Magnetostrictive Tactile Sensor Array Based on L-Shaped Galfenol Wire and Application for Tilt Detection

• Published in: IEEE sensors journal 2022-07, Vol.22 (13), p.12645-12655
• Main Authors: Yang, Huiwen, Weng, Ling, Wang, Bowen, Li, Zhuolin, Sun, Ying, Huang, Wenmei, Zhang, Xuehui
• Format: Article
• Language: English
• Subjects: Ambient temperature; Attitude (inclination); Coupling; Elastodynamics; Electric potential; Force distribution; Force measurement; Galfenol; Grasping (robotics); Inverse magnetostrictive effect; L-shaped Galfenol wire; Magnetic fields; Magnetic sensors; Magnetic tunneling; Magnetoresistivity; Magnetostriction; Neural networks; Object recognition; Permanent magnets; probabilistic neural network; Sensitivity; Sensor arrays; Sensors; tactile perception; Tactile sensors; Tactile sensors (robotics); Tunnel magnetoresistance; Voltage; Wires
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2022.3177207

A magnetostrictive tactile sensor unit with a large force measurement range and high sensitivity is designed using L-shaped Galfenol wires, tunnel magnetoresistive elements, and cylindrical permanent magnets. The output voltage model of the sensor unit is established based on the inverse magnetostrictive effect, tunnel magnetoresistance, and elastodynamic theory. Structures of the sensor unit and array are optimized using COMSOL. The static, dynamic, and temperature characteristics are tested. The interaction between the output voltages of each unit in a 3\times 3 tactile sensor array is described. The experimental results show that the sensitivity of the sensor unit is 85.5 mV/N at 0-10 N and 28.4 mV/N at 10-16 N under a biased magnetic field of 1.671 kA/m. The dynamic force sensitivity of the sensor unit does not vary by more than 1.4%. The magnetic field lateral coupling and longitudinal coupling of the sensor array do not exceed a maximum of 2.3%. The array can accurately distinguish the force distribution when multiple contacts are subjected to force at the same time. The sensitivity does not vary by more than 3.9% over the ambient temperature range of 23- 59~^{\circ }\text{C} . The tactile sensor array is mounted on the mechanical fingertips to grasp bar-shaped objects with different tilt angles. The output voltage of the sensor array obtained from the robotic grasping experiments is analyzed using Probabilistic Neural Network, which can determine the tilt angle of the grasped objects, and the accuracy rate of object tilt recognition is 96.74%.