This article presents the development of a nonferrous–metal magnetic/eddy-current (NFM-M/EC) sensing system for simultaneous estimation of electrical conductivity and thickness in nonferrous metal plates. The distributed current source (DCS) method models the axisymmetric magnetic/eddy-current fields for the sensor design. An anisotropic magnetoresistive sensor is used to detect the change in magnetic flux density caused by the induced eddy currents in the test plates. The frequency-mapping method for sweep frequency measurements is numerically validated. Calibration between the model and experimental data by the mesh refinement method is introduced to improve the accuracy of estimations efficiently. The DCS method is verified numerically by comparing it with finite-element analysis. The proposed sensing system design is tested on four different materials with varying thicknesses. Accurate estimations in experiments substantiate the NFM-M/EC sensing system as a new alternative for nondestructive detection.

This paper presents a new modeling method to determine the harmonic eddy-current (EC) field induced in a non-ferrous metal and its corresponding magnetic flux density (MFD) by an EC-based sensing system for geometrical measurements, which accounts for the boundary effects of the object. Modeled using a distributed current source (DCS) method in state-space representation, the EC field is formulated as a two-step constrained least-square (CLS) problem to solve for its real and imaginary parts. Two practical techniques to improve the efficiency and accuracy of the EC solutions are illustrated; the first refines the DCS distribution based on the skin-depth effects, and the second takes advantages of commercial mesh-generation software to facilitate the modeling of EC induced in complex shaped objects. The DCS-based EC models are verified numerically by comparing computed results with 2D analytical axisymmetric solutions and commercial finite-element analysis (FEA), and evaluated experimentally with an EC sensor that measures the MFD generated by the induced EC in different materials and geometrical configurations.

This paper presents a vibration control method for the vibration suppression of a robotic arm. To understand the causes of the vibration in the six-axis manipulator and then eliminate the vibration through an effective control method, the vibration control structure can be divided into two parts. First, variational mode decomposition (VMD) and the Hilbert–Huang transform (HHT) algorithm are integrated to analyze the vibration signal and extract the vibration characteristics. Second, a smooth trajectory planning method is used to eliminate the residual vibration. Then, a feedback controller and iterative learning controller are used for controlling the vibration to stabilize the system while compensating for the torque of the elastic-joint model and repetitive errors caused by the vibration through the repeated operation of the arm. In addition, to improve the iterative learning control (ILC) performance, the integration of VMD and the HHT is used to distinguish useless information. The ILC is able to learn more quickly and compensate for the error caused by the vibration. The proposed VMD-HHT iterative learning control for the elastic joint (VH-ILC-EJ) method, along with a laboratory-developed six-axis degree-of-freedom (6-DOF) robotic arm, has been numerically and experimentally validated for vibration suppression.

This paper presents a pulsed eddy current (PEC) sensor design for cascade electrical conductivity and thickness estimation in non-ferrous metal plates. With a single excitation coil design, the combined change in magnetic flux density caused by the excitation coil and the induced eddy current (EC) in the test plate is detected by a compact anisotropic magneto-resistive (AMR) sensor. The effects of the varying input pulse width are discussed with both numerical simulations in commercial finite element analysis (FEA) software and experimental results, and the time-domain characteristics of the responses are analyzed in relation to the properties of the test plates. It is revealed that the electrical conductivity can be gauged based on the peak value of the short pulse response, and with a known electrical conductivity value, plate thickness can be subsequently derived with the rise time of the long pulse response. A cascade estimation process is therefore proposed to calculate the parameters sequentially by adopting hybrid input signals with both short and long input pulses. Verified by the FEA results, this cascade estimation process was then experimentally validated with a prototype of the PEC sensor.

This paper presents the development of a visual-perception system on a dual-arm mobile robot for human-robot interaction. This visual system integrates three subsystems. Hand gesture recognition is utilized to trigger human-robot interaction. Engagement and intention of the participants are detected and quantified through a cognitive system. Visual servoing uses YOLO to identify the object to be tracked and hybrid, model-based tracking to follow the object’s geometry. The proposed visual-perception system is implemented in the developed dual-arm mobile robot, and experiments are conducted to validate the proposed method’s effects on human-robot interaction applications.

In this paper, we propose a dual-fiberoptic Fabry–Perot interferometer (FFPI) sensing system integrated with a low-cost and low-coherence light-emitting diode (LED) as a light source to detect dynamic vibration caused by acoustic waves with a cut-off frequency of 200 Hz. When the acoustic signals are applied, the sensing FFPI on a Styrofoam sheet provides the function of partially transforming the longitudinal energy as the transverse energy generates a phase shift in the sensing FFPI cavity. The light reflected from the sensor is demodulated by the reference FFPI to extract the measurand. The low-power (sub-nW) optical signals are transferred into electrical signals, processed by a designed optical receiver, and recorded for data analysis.