Digital Twin Development for Serial Manipulators: Data Driven Optimized Planning and Sequencing of Tasks
Initially, the part recognition system of shaft
assembly parts is constructed with the
trained deep learning model using computer
vision (CV) techniques. The CV system at the
station requires a 3-dimensional (3D)
camera in this application as the point cloud
data provides more accuracy for cobot
motion and material handling processes.
Afterward, the new cobot motion planning
and simulation for specific shaft assemblies
are performed by a robotic engineer as
shown in Figure 8. Once the new cobot
motion path is achieved, the engineer can
review and validate it with the virtual cobot
in VML. When the confirmation is complete,
the new cobot path is released to the robot
controller, and the cobot setup is ready to
execute the customized shaft sub-assembly
operations.
Figure 8: The cobot simulation model running in the robot motion planning software
or a real physical cobot on the shop floor,
based on the requirements. In VML, the
cobot DT model is running and links with the
actual cobot at the shaft assembly station.
The detail cobot DT design and
implementation steps are published in a
Novelty & innovation of the DT model
In order to minimize the effect on the on-
going full gearbox assembly line process,
applying the cobot digital twin model to
validate and verify a newly developed cobot
motion plan is cost-effective and efficient.
This is because the planning, simulation,
analysis and verification of the new cobot
motion plan can be completed with the
virtual cobot (cobot DT model), which can
then be connected to the simulation model
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November 2019