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Path Driven Dual Arm Mobile Co-Manipulation Architecture for Large Part Manipulation in Industrial Environments

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

4 Citas (Scopus)
2 Descargas (Pure)

Resumen

Collaborative part transportation is an interesting application as many industrial sectors require moving large parts among different areas of the workshops, using a large amount of the workforce on this tasks. Even so, the implementation of such kinds of robotic solutions raises technical challenges like force-based control or robot-to-human feedback. This paper presents a path-driven mobile co-manipulation architecture, proposing an algorithm that deals with all the steps of collaborative part transportation. Starting from the generation of force-based twist commands, continuing with the path management for the definition of safe and collaborative areas, and finishing with the feedback provided to the system users, the proposed approach allows creating collaborative lanes for the conveyance of large components. The implemented solution and performed tests show the suitability of the proposed architecture, allowing the creation of a functional robotic system able to assist operators transporting large parts on workshops.
Idioma originalInglés
Número de artículo6620
Páginas (desde-hasta)6620
Número de páginas1
PublicaciónSensors
Volumen21
N.º19
DOI
EstadoPublicada - 5 oct 2021

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

  1. ODS 9: Industria, innovación e infraestructura
    ODS 9: Industria, innovación e infraestructura

Palabras clave

  • Mobile co-manipulation
  • Force control
  • Human-robot interaction
  • Robotic application
  • Assistant robots

Project and Funding Information

  • Project ID
  • info:eu-repo/grantAgreement/EC/H2020/820689/EU/Seamless and safe human - centred robotic applications for novel collaborative workplaces/SHERLOCK
  • Funding Info
  • This work has received funding from the European Union Horizon 2020 research and innovation programme as part of the project SHERLOCK under grant agreement No 820689.

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