Cargando…
Deep Learning Framework for Controlling Work Sequence in Collaborative Human–Robot Assembly Processes
The human–robot collaboration (HRC) solutions presented so far have the disadvantage that the interaction between humans and robots is based on the human’s state or on specific gestures purposely performed by the human, thus increasing the time required to perform a task and slowing down the pace of...
Autores principales: | Garcia, Pedro P., Santos, Telmo G., Machado, Miguel A., Mendes, Nuno |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823442/ https://www.ncbi.nlm.nih.gov/pubmed/36617153 http://dx.doi.org/10.3390/s23010553 |
Ejemplares similares
-
Toward an Interactive Reinforcement Based Learning Framework for Human Robot Collaborative Assembly Processes
por: Akkaladevi, Sharath Chandra, et al.
Publicado: (2018) -
Recognition of Grasping Patterns Using Deep Learning for Human–Robot Collaboration
por: Amaral, Pedro, et al.
Publicado: (2023) -
The effect of human autonomy and robot work pace on perceived workload in human-robot collaborative assembly work
por: van Dijk, Wietse, et al.
Publicado: (2023) -
A Framework and Algorithm for Human-Robot Collaboration Based on Multimodal Reinforcement Learning
por: Cai, Zeyuan, et al.
Publicado: (2022) -
Evaluation of Collaborative Robot Sustainable Integration in Manufacturing Assembly by Using Process Time Savings
por: Calvo, Roque, et al.
Publicado: (2022)