MagCilia: A Compact Magnetociliary Tactile Sensor with 3D Force Sensing for Robotic Contact Perception and Grasping Feedback

2026-10-07

AI Summary

This study addresses the challenges of three-dimensional force measurement and surface tactile perception in robotic grasping by proposing a compact magnetic cilia tactile sensor. The sensor integrates a flexible structure with Hall-effect elements to enable 3D force feedback, and introduces a novel causal history fusion regression algorithm that leverages magnetic field variations to accurately decouple multi-channel coupled forces. System modeling and identification are further accomplished through quasi-static finite element analysis and frequency-domain feature extraction. Experimental results demonstrate that the root mean square error for triaxial force remains below 0.69 N with an R² exceeding 0.9, while classification accuracy across six surface materials reaches 99.39%. These findings confirm the sensor’s effectiveness in supporting real-time grasp adjustment for robotic manipulators.

Abstract

Robotic grasping and surface exploration benefit from simultaneous measurement of normal and tangential forces and from surface information obtained through contact. Here, we present a compact magnetociliary tactile sensor (MagCilia) that combines a flexible magnetic-cilia structure with a Hall sensor for 3D force sensing. Quasi-static finite element analysis is used to investigate structural deformation and magnetic responses under multidirectional loading. To reconstruct forces from the coupled magnetic channels, we propose causal history fusion regression (CHFR), which combines current magnetic-field measurements with their recent changes. Five-fold cross-validation grouped by calibration record yields root-mean-square errors of 0.40, 0.57, and 0.69 N for Fx, Fy, and Fz, respectively, with corresponding coefficients of determination of 0.93, 0.90, and 0.92. Robotic experiments demonstrate tangential-force-guided gripper adjustment and multi-axis load monitoring under external perturbations. Frequency-domain features of the reconstructed forces distinguish six surface categories with 99.39% accuracy in three-fold cross-validation grouped by acquisition session. An online robotic demonstration additionally identifies all six tested surfaces. These results demonstrate 3D force reconstruction, grasping feedback, and surface recognition using a single compact tactile unit.

Problem

• 3D force sensing
• tactile sensor
• robotic grasping
• surface recognition
• contact perception

Innovation

• Magnetociliary Tactile Sensor
• 3D Force Sensing
• Causal History Fusion Regression
• Surface Recognition
• Robotic Grasping