Mixed Reality Human–Robot Interface With Adaptive Communications Congestion Control for the Teleoperation of Mobile Redundant Manipulators in Hazardous Environments
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Otros documentos de la autoría: Marin, Raul; Szczurek, Krzysztof Adam; Matheson, Eloise; Rodriguez-Nogueira, Jose; Di Castro, Mario
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Título
Mixed Reality Human–Robot Interface With Adaptive Communications Congestion Control for the Teleoperation of Mobile Redundant Manipulators in Hazardous EnvironmentsAutoría
Fecha de publicación
2022-08-22Editor
IEEECita bibliográfica
Szczurek, K. A., Prades, R. M., Matheson, E., Rodriguez-Nogueira, J., & Di Castro, M. (2022). Mixed Reality Human–Robot Interface With Adaptive Communications Congestion Control for the Teleoperation of Mobile Redundant Manipulators in Hazardous Environments. IEEE Access, 10, 87182-87216.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/abstract/document/9857951Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Robotic interventions with redundant mobile manipulators pose a challenge for telerobotics in
hazardous environments, such as underwater, underground, nuclear facilities, particle accelerators, aerial or
space. ... [+]
Robotic interventions with redundant mobile manipulators pose a challenge for telerobotics in
hazardous environments, such as underwater, underground, nuclear facilities, particle accelerators, aerial or
space. Communication issues can lead to critical consequences, such as imprecise manipulation resulting in
collisions, breakdowns and mission failures. The research presented in this paper was driven by the needs
of a real robotic intervention scenario in the Large Hadron Collider (LHC) at the European Organization for
Nuclear Research (CERN). The goal of the work was to develop a framework for network optimisation in
order to help facilitate Mixed Reality techniques such as 3D collision detection and avoidance, trajectories
planning, real-time control, and automatized target approach. The teleoperator was provided with immersive
interactions while preserving precise positioning of the robot. These techniques had to be adapted to
delays, bandwidth limitation and their volatility in the 4G shared network of the real underground particle
accelerator environment. The novel application-layer congestion control with automatic settings was applied
for video and point cloud feedback. Twelve automatic setting modes were proposed with algorithms based
on the camera frame rate, resolution, point cloud subsampling, network round-trip time and throughput to
bandwidth ratio. Each mode was thoroughly characterized to present its specific use-case scenarios and the
improvements it brings to the adaptive camera feedback control in teleoperation. Finally, the framework was
presented according to which designers can optimize their Human-Robot Interfaces and sensor feedback
depending on the network characteristics and task. [-]
Publicado en
IEEE Access ( Volume: 10, 87182 - 87216)Derechos de acceso
info:eu-repo/semantics/openAccess
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