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DEVELOPMENT OF A PROGRAM FOR PROCESSING 3D MODELS OF
OBJECTS IN A COLLABORATIVE ROBOT WORKSPACE USING AN HD
CAMERA
Vladyslav Yevsieiev1, Svitlana Maksymova1, Ahmad Alkhalaileh2, Dmytro
Gurin1
1Department of Computer-Integrated Technologies, Automation and Robotics,
Kharkiv National University of Radio Electronics, Ukraine
2Senior Developer Electronic Health Solution, Amman, Jordan
Abstract
This article presents the research of methods for program development for
processing 3D models of objects in a collaborative robot workspace using an HD
camera. The features of creating and processing a point cloud in real time under
different lighting conditions and image refresh rates are considered, and the influence
of these factors on the accuracy of object recognition is also assessed. The presented
experimental results show the optimal settings to ensure stable operation of the system
in various production conditions. The proposed methodology allows to increase the
accuracy and speed of the manipulator operation due to improved visual processing of
images and 3D models.
Keywords:
Industry 5.0, 3D Model Processing, Collaborative Robot,
Manipulator, Hd Camera, Point Cloud, Object Recognition.
Introduction
In today’s industrial environment, which is increasingly oriented towards
Industry 5.0, the interaction between humans and robots is becoming particularly
relevant. Collaborative robot manipulators, or cobots, perform tasks that were
previously only available to humans, providing high accuracy, flexibility and speed in
production. One of the main conditions for their effective operation is the ability to
accurately process and interpret data about the environment, especially in the form of
three-dimensional models of objects located in the robot’s working area [1]-[6].
Processing 3D models allows the robot to quickly and correctly adapt to changes in the
environment, avoid obstacles and interact with various objects, which significantly
increases its safety and efficiency. Various methods and approaches can be used here
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[7]-[25]. In particular, the availability of accurate algorithms for analyzing and
processing such models is key to the successful use of robots in various industries, from
the automotive and electronics industries to logistics and medicine.
At the same time, the development of effective algorithms for processing 3D
data is a challenge due to the large amounts of data, the complexity of calculations and
the need for high accuracy for successful recognition and classification of objects [26]-
[30]. The use of modern methods of machine learning and computer vision, in
particular deep neural networks, opens up new possibilities for processing three-
dimensional models [31]-[42]. The combination of such technologies with libraries,
such as Open3D and Point Cloud Library (PCL), allows you to create tools for detailed
scanning and analysis of objects, which reduces the risk of errors and increases the
speed of decision-making. In this context, research and development of algorithms for
processing 3D models of objects in the working area of cobots are important for
improving autonomous solutions and improving the quality of human-robot
cooperation. The growing demand for robotic systems that can interact with the
environment in real time and work safely next to people makes it necessary to conduct
such research. Therefore, the development of algorithms that optimize the processing
and recognition of objects in the robot space is a relevant topic for scientific research
and practical applications in the field of new generation industrial and service robots.
Related works
Detection and recognition of objects is becoming especially relevant due to the
increasing implementation of Industry 5.0 principles. Naturally, many scientists are
working on this problem and dedicating their works to it. Let's look at several recent
such works.
Let us begin with the work [43] where the authors present an overview of
Computer Vision use in the Fifth Industrial Revolution. They also identify and present
the changes between the two consequent periods: Industry 4.0 and Industry 5.0.
Rane, N. in [44] note that real-time object detection technologies, notably You
Only Look Once (YOLO) and Faster Region Convolutional Neural Network (Faster
R-CNN) algorithms are central for object detection. But the author explores challenges
such as data privacy concerns, computational complexity, and ethical considerations.
hese technologies are shaping a smarter, more connected, and efficient future across
diverse sectors.
In their turn, Wang, H., and co-authors in [45] propose a reasoning approach
towards factory unsafe states based on Digital Twin. First, a machine-readable semantic
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reasoning framework is introduced. Second, the ontology of unsafe states during
production is modeled. Then, a high-fidelity virtual Digital Twin Workshop is
constructed, which can simulate various workshop unsafe states and generate a virtual
dataset.
Industry 5.0 focuses on collaboration between humans and machines,
demanding robustness and efficiency and the accuracy of intelligent and innovative
components used [46]. The paper [46] note that multimodal co-learning is one such
approach to study the robustness of sensor fusion for missing and noisy modalities.
The paper [47] note that Cyber-Physical Human-centered Systems (CPHSs)
have emerged to leverage operator capabilities in order to meet the goals of complex
manufacturing systems towards human-centricity, resilience and sustainability. Such a
CPHS enables increased operator safety and operation tracking in manufacturing
processes that rely on collaborative robots and heavy machinery.
Researchers in [48] present an end-to-end vision system for bin-picking
applications at the edge of industrial premises via industrial computers, where bin-
picking refers to the dedicated selection and displacement of objects into designated
areas. Our system is designed for human-robot collaboration in a range of distinct
challenging industrial environments with a focus on automatically learning new
objects.
So we see that the problems arising during the implementation of the principles
of Industry 5.0 are quite diverse and attract many researchers. Further in this work we
will present our solution to the problem of ensuring safety, namely, the definition of
objects in the workspace of a collaborative robot.
Mathematical representation of image processing for object recognition and
point cloud construction with 3D model visualization
To develop an algorithm for processing 3D models of objects in a collaborative
robot workspace, at the first stage it is necessary to describe the sequence of the main
stages:
– reading a frame from the camera;
– image processing for object recognition;
– building a point cloud;
– visualization of a 3D model.
Let's consider each of these stages in the form of mathematical models. The
camera captures frames in the form of a pixel matrix, which is fed to the program input
as:
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I
H W
C
=
(1)
I
– image;
H
– image height;
W
– image width;
C
– number of color channels (in the case of a color image
C
=3).
For image processing, we will use the MobileNetV2 model, which is loaded
using TensorFlow, designed to classify objects in a frame. Before passing the image to
the model, it is scaled to size , which is the standard input image size for MobileNetV2.
Then the image preprocessing, that is, when the image is normalized and scaled
to values from -1 to 1, which is the standard normalization for the MobileNetV2 model,
can be described by the following representation:
127.5
127.5
norm
I
I
−
=
(2)
norm
I
– normalized image;
I
– original image;
127.5
– this is the average value of the range [0,255].
The value 127.5 in image preprocessing is often used in neural networks,
particularly when preparing input images for deep learning. This value is used as part
of the normalization process of pixel values to standardize the data before feeding it to
the neural network, which helps the network learn faster and more accurately.
The normalized image is fed into a model
MobilNet
f
that performs class prediction.
The model returns a probability vector for each class:
(
)
MobilNet
norm
y
f
I
=
(3)
K
R
y
– class probability vector;
K
– number of classes, for each of which the model returns the probability that
the image belongs to this class.
To create a 3D model, the program builds a point cloud, which is simulated based
on the intensity of the pixels of the frame. In real conditions, a special camera (for
example, an RGB-D camera) is used to read the depth of each point. To calculate the
spatial coordinates, it is proposed to use a fixed focal length of the camera
x
f
and
y
f
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(for the
x
and
y
axes). For each pixel
( , )
u
with depth
z
, the spatial coordinates are
calculated as follows:
(
)
x
x
u
c
z
x
f
−
=
(4)
(
)
y
y
c
z
y
f
−
=
(5)
( , )
z
d u
=
(6)
( , )
u
– pixel coordinates in the image;
( ,
)
x
y
с c
– coordinates of the center point of the image (camera center);
x
f
та
y
f
– focal length along the axes
x
and
y
;
( , )
d u
– depth value for a pixel
( , )
u
, which is simulated through normalized
gray values.
A point cloud is a collection of three-dimensional points that represent the spatial
coordinates
( , , )
x y z
of an object or scene. Typically, a point cloud is created by
scanning the object using 3D scanners, lidar, photogrammetry, stereo cameras, or other
technologies. Each point in the cloud corresponds to a specific location in space, and
sometimes also has additional attributes, such as color or reflection intensity. In this
case, a point cloud
( )
P
is a set of spatial points:
{( ,
, ) |
1, 2,..., }
i
i
i
P
x y z
i
N
=
=
(7)
N
– the number of points obtained from the image.
Visualization of a 3D model of objects in a collaborative robot workspace is the
visualization of a cloud of points in space, that is, a visualization object is created and
a cloud of points
( )
P
is added to it, which is updated with each frame iteration.
Software implementation of the program for processing 3D models of
objects in a collaborative robot workspace
The choice of the Python programming language for implementing a program
for processing 3D object models in the workspace of a collaborative robot manipulator
is optimal due to its powerful libraries and ease of integration with modern computer
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vision and machine learning tools. Python has a large set of ready-made libraries, such
as OpenCV for image processing, Open3D for working with 3D geometry, and
TensorFlow for deep learning, which allows you to significantly simplify the process
of creating complex object recognition and analysis algorithms. Due to the simplicity
of the syntax and high readability of the code, Python is an ideal choice for developers
working with artificial intelligence algorithms and 3D data processing, as it allows you
to quickly prototype and test solutions. Python's high compatibility with various
hardware platforms and operating systems facilitates easy integration of software for
robotic systems in real conditions.
Let us present a description of the software implementation of the mathematical
representation of image processing for object recognition and point cloud construction
with 3D model visualization.
def predict_objects(frame):
img = cv2.resize(frame, (224, 224))
img = tf.keras.applications.mobilenet_v2.preprocess_input(img)
img = np.expand_dims(img, axis=0)
predictions = model.predict(img)
decoded_predictions
=
tf.keras.applications.mobilenet_v2.decode_predictions(predictions, top=3)
return decoded_predictions[0]
This code snippet is used to recognize objects in an image captured by a camera.
First, the frame is scaled to 224x224, which is a requirement of the MobileNetV2
model, and then the data is processed to match the model. The `model.predict(img)`
function makes a prediction for the objects in the frame, and then `decode_predictions`
decodes the results, returning a list of the three most likely objects and their
probabilities.
predictions = predict_objects(frame)
for i, (imagenet_id, label, score) in enumerate(predictions):
text = f"{label}: {score:.2f}"
cv2.putText(frame,
text,
(10,
30
+
i
*
30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
This code snippet displays the results of object recognition on a video frame in
real time. Specifically, it gets object predictions from the `predict_objects(frame)`
function, and then for each recognized object, writes its label (`label`) and probability
(`score`) to the video frame. Text is applied to the image using `cv2.putText` to visually
label the identified objects along with their probabilities at the given positions.
depth_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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depth_image = cv2.resize(depth_image, (640, 480))
depth_image = np.float32(depth_image) / 255.0
This code snippet processes a video frame, converting it to grayscale to simulate
image depth. First, `frame` is converted to black and white using
`cv2.COLOR_BGR2GRAY`, then resized to a standard 640x480 pixels. Finally, the
pixel values are normalized to a range of 0 to 1, which makes it easier to perform further
calculations to simulate the point cloud.pcd = o3d.geometry.PointCloud()
h, w = depth_image.shape
fx, fy = 500, 500 # Simulating focal lengths
cx, cy = w // 2, h // 2
points = []
for v in range(h):
for u in range(w):
z = depth_image[v, u]
x = (u - cx) * z / fx
y = (v - cy) * z / fy
points.append((x, y, z))
pcd.points = o3d.utility.Vector3dVector(np.array(points))
This code snippet creates a point cloud object, `pcd`, to display a 3D structure
based on a depth image `depth_image`. Each pixel in the image is converted to a 3D
coordinate (x, y, z) using the focal lengths `fx` and `fy` and the image center `(cx, cy)`
to calculate the spatial location of each point. The resulting coordinates are added to a
list `points`, which is then converted to an Open3D-compatible format and assigned to
`pcd.points`.vis.clear_geometries()
vis.add_geometry(pcd)
vis.poll_events()
vis.update_renderer()
This code snippet updates the point cloud visualization in the Open3D window.
It first clears the previous geometry using `vis.clear_geometries()`, and then adds a
new point cloud `pcd` using `vis.add_geometry(pcd)`. The `vis.poll_events()` and
`vis.update_renderer()` calls process the events and update the visualization to reflect
the current changes in real time.
An example of the program processing 3D models of objects in collaborative robot
workspace based on an HD camera is shown in Figure 1.
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a)
b)
a) «Video Stream» window
b) «3D Object Models» window
Figure 1:
Example of the program processing 3D models of objects in a collaborative
robot workspace based on an HD camera
Let us conduct a series of experiments to evaluate the performance of the
program for processing 3D models of objects in a collaborative robot workspace at
different settings of the video stream frame rate. We investigate how the program
processes data from the camera in real time under different settings and conditions, for
example, by changing the frame rate of the video stream. This will allow us to
determine the delays in displaying data and visualizing the 3D model and to assess how
quickly the system updates the image when the load increases. In the conclusions,
present a table with the data. Table 1 shows the values of the frame rate (FPS), the
average frame processing time, the image update delay and the point cloud update rate
in real time.
Table 1:
The first experiment results
Frame
rate
(FPS)
Average
frame
processing
time
(ms)
Image refresh delay
(ms)
Point cloud refresh
rate (FPS)
15
53
72
10
20
62
87
13
25
77
104
17
30
92
123
20
35
111
141
22
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40
136
164
24
45
154
186
25
50
176
203
27
Let us present the obtained data of the first experiment in the form of a combined
graph in Figure 2.
Figure 2:
Combined graph of 3D modeling program performance analysis
The data from the first experiment allows us to assess the impact of frame rate
on the performance and ability of the program to process 3D models in the
collaborative robot workspace. At a low frame rate of 15 frames per second (FPS), the
system demonstrates an average frame processing time of 53 ms and an image refresh
delay of 72 ms, and the point cloud refresh rate is 10 FPS. With an increase in frame
rate to 20 FPS, the frame processing time increases to 62 ms and the delay to 87 ms,
indicating an increased load on the system. At a rate of 25 FPS, frame processing
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increases to 77 ms and the delay to 104 ms, indicating that an increase in frame rate
requires additional computing resources and affects the refresh rate.
At 30 FPS, the processing time increases to 92 ms and the delay reaches 123 ms,
which is repeated at each subsequent increase: frame processing reaches 111 ms at 35
FPS and 136 ms at 40 FPS, and the delay increases to 141 ms and 164 ms, respectively.
At a high frame rate of 50 FPS, the system processes frames in 176 ms with an update
latency of 203 ms, indicating some limitations for real-time processing. The point cloud
update rate also increases with frame rate, reaching 27 FPS at the maximum input
stream rate of 50 FPS, indicating the system's ability to update the 3D visualization
faster as the amount of data increases.
The second experiment is associated with different lighting conditions, which
makes it possible to check how the results of recognition and point cloud construction
change when the illumination level changes. The results obtained during the second
experiment are given in Table 2.
Table 2:
The second experiments results
Lighting
level
(lux)
Point cloud
quality (%)
Object
recognition
accuracy
(%)
Frame
processing
time (ms)
Point cloud
update
delay (ms)
Notes
100
76
66
83
112
Low
light,
strong
shadows
300
82
78
73
94
Normal
daylight,
weak
shadows
500
91
87
65
83
Moderate
artificial
lighting
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700
96
89
62
81
Bright
artificial
lighting, no
shadows
1000
94
87
64
82
Very bright
lighting,
slight
overexposure
1500
71
63
92
107
Excessive
lighting,
overexposure
and glare
Let us present the obtained data of the second experiment in the form of a
combined graph in Figure 3.
Figure 3:
Combined graph of the impact of lighting on 3D model processing
Analysis of the obtained experimental data shows a significant influence of the
illumination level on the quality of the point cloud and the accuracy of object
recognition in the collaborative robot workspace. At low illumination (100 lux), the
quality of the point cloud is 76%, and the accuracy of object recognition is 66%, which
indicates a decrease in quality due to the presence of shadows. With an increase in
illumination to 300 lux, the quality of the point cloud and the accuracy of recognition
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increase to 82% and 78%, respectively, and the frame processing time decreases to 73
ms, which indicates an improvement in the conditions for processing. The optimal level
for processing is the illumination level within 500-700 lux, where the quality of the
point cloud reaches 91-96%, the accuracy of recognition is 87-89%, and the frame
processing time decreases to 62 ms. This is explained by the stability of the lighting
conditions and the absence of shadows. Overillumination conditions (1000 lux) show
a slight decrease in point cloud quality to 94%, while accuracy remains stable at 87%,
although slight overillumination appears. Under excessive illumination (1500 lux),
point cloud quality drops sharply to 71%, and recognition accuracy drops to 63% due
to the influence of glare, which significantly degrades the system's ability to accurately
recognize objects.
Conclusion
The article considered the process of developing software for processing 3D
models of objects in a collaborative robot workspace using an HD camera. Based on
modern methods of computer vision and neural networks, it was possible to create a
program that is capable of recognizing and visualizing objects in real time, using the
MobileNetV2 architecture for accurate and fast recognition. The aspects of integrating
3D visualization into the robot's working area to build accurate point clouds were also
considered, which allows forming three-dimensional models of objects and spatially
assessing their positions, which is especially important for collaborative environments.
The use of OpenCV and Open3D libraries ensured fast processing of the video stream
and convenient work with 3D data. The program allows robot-manipulators to work
more efficiently, reducing the risk of collisions and increasing safety due to the accurate
determination of the location of objects and the ability to recognize them in dynamic
conditions. This also opens up the possibility for further automation in industrial
applications, where the presence of an accurate 3D picture of the workspace can
optimize the performance of tasks by manipulators. The work demonstrates that the
further development of such software systems has great potential in the field of
robotics, especially in Industry 5.0, where accuracy and integration with artificial
intelligence are becoming critically important. The developed software solution can
become the basis for complex robotic systems that require advanced visual control and
analysis capabilities.
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Acumen:
International Journal of
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ISSN: 3060-4745
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