Tech Briefs Magazine - December 2021 - MD-6

here, the safe prevention of accidents is
the top priority to avoid any risk to the
health of human colleagues. The use of
robots can also result in high costs and
idle time; for example, if they damage
workpieces or other automation devices
through incorrect movements. Here too,
camera systems help increase the reliability
of systems with integrated robots.
In addition to avoiding these kinds of
unwanted situations, " robots that see "
offer many other advantages. They enable
more flexible processes since the
evaluated image data can be used to
precisely control the robotic movements.
Even simple tasks such as gripping components
from a defined position can fail
without the use of image processing if a
component doesn't arrive exactly in the
expected position where a robot is designed
to pick it up.
In many cases, this is no problem for
a robotic system enhanced with a vision
system. A camera takes an image of the
inaccurately positioned component. The
subsequent image analysis calculates its
deviation from the position and then
forwards the corrected 2D or 3D gripping
coordinates to the robot's control
system. Within process-dependent limits,
this method ensures that components
are picked up reliably. The ultimate
challenge in taking up components is
so-called bin picking. For a robot to
grip parts that lie unsorted in a container
requires sophisticated vision systems.
They handle the detection of the
next pickable component along with the
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exact specification of its 3D position and
forward this information to the robot.
Based on the current state of technology,
this task would be unsolvable in many
cases without using image processing.
Setting up the Vision System
Properly is Crucial
Which image processing system can
be optimally combined with a robot in
a particular application depends on several
factors. One fundamental criterion
is the camera's positioning in the system.
It can be permanently installed above
a robot cell (off-arm), for example, or
attached directly to the robot arm (onarm).
In the second scenario, the robot's
" sense of sight " is available very close
to the action or at the gripper, but the
movements require that the camera's
weight is as low as possible, that it's very
robust in terms of acceleration and vibrations,
and that the cable routing is
well designed and suitable for robots.
Before a " seeing " robot can be developed,
it's also necessary to answer
the fundamental question of whether a
traditional industrial camera or an intelligent
camera (smart camera) is the better
choice for the task. In smart cameras, the
captured images are analyzed directly in
the camera housing, whereas industrial
cameras transmit their images to a PC system
for analysis, which generally enables
higher precision and speed for the image
processing than intelligent cameras.
Both architectures have their advantages
and disadvantages, so that criteria
such as the required precision, speed of
processes and movements, type of industrial
environment and the resulting
necessary protection class of the vision
system, the load-bearing capacity of the
robot, the preferred communication interfaces,
or additional influencing conditions
all decide which image processing
system is the optimal solution for the
respective application.
The camera isn't the only decisive factor
in the successful use of vision systems
in robotics. Lighting is an important
element for any image processing system.
Only with lighting that is optimally
adjusted to the task are cameras able to
record images in the required quality to
subsequently enable a reliable analysis.
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Tech Briefs Magazine - December 2021

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