IEEE Robotics & Automation Magazine - June 2021 - 52

delta robots to rapidly transfer recyclables from a conveyor
belt to a bin.
Besides commercial systems, recent research endeavors
aim to improve automated recyclable recovery technology.
For example, Zhihong et al. [10] used an industrial KUKA
manipulator with a two-finger system to sort waste according
to the vision-based classification of moving objects.
Similarly, we recently developed a robotic system that
accomplishes one sort per second, utilizing delta robot technology
[2]. Bircanoğlu
et al. [11] assessed wellknown
deep convolutional
neural network
(CNN) architectures for
the classification of different
types of recyclables.
Additionally, the
authors of [11] proposed
a deep learning architecture
for recyclables
classification, namely
RecycleNet, which has
been shown to be very
effective in categorizing
images of single objects.
In contrast to the preced●
Camera:
A stereo, full-high-definition ZED camera enabled
the automated categorization of recyclable materials (see the
" Vision-Based Material Categorization " section). The camera
was placed 148 cm in front of the robot (in the direction
in which the recyclables were moving), at a height of 75 cm
above the conveyor belt, looking downward. To ensure constant
light conditions during data acquisition and system
operation, the camera was placed inside a box with LED
equipment. The camera's field of view covered the entire
width of the conveyor belt, providing information about the
shape and color of the transported waste.
●
Robotic technology is
coming to support (and
even replace) existing
installations by offering
a more effective and
autonomous alternative.
ing, the current work focuses on much more complex multiobject
images, solving a three-fold problem related to object
identification (bounding box specification), localization
(masking), and material type attribution (classification).
This triplet makes our solution applicable in industrial conditions
where several potentially overlapping recyclables
shown in the very same image need to be identified, localized,
and classified.
Industrial Research Setup
The development of a robotic system that can be applied in
industrial environments presupposes extensive assessments
in realistic and demanding conditions. To this end, with the
help of waste management experts, a research setup was
implemented at Crete's MRF (Figure 1) to facilitate data collection,
experimentation, and system assessment. Key components
deployed in the robot cabin (see Figure 1) are
described in the following.
●
Robot: An ABB IRB360 delta robot was installed above the
conveyor belt to enable the picking and transfer of waste
to bins. The robot consisted of three high-torque servomotors
mounted on a rigid frame, each linked to a different
arm. The three arms were connected to a central
platform, driving it to move very fast and accurately in 3D
space. The robot had a payload of 6 kg, which was appropriate
for repetitive and rapidly completed applications.
●
Vacuum gripper: To enable automated picking and placing,
we attached a vacuum gripping module to the end
effector of the robot; it consisted of a vacuum blower that
provided high-volume suction to pick up and hold selected
materials (see the " Recyclable Object Manipulation "
section). The use of vacuum technology provides a robust
and low-cost solution for material transfer [1].
Material Detection and Manipulation
This article presents an integrated robotic system for recyclable
sorting that is composed of two main parts: a robotic
manipulator for the physical separation of waste to different
bins, depending on the material type, and a vision-based
material detection and categorization module.
Conveyor belt: Typically, industrial waste processing units
transfer recyclables on conveyor belts. We developed a replica
of such a setup through the installation of a 22.5-m
long, 1-m wide (usable width: 0.8 m) belt with a speed of
up to 0.15 m/s and an optical encoder system so that the
speed could be monitored.
●
Waste feeder: A dual feeder shed waste at a controlled rate
onto the belt. The material came from a waste processing
installation that transferred uncontrolled urban waste or a
from a newly developed stream located above the conveyor
to feed the system a controlled waste mix for experimentation
purposes.
52 * IEEE ROBOTICS & AUTOMATION MAGAZINE * JUNE 2021
Picking and Placing Moving Recyclables
In the recycling industry, waste is typically preprocessed to
facilitate material recovery. In our setup, recyclables are filtered
through a trommel (a rotating punch plate) to remove
small objects. At the end of the trommel, recyclables fall
onto a conveyor belt to be transported to the robotic sorting
installation. The conveyor belt moves at a constant velocity,
facilitating the real-time estimation of the location of identified
objects (see the " Vision-Based Material Categorization "
section) and thus the planning of the robot's activities.
Any object added to the recognized recyclables list is a
potential target for the robot. Let
robot's starting position and
Px yz00 00
=
Px yztt tt
=
(, ,) be the
(, ,) be the target's
v 02 m/s.=
.
position (the reference frame in Figure 2). The conveyor
moves along the x-axis at a constant velocity of
Then, the location of the moving target is computed by
()
Z
[
\
]
]
]
]
xx tv
yy
zz
tt t
pick
pick
pick
all
=+ #
=
=
t
t
t
=+
Hc
all
,
(1)

IEEE Robotics & Automation Magazine - June 2021

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