ArticleSensors (Basel, Switzerland)2018
Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human-Robot Interaction.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 64 citations in OpenAlex.
- A 3D-Force and Torsion Sensor Using Patterned Color Encoding.Sensors (Basel, Switzerland) · 2026Article
- Composites Based on Cotton Microfibers Impregnated with Magnetic Liquid for Magneto-Tactile Sensors.Materials (Basel, Switzerland) · 2023Article
- EEG guided electrical stimulation parameters generation from texture force profiles.Journal of neural engineering · 2022Article
- Recent Progress of Biomimetic Tactile Sensing Technology Based on Magnetic Sensors.Biosensors · 2022Review
- Identifying the Strength Level of Objects' Tactile Attributes Using a Multi-Scale Convolutional Neural Network.Sensors (Basel, Switzerland) · 2022Article
- A 3D-Printed Fin Ray Effect Inspired Soft Robotic Gripper with Force Feedback.Micromachines · 2021Article
- Texture Recognition Based on Perception Data from a Bionic Tactile Sensor.Sensors (Basel, Switzerland) · 2021Article
- A Deep Learning Method for Vision Based Force Prediction of a Soft Fin Ray Gripper Using Simulation Data.Frontiers in robotics and AI · 2021Article
- Proprioceptive Estimation of Forces Using Underactuated Fingers for Robot-Initiated pHRI.Sensors (Basel, Switzerland) · 2020Article
- Using 3D Convolutional Neural Networks for Tactile Object Recognition with Robotic Palpation.Sensors (Basel, Switzerland) · 2019Article
- Learning Spatio Temporal Tactile Features with a ConvLSTM for the Direction Of Slip Detection.Sensors (Basel, Switzerland) · 2019Article
- RGB-D Object Recognition Using Multi-Modal Deep Neural Network and DS Evidence Theory.Sensors (Basel, Switzerland) · 2019Article
- Open-Environment Robotic Acoustic Perception for Object Recognition.Frontiers in neurorobotics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human-robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.