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Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition

This study presents a novel approach to cope with the human behaviour uncertainty during Human-Robot Collaboration (HRC) in dynamic and unstructured environments, such as agriculture, forestry, and construction. These challenging tasks, which often require excessive time, labour and are hazardous fo...

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Detalles Bibliográficos
Autores principales: Yalçinkaya, Beril, Couceiro, Micael S., Soares, Salviano Pinto, Valente, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099038/
https://www.ncbi.nlm.nih.gov/pubmed/37050446
http://dx.doi.org/10.3390/s23073388
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author Yalçinkaya, Beril
Couceiro, Micael S.
Soares, Salviano Pinto
Valente, Antonio
author_facet Yalçinkaya, Beril
Couceiro, Micael S.
Soares, Salviano Pinto
Valente, Antonio
author_sort Yalçinkaya, Beril
collection PubMed
description This study presents a novel approach to cope with the human behaviour uncertainty during Human-Robot Collaboration (HRC) in dynamic and unstructured environments, such as agriculture, forestry, and construction. These challenging tasks, which often require excessive time, labour and are hazardous for humans, provide ample room for improvement through collaboration with robots. However, the integration of humans in-the-loop raises open challenges due to the uncertainty that comes with the ambiguous nature of human behaviour. Such uncertainty makes it difficult to represent high-level human behaviour based on low-level sensory input data. The proposed Fuzzy State-Long Short-Term Memory (FS-LSTM) approach addresses this challenge by fuzzifying ambiguous sensory data and developing a combined activity recognition and sequence modelling system using state machines and the LSTM deep learning method. The evaluation process compares the traditional LSTM approach with raw sensory data inputs, a Fuzzy-LSTM approach with fuzzified inputs, and the proposed FS-LSTM approach. The results show that the use of fuzzified inputs significantly improves accuracy compared to traditional LSTM, and, while the fuzzy state machine approach provides similar results than the fuzzy one, it offers the added benefits of ensuring feasible transitions between activities with improved computational efficiency.
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spelling pubmed-100990382023-04-14 Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition Yalçinkaya, Beril Couceiro, Micael S. Soares, Salviano Pinto Valente, Antonio Sensors (Basel) Article This study presents a novel approach to cope with the human behaviour uncertainty during Human-Robot Collaboration (HRC) in dynamic and unstructured environments, such as agriculture, forestry, and construction. These challenging tasks, which often require excessive time, labour and are hazardous for humans, provide ample room for improvement through collaboration with robots. However, the integration of humans in-the-loop raises open challenges due to the uncertainty that comes with the ambiguous nature of human behaviour. Such uncertainty makes it difficult to represent high-level human behaviour based on low-level sensory input data. The proposed Fuzzy State-Long Short-Term Memory (FS-LSTM) approach addresses this challenge by fuzzifying ambiguous sensory data and developing a combined activity recognition and sequence modelling system using state machines and the LSTM deep learning method. The evaluation process compares the traditional LSTM approach with raw sensory data inputs, a Fuzzy-LSTM approach with fuzzified inputs, and the proposed FS-LSTM approach. The results show that the use of fuzzified inputs significantly improves accuracy compared to traditional LSTM, and, while the fuzzy state machine approach provides similar results than the fuzzy one, it offers the added benefits of ensuring feasible transitions between activities with improved computational efficiency. MDPI 2023-03-23 /pmc/articles/PMC10099038/ /pubmed/37050446 http://dx.doi.org/10.3390/s23073388 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yalçinkaya, Beril
Couceiro, Micael S.
Soares, Salviano Pinto
Valente, Antonio
Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition
title Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition
title_full Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition
title_fullStr Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition
title_full_unstemmed Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition
title_short Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition
title_sort human-aware collaborative robots in the wild: coping with uncertainty in activity recognition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099038/
https://www.ncbi.nlm.nih.gov/pubmed/37050446
http://dx.doi.org/10.3390/s23073388
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