Triaxial tactile sensing for next-gen robotics and wearable devices

Triaxial tactile sensing technology overcomes the limitations of conventional single-axis sensors by enabling real-time decoupling of normal and shear forces, thereby supporting multi-dimensional perception in robotics, wearable devices, and human-computer interaction. By integrating flexible electronics with high-density sensor arrays, this technology enables precise object manipulation, environmental mapping, and physiological monitoring. Current applications include haptic feedback in virtual reality/augmented reality, electronic skin, and robotic slip control, demonstrating high sensitivity, fast response, and high spatial resolution. The core challenge lies in simultaneously optimizing sensing performance, long-term durability, and integration feasibility. Advances in nanomaterial engineering and machine learning algorithms are improving the accuracy of force decoupling and the efficiency of signal processing. This review systematically examines the working principles, strategies for performance enhancement, data processing methods, and cross-domain applications of triaxial tactile sensing. Instead of focusing primarily on materials or individual sensing mechanisms, it highlights critical performance trade-offs and co-optimization frameworks involving sensing performance, durability, and integration, to promote the widespread adoption of intelligent tactile systems across various industries.

Authors

Publication Details

Journal
Smart Materials and Devices
Published
2025-01-01
DOI
https://doi.org/10.70401/smd.2025.0012
Citations
2
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
1.35
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Triaxial tactile sensing for next-gen robotics and wearable devices

Guolin Yun, Zhiwei Hu
2 citations
Smart Materials and Devices
Advanced Sensor and Energy Harvesting Materials
1.35
article

Triaxial tactile sensing for next-gen robotics and wearable devices

Guolin Yun, Zhiwei Hu
article en
2 citations

Abstract

Triaxial tactile sensing technology overcomes the limitations of conventional single-axis sensors by enabling real-time decoupling of normal and shear forces, thereby supporting multi-dimensional perception in robotics, wearable devices, and human-computer interaction. By integrating flexible electronics with high-density sensor arrays, this technology enables precise object manipulation, environmental mapping, and physiological monitoring. Current applications include haptic feedback in virtual reality/augmented reality, electronic skin, and robotic slip control, demonstrating high sensitivity, fast response, and high spatial resolution. The core challenge lies in simultaneously optimizing sensing performance, long-term durability, and integration feasibility. Advances in nanomaterial engineering and machine learning algorithms are improving the accuracy of force decoupling and the efficiency of signal processing. This review systematically examines the working principles, strategies for performance enhancement, data processing methods, and cross-domain applications of triaxial tactile sensing. Instead of focusing primarily on materials or individual sensing mechanisms, it highlights critical performance trade-offs and co-optimization frameworks involving sensing performance, durability, and integration, to promote the widespread adoption of intelligent tactile systems across various industries.

Smart Materials and Devices
Openalex Percentile: Top 19%
Advanced Sensor and Energy Harvesting Materials
1.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.