Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/85111
Título: | Large-scale tactile detection system based on supervised learning for service robots human interaction |
Autor(es): | Cunha, Fábio Ribeiro, Tiago Lopes, Gil Ribeiro, A. Fernando |
Palavras-chave: | Robotics Service robots Human-machine interface Touch sensor Machine Learning Artificial Neural Networks |
Data: | 11-Jan-2023 |
Editora: | Multidisciplinary Digital Publishing Institute (MDPI) |
Revista: | Sensors |
Citação: | Cunha, F.; Ribeiro, T.; Lopes, G.; Ribeiro, A.F. Large-Scale Tactile Detection System Based on Supervised Learning for Service Robots Human Interaction. Sensors 2023, 23, 825. https://doi.org/10.3390/s23020825 |
Resumo(s): | 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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/85111 |
DOI: | 10.3390/s23020825 |
ISSN: | 1424-8220 |
e-ISSN: | 1424-8220 |
Versão da editora: | https://www.mdpi.com/1424-8220/23/2/825 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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sensors-23-00825.pdf | 3,95 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons