), second channel width (S), and split ratio (Q₁/Q) on PM classification efficiency were analyzed, and optimized design parameters were achieved. The collection efficiency curves were plotted according to PM separation effects of the chip on various particle sizes (0.5–15 μm). The results indicate that the chip has good separation effect, which provides an efficient structural model for the PM micro-fluidization chip design.

Real-time monitoring of drug delivery in an intravenous infusion system can prevent injury caused by improper drug doses. As the medicine must be administered into the vein at different rates and doses in different people, an ideal intravenous infusion system requires both a low flow rate and large dynamic range monitoring. In this study, a bio-inspired and micromachined volumetric flow sensor is presented for the biomedical application of an intravenous system. This was realized by integrating two sensing units with different sensitivities on one silicon die to achieve a large dynamic range of the volumetric flow rate. The sensor was coated with a parylene layer for waterproofing and biocompatibility purposes. A new packaging scheme incorporating a silicon die into a flow channel was employed to demonstrate the working prototype. The test results indicate that the sensor can detect a volumetric flow rate as low as 2 mL/h, and its dynamic range is from 2 mL/h to 200 mL/h. The sensor performed better than the other two commercial sensors for low-flow detection. The high sensitivity, low cost, and small size of this flow sensor make it promising for intravenous applications.

For unstructured environment applications, the ability of self-recognition for grasping operations should be guaranteed for manipulators. For this purpose, a grasping process, including instance segmentation, pose estimation, and pose transformation, is proposed herein to achieve autonomous object detection, location detection, and grasp planning. An inverse solution in position form is derived for pose transformation to guarantee redundant manipulator adaption. The inverse solution requires no default initial configuration and can obtain all feasible solutions for grasping. Additionally, the optimal grasp can be selected by introducing an optimal factor, such as manipulability. Besides, the process is programmed with high computational efficiency, making it a better choice for manipulators to achieve self-recognized grasping operation. Experiments are carried out herein to verify the necessity of instance segmentation, pose estimation, and pose transformation in achieving self-recognized grasping operation. The inverse solution in the position form is also proven to be efficient and adaptable for the pose transformation of redundant manipulators.