Classification of Vision-Based Tactile Sensors: A Review

Fuente: arXiv
Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Li, Haoran, Lin, Yijiong, Lu, Chenghua, Yang, Max, Psomopoulou, Efi, Lepora, Nathan F
Format: Preprint
Veröffentlicht: 2025
Schlagworte:
Online-Zugang:
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
_version_ 1866916933142577152
author Li, Haoran
Lin, Yijiong
Lu, Chenghua
Yang, Max
Psomopoulou, Efi
Lepora, Nathan F
author_facet Li, Haoran
Lin, Yijiong
Lu, Chenghua
Yang, Max
Psomopoulou, Efi
Lepora, Nathan F
contents Vision-based tactile sensors (VBTS) have gained widespread application in robotic hands, grippers and prosthetics due to their high spatial resolution, low manufacturing costs, and ease of customization. While VBTSs have common design features, such as a camera module, they can differ in a rich diversity of sensing principles, material compositions, multimodal approaches, and data interpretation methods. Here, we propose a novel classification of VBTS that categorizes the technology into two primary sensing principles based on the underlying transduction of contact into a tactile image: the Marker-Based Transduction Principle and the Intensity-Based Transduction Principle. Marker-Based Transduction interprets tactile information by detecting marker displacement and changes in marker density. In contrast, Intensity-Based Transduction maps external disturbances with variations in pixel values. Depending on the design of the contact module, Marker-Based Transduction can be further divided into two subtypes: Simple Marker-Based (SMB) and Morphological Marker-Based (MMB) mechanisms. Similarly, the Intensity-Based Transduction Principle encompasses the Reflective Layer-based (RLB) and Transparent Layer-Based (TLB) mechanisms. This paper provides a comparative study of the hardware characteristics of these four types of sensors including various combination types, and discusses the commonly used methods for interpreting tactile information. This~comparison reveals some current challenges faced by VBTS technology and directions for future research.
format Preprint
id arxiv_https___arxiv_org_abs_2509_02478
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Classification of Vision-Based Tactile Sensors: A Review
Li, Haoran
Lin, Yijiong
Lu, Chenghua
Yang, Max
Psomopoulou, Efi
Lepora, Nathan F
Robotics
Vision-based tactile sensors (VBTS) have gained widespread application in robotic hands, grippers and prosthetics due to their high spatial resolution, low manufacturing costs, and ease of customization. While VBTSs have common design features, such as a camera module, they can differ in a rich diversity of sensing principles, material compositions, multimodal approaches, and data interpretation methods. Here, we propose a novel classification of VBTS that categorizes the technology into two primary sensing principles based on the underlying transduction of contact into a tactile image: the Marker-Based Transduction Principle and the Intensity-Based Transduction Principle. Marker-Based Transduction interprets tactile information by detecting marker displacement and changes in marker density. In contrast, Intensity-Based Transduction maps external disturbances with variations in pixel values. Depending on the design of the contact module, Marker-Based Transduction can be further divided into two subtypes: Simple Marker-Based (SMB) and Morphological Marker-Based (MMB) mechanisms. Similarly, the Intensity-Based Transduction Principle encompasses the Reflective Layer-based (RLB) and Transparent Layer-Based (TLB) mechanisms. This paper provides a comparative study of the hardware characteristics of these four types of sensors including various combination types, and discusses the commonly used methods for interpreting tactile information. This~comparison reveals some current challenges faced by VBTS technology and directions for future research.
title Classification of Vision-Based Tactile Sensors: A Review
topic Robotics
url https://arxiv.org/abs/2509.02478