This paper presents a theoretical and experimental study on high-frequency (>5000 Hz) output characteristics of the giant magnetostrictive transducer (GMT) at varied operating conditions. Based on the Jiles–Atherton model, Maxwell’s equations, Newton’s law, and Fourier’s heat transfer equation, a nonlinear electromagnetic–mechanical–thermal multi-field coupled finite element model for GMT is established. The output displacement amplitude of the magnetostrictive rod and the acceleration of vibrating horn at the GMT device are measured and analyzed at varied exciting amplitude of ac magnetic field and frequencies (from 5600 to 7000 Hz). The frequency dependences of permeability and magnetoelastic coupling coefficient obtained from the experimental results are used in revising the model parameters. These output characteristics analysis results can provide theoretical and experimental guidance in the optimization of structural design and precise controlling for high-frequency GMT system.

A novel magnetostrictive tactile sensor has been designed, based on the inverse magnetostrictive effect and bionics, which can be used to test the gripping force of manipulator and to detect the stiffness of the manipulated objects. Based on the electromagnetics theory, the inverse magnetostrictive effect, and the Hooke's law, the force measurement model and the stiffness detection model have been established. A magnetostrictive tactile sensor is designed to measure the applied force from 0 to 5 N with a sensitivity of 114 mV/N. The stiffness classification of the manipulated objects has been tested with four different types of sample materials. The results indicate that the output voltage and slope can be applied as criteria for classifying stiffness of manipulated objects. The magnetostrictive tactile sensor has a simple structure with a rapid response and can realize the precise perception of manipulated objects.

Tactile sensors enabling human-like perception are crucial for modern intelligent robotics. This study developed a biomimetic magnetostrictive tactile sensor consisting of Galfenol wires, permanent magnets, and a Hall sensor. This novel tactile sensor exhibits a static sensitivity of 23.86 mV/N and a measurement range up to 3 N. The sensitivity is stable up to 4 Hz. A theoretical model of the tactile sensor unit was derived to correlate the output voltage with the applied force. The modeling result shows an error of 1.65% in sensitivity compared to the experimental data. The response time and recovery time of the sensor are only 11 ms and 10 ms, faster than nanoscale type of sensor. A 3×3 magnetostrictive sensor array was constructed by integrating multiple magnetostrictive tactile sensors on a flexible printed circuit board. Since the outputs of the sensing units are not affected by each other, the array can be expanded as needed. Experimental results show that the sensor array is able to detect the geometry of the object in contact.

A new high-sensitivity magnetostrictive tactile sensor array is designed based on the inverse magnetostrictive effect and tunnel magnetoresistance to enable intelligent robots to have a sensitive sense of touch and perform delicate operations. The output voltage model of the tactile sensor is developed. The optimal bias field and spacing of the sensor unit are determined using COMSOL Multiphysics. Test platforms for static and dynamic characteristics are built. The output characteristics of the tactile sensor under static and dynamic forces are tested. The sensitivity is compared with that of other types of tactile sensors. The tactile sensor units are set into a $2\times3$ sensor array and the coupling effect of the output voltage of each unit in the array is tested. The experimental results show that the experimental value of the output voltage matches the calculated value with a maximum error of 3.47%. The sensitivity of the tactile sensor is 323 mV/N at 0–4 N static force, which is higher compared to other types of tactile sensors. The sensitivity of the sensor unit changes less than 1.15% at 1–4 Hz dynamic force. The interaction of output voltage between units in the $2\times3$ tactile sensor array does not exceed 1.5% and is negligible. The $2\times3$ tactile sensor array is attached to a three-finger manipulator for grasping objects with a different hardness. The results of the study have guiding significance to the design of high-sensitivity tactile sensors for intelligent robots.

To more actively perform fine manipulation tasks in the real world, intelligent robots should be able to understand and communicate the physical attributes of the material during interaction with an object. Tactile and vision are two important sensing modalities in robotic perception system. In this article, we propose a cross-modal material perception framework for recognizing novel objects. Concretely, it first adopts an object-agnostic method to associate information from tactile and visual modalities. It then recognizes a novel object by using its tactile signal to retrieve perceptually similar surface material images through the learned cross-modal correlation. This problem exhibits a challenge because data from visual and tactile modalities are highly heterogeneous and weakly paired. Moreover, the framework should not only consider cross-modal pairwise relevance but also be discriminative and generalized for unseen objects. To this end, we propose a weakly paired cross-modal adversarial learning (WCMAL) model for the visual-tactile cross-modal retrieval, which combines the advantages of deep learning and adversarial learning. In particular, the model fully considers the weak pairing problem between the two modalities. Finally, we conduct verification experiments on a publicly available data set. The results demonstrate the effectiveness of the proposed method.

The tactile perception of an object's surface material plays an important role in many intelligent fields including object recognition, robotic grasp, and environment exploration. Enlightened by the human active tactile perception process that originates from scanning a finger on the surface, a novel finger-like tactile sensor for surface material recognition has been designed according to the transduction mechanism of cilia and Villari effect of the iron-gallium alloy. Based on the structural dynamic theory of Euler-Bernoulli beam, the linear constitutive equations of magetostrictive material and the Faraday law of electromagnetic induction, the output voltage model of the sensor under vibration induced from the fingertip-surface scanning has been established. Furthermore, a prototype of the sensor, on the macroscale, is fabricated. In order to verify the performance, the sensor is used to measure induced vibration signals with texture properties during sliding on the surface and classify five similar texture surfaces with small differences with a classification algorithm. The experimental results indicate that the proposed tactile sensor is effective and highly accurate for surface material classification. The sensor is characterized by high sensitivity and quick response. It has the potential of being miniaturized and integrated into the finger of a robotic hand to realize surface material recognition in real time.