Ubiquitous manufacturing system based on Cloud: A robotics application

Wang, Xi Vincent; Wang, Lihui; Mohammed, Abdullah; Givehchi, Mohammad

doi:10.1016/j.rcim.2016.01.007

Cited by 132 publications

(38 citation statements)

References 33 publications

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“…The service layer provides various taskrelated services, e.g., augmented reality (AR) based visualisation, collaboration services, and pre-and post-augmented services considering the contexts. On the other hand, resource monitoring is supported by sensors, 3D models, point clouds and remote computing resources [23,166,245,247,248,250,258,214].…”

Section: Context Awareness and Resource Monitoringmentioning

confidence: 99%

Symbiotic human-robot collaborative assembly

et al. 2019

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In human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbiotic communication and control methods have been a focus in recent years. These methods include voice processing, gesture recognition, haptic interaction, and brainwave perception. Deep learning is used for classification, recognition and context awareness identification. Within this context, this keynote provides an overview of symbiotic human-robot collaborative assembly and highlights future research directions.

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Section: Context Awareness and Resource Monitoringmentioning

confidence: 99%

Symbiotic human-robot collaborative assembly

et al. 2019

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“…The essential components of a robot are the mechanical manipulator, the actuators that convert electrical, hydraulic, or pneumatic energy into mechanical power for movement, the sensors that measure position, speed, force, or torque, the control unit, and the power unit (Yamamoto et al, 2016). The main tasks performed by robots in manufacturing are welding, handling, painting, assembling, and palletizing (Wang et al, 2017). In FMS's, loads are handled by automated storage and retrieval systems (AS/RS), composed of storage structures, stacker cranes and transelevators.…”

Section: Components Of An Fmsmentioning

confidence: 99%

Implementation of a Flexible Manufacturing System in a production cell of the automotive industry: decision and choice

Sellitto

Mancio

2019

Prod.

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“…In the cloud robot paradigm, there are various deployment models, including private, community, public, and hybrid clouds. Wang et al [20] introduced these models into the robot clouds. This study proposes and evaluates various connection methods that occur in the implementation of cloud robots.…”

Section: Faas-fr Model For Cognitive Robotsmentioning

confidence: 99%

A Function as a Service Based Fog Robotic System for Cognitive Robots

Ahn

2019

Applied Sciences

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Cloud robotics is becoming an alternative to support advanced services of robots with low computing power as network technology advances. Recently, fog robotics has gained attention since the approach has merit relieving latency and security issues over the conventional cloud robotics. In this paper, a function as a service based fog robotic (FaaS-FR) for cognitive robots is proposed. The model distributes the cognitive functions according to the computational power, latency, and security with a public robot cloud and fog robot server. During the experiment with a Raspberry Pi as an edge, the proposed FaaS-FR model shows efficient and practical performance in the proper distribution of the computational work of the cognitive system.

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Ubiquitous manufacturing system based on Cloud: A robotics application

Cited by 132 publications

References 33 publications

Symbiotic human-robot collaborative assembly

Symbiotic human-robot collaborative assembly

Implementation of a Flexible Manufacturing System in a production cell of the automotive industry: decision and choice

A Function as a Service Based Fog Robotic System for Cognitive Robots

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