Masoni for the same weight user can check the

Masoni et al. worked
on the remote maintenance by the association of mobile devices and augmented
reality. Their work was focusing on reducing authoring time without using high
speed network. They analyses video and audio streaming, data transection,
maintenance operation and tracking algorithm for AR. The solution is a client
take a real-time operation picture to a skilled person (server) and the server
will send. They have used technology like unity 3D and Vuforia 3.

 

Data Scientist from GE
are doing R&D with Digital Twins for their Industrial assets and
manufacturing process. Their goal is to get better product, better maintenance
of their own plant, better product delivery and better services 4.

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Igor Verner, Michael Reitman
and Dan Cuperman worked with Digital Twin to train a learning robot. Their
focus was using the combination of IoT, Digital Twin and virtual sensors. They
have analyzed different robot gesture using a learning robot and Digital twin.
For each weight robot send the each joint angle to the server. User can check
the action in the DT app. Next time for the same weight user can check the
robot gesture in the DT app 6.

 

Björn Löfvendahl has
worked with ABB Robotics for the research on using AR application for industrial robots. His
research was to show a safety zone around the robot. A tablet app was used to
show the safety zone marked by AR. For the proof of concept he used technology
like Unity 3D, RobotStudio, SafeMove add-ons on ABB’s industrial robots. This
research target was to make a safe zone during working beside or with the
Industrial robot. He also mentioned 30 to 50 second delay problem during input
and output real world video streaming 8.

 

Pereira et
al. has researched on IoT based real-time cloud monitoring of a decentralized
photovoltaic plant. They have collected different information like voltage,
current, temperature, humidity using different sensors. Those sensors are also
connected to a Raspberry Pi device. RPi then send and receive data using a php
script through cloud. A website will show this real-time sensor data from cloud
storage. One user or multiple users can access and monitor the website
information. They have used Google cloud platform and RPi as cloud connector.
In their research they have analysed real-time data and crated solar Irradiance
data chart by comparing different sensors data 9.

 

Wang et al.
provides a solution for obstacle avoidance for NAO Humanoid robot using Google
Glass. In their solution, NAO robot is connected with the Google Glass through
TCP connection. NAO robot sends the video of its path to the user. Then the
user can detect the obstacle from the video and the Google Glass detects head
gestures using its multisensory data. Then the system conducts an effective
obstacle avoidance task for the NAO robot 13.

 

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