Development of Pressure-based Displacement Sensor System for Inclination Measurement

Precise measurement of contact force and accurate shape detection is essential in various industrial applications. While conventional unidirectional sensors are effective in measuring the magnitudes of contact forces along a specific axis, they cannot measure parameters such as the inclination of the contact surface. This study introduces a multi-directional pressure-sensing system to overcome these limitations. A sensing module with a linear pressure–displacement relationship was developed, and a linear regression model was established through statistical analysis to verify its reliability. Additionally, the sensing modules were arranged at equal intervals of 120° to enable measurement of the contact surface’s inclination and pressure distribution. Based on experimental results, the proposed sensing module exhibited an error rate of less than 0.5%, which highlights its reliability. Moreover, the inclination measurement system maintained an error of 1.2% within a 5° range, thereby guaranteeing high accuracy throughout its operating window. The proposed multi-directional pressure-sensing overcomes the limitations of conventional unidirectional sensors and is expected to be effective in various systems requiring precise contact force and inclination measurements, such as robotic grippers, medical devices, and industrial automation systems.

Authors

Publication Details

Journal
Journal of Institute of Control Robotics and Systems
Published
2025-04-13
DOI
https://doi.org/10.5302/j.icros.2025.24.8005
Primary Topic
Inertial Sensor and Navigation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development of Pressure-based Displacement Sensor System for Inclination Measurement

Se-jeong Kim, Seong‐Ho Son, Gun-Soo Shin, Chang‐Sei Kim et al.
Journal of Institute of Control Robotics and Systems
Inertial Sensor and Navigation
article

Development of Pressure-based Displacement Sensor System for Inclination Measurement

Se-jeong Kim, Seong‐Ho Son, Gun-Soo Shin, Chang‐Sei Kim, Dong-yeon Kim
article en

Abstract

Precise measurement of contact force and accurate shape detection is essential in various industrial applications. While conventional unidirectional sensors are effective in measuring the magnitudes of contact forces along a specific axis, they cannot measure parameters such as the inclination of the contact surface. This study introduces a multi-directional pressure-sensing system to overcome these limitations. A sensing module with a linear pressure–displacement relationship was developed, and a linear regression model was established through statistical analysis to verify its reliability. Additionally, the sensing modules were arranged at equal intervals of 120° to enable measurement of the contact surface’s inclination and pressure distribution. Based on experimental results, the proposed sensing module exhibited an error rate of less than 0.5%, which highlights its reliability. Moreover, the inclination measurement system maintained an error of 1.2% within a 5° range, thereby guaranteeing high accuracy throughout its operating window. The proposed multi-directional pressure-sensing overcomes the limitations of conventional unidirectional sensors and is expected to be effective in various systems requiring precise contact force and inclination measurements, such as robotic grippers, medical devices, and industrial automation systems.

Journal of Institute of Control Robotics and SystemsVol. 31(4)
Affordable and clean energy
Openalex Percentile: Top 90%
Inertial Sensor and Navigation
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.