Large-Scale Tactile Detection System Based on Supervised Learning for Service Robots Human Interaction
In this work, a large-scale tactile detection system is proposed, whose development is based on a soft structure using Machine Learning and Computer Vision algorithms to map the surface of a forearm sleeve. The current application has a cylindrical design, whose dimensions intend to be like a human...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/2/825 |
| _version_ | 1797437216573620224 |
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| author | Fábio Cunha Tiago Ribeiro Gil Lopes A. Fernando Ribeiro |
| author_facet | Fábio Cunha Tiago Ribeiro Gil Lopes A. Fernando Ribeiro |
| author_sort | Fábio Cunha |
| collection | DOAJ |
| description | In this work, a large-scale tactile detection system is proposed, whose development is based on a soft structure using Machine Learning and Computer Vision algorithms to map the surface of a forearm sleeve. The current application has a cylindrical design, whose dimensions intend to be like a human forearm or bicep. The model was developed assuming that deformations occur only at one section at a time. The goal for this system is to be coupled with the CHARMIE robot, a collaborative robot for domestic and medical environments. This system allows the contact detection of the entire forearm surface enabling interaction between a Human Being and a robot. A matrix with sections can be configured to present certain functionalities, allowing CHARMIE to detect contact in a particular section, and thus perform a specific behaviour. After building the dataset, an Artificial Neural Network (ANN) was created. This network was called Section Detection Network (SDN), and through Supervised Learning, a model was created to predict the contact location. Furthermore, Stratified K-Fold Cross Validation (SKFCV) was used to divide the dataset. All these steps resulted in Neural Network with a test data accuracy higher than 80%. Regarding the real-time evaluation, a graphical interface was structured to demonstrate the predicted class and the corresponding probability. This research concluded that the method described has enormous potential to be used as a tool for service robots allowing enhanced human-robot interaction. |
| first_indexed | 2024-03-09T11:16:40Z |
| format | Article |
| id | doaj.art-2e0a96ecbf0e44a7b1dcd4e7c0158be7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:16:40Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2e0a96ecbf0e44a7b1dcd4e7c0158be72023-12-01T00:28:15ZengMDPI AGSensors1424-82202023-01-0123282510.3390/s23020825Large-Scale Tactile Detection System Based on Supervised Learning for Service Robots Human InteractionFábio Cunha0Tiago Ribeiro1Gil Lopes2A. Fernando Ribeiro3Industrial Electronics Department, University of Minho, 4800-058 Guimarães, PortugalIndustrial Electronics Department, University of Minho, 4800-058 Guimarães, PortugalINESCTEC, University of Maia—ISMAI, 4475-690 Maia, PortugalIndustrial Electronics Department, University of Minho, 4800-058 Guimarães, PortugalIn this work, a large-scale tactile detection system is proposed, whose development is based on a soft structure using Machine Learning and Computer Vision algorithms to map the surface of a forearm sleeve. The current application has a cylindrical design, whose dimensions intend to be like a human forearm or bicep. The model was developed assuming that deformations occur only at one section at a time. The goal for this system is to be coupled with the CHARMIE robot, a collaborative robot for domestic and medical environments. This system allows the contact detection of the entire forearm surface enabling interaction between a Human Being and a robot. A matrix with sections can be configured to present certain functionalities, allowing CHARMIE to detect contact in a particular section, and thus perform a specific behaviour. After building the dataset, an Artificial Neural Network (ANN) was created. This network was called Section Detection Network (SDN), and through Supervised Learning, a model was created to predict the contact location. Furthermore, Stratified K-Fold Cross Validation (SKFCV) was used to divide the dataset. All these steps resulted in Neural Network with a test data accuracy higher than 80%. Regarding the real-time evaluation, a graphical interface was structured to demonstrate the predicted class and the corresponding probability. This research concluded that the method described has enormous potential to be used as a tool for service robots allowing enhanced human-robot interaction.https://www.mdpi.com/1424-8220/23/2/825roboticsservice robotshuman-machine interfacetouch sensorMachine LearningArtificial Neural Networks |
| spellingShingle | Fábio Cunha Tiago Ribeiro Gil Lopes A. Fernando Ribeiro Large-Scale Tactile Detection System Based on Supervised Learning for Service Robots Human Interaction Sensors robotics service robots human-machine interface touch sensor Machine Learning Artificial Neural Networks |
| title | Large-Scale Tactile Detection System Based on Supervised Learning for Service Robots Human Interaction |
| title_full | Large-Scale Tactile Detection System Based on Supervised Learning for Service Robots Human Interaction |
| title_fullStr | Large-Scale Tactile Detection System Based on Supervised Learning for Service Robots Human Interaction |
| title_full_unstemmed | Large-Scale Tactile Detection System Based on Supervised Learning for Service Robots Human Interaction |
| title_short | Large-Scale Tactile Detection System Based on Supervised Learning for Service Robots Human Interaction |
| title_sort | large scale tactile detection system based on supervised learning for service robots human interaction |
| topic | robotics service robots human-machine interface touch sensor Machine Learning Artificial Neural Networks |
| url | https://www.mdpi.com/1424-8220/23/2/825 |
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