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H-R DATA VISUALIZATION OF THE DISTANCE TO THE OBJECT IN THE
COLLABORATIVE ROBOT WORKSPACE BASED ON HC-SR04 SENSOR
Vladyslav Yevsieiev1,
Svitlana Maksymova1,
Dmytro Gurin1,
Ahmad Alkhalaileh2
1Department of Computer-Integrated Technologies, Automation and Robotics,
Kharkiv National University of Radio Electronics, Ukraine
2Senior Developer Electronic Health Solution, Amman, Jordan
Abstract
The article considers the use of HC-SR04 sensors for measuring the distance to
an object in a collaborative robot workspace. Special attention is paid to the
visualization of the received data, which allows for effective analysis of information
about the robot's environment. The experimental results show that the HC-SR04 can
provide satisfactory measurement accuracy, which is important for the safety and
optimization of robotic systems. The use of data visualization helps to improve
interaction with the environment and increases the tasks efficiency.
Key words:
HC-SR04, Collaborative Robot, Data Visualization, Distance
Measurement, Robotics, Interaction Environment, Industry 5.0
Introduction
The rapid development of robotics has led to the emergence of the concept of
Industry 5.0, which involves the use of joint work of robots and humans. This has
created new advantages, as well as difficulties in work [1]-[16].
The relevance of the study of H-R (Human-Robot) systems for visualizing data
of the distance to the object in a collaborative robot workspace based on HC-SR04
ultrasonic sensors is due to the growing integration of robotic solutions into production
processes within the framework of the Industry 5.0 concept. This concept envisages
harmonious cooperation between humans and robots, focused on safe, effective and
personalized interaction, which is especially important in conditions of high
requirements for accuracy and efficiency [17]-[19].
The use of ultrasonic sensors for distance monitoring allows you to receive
information about the position of objects in real time, providing a warning about the
approach of the robot to a person or other objects, which increases the level of safety
in the work area. Visualization of this data in real time enables the operator to quickly
assess the situation and make the necessary decisions, minimizing the risks of
collisions [20]-[22]. Various methods and approaches can be used here [23]-[40].
Therefore, the study of such visualization contributes to the development of adaptive
technologies and increasing the efficiency of human-robot cooperation, which
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corresponds to the main principles of Industry 5.0 regarding humanity and an
individualized approach in automated systems.
Related works
The use of collaborative robots allows one to obtain the advantages of both
a robot and a human working simultaneously. However, this increases the safety
requirements for human work. Therefore, it is necessary to provide the robot with the
ability to determine the distance to objects, including humans. Many scientific papers
are devoted to solving this problem. Let us consider some of these papers.
Let us begin with the work [41]. Leichtmann, B., & Nitsch, V. Give an overview
of the literature on interpersonal distances focusing on human-robot interaction.
Findings on the most prominent factors are summarized and discussed with respect to
findings from distancing in human-human interaction and theoretical considerations.
Group of authors in [42] present an approach for safe, and object-independent
human-to-robot handovers using real time robotic vision, and manipulation. Putting a
high emphasis on safety, they use two perception modules: human div part
segmentation, and hand/finger segmentation.
The article [43] note that generalist robot must be able to complete a variety of
tasks in its environment. One appealing way to specify each task is in terms of a goal
observation. Learned dynamics models are a promising approach for learning about the
environment without rewards or task-directed data, but planning to reach goals with
such a model requires a notion of functional similarity between observations and goal
states.
Scientists in [44] focus on human-centered robotics. They propose a
categorization to organize the use cases of proximity sensors in human-centered
robotics. They present the sensing technologies and different measuring principles that
have been developed over the years.
The paper [45] presents iSDF, a continual learning system for real-time signed
distance field (SDF) reconstruction. Given a stream of posed depth images from a
moving camera, it trains a randomly initialized neural network to map input 3D
coordinate to approximate signed distance.
So, we see that determining the distance to objects is a multifaceted task that is
solved in order to solve more serious problems. Further in this article we will present
our methods for solving problems of determining the distance to an object.
Distance to the object data representation mathematical model
The mathematical model for representing distance data to an object in a
collaborative robot workspace based on HC-SR04 ultrasonic sensors is based on the
principles of displaying and receiving an ultrasonic signal, and also takes into account
factors affecting the accuracy of measurements. The HC-SR04 sensor measures the
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International Journal of Multidisciplinary Research
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distance by calculating the time the sound wave travels from the transmitter to the
object and back to the receiver. The general viewof the HC-SR04 sensor is presented
in Figure 1, and its technical characteristics are presented in Table 1.
Figure 1: General view of the HC-SR04 sensor
Table 1: Technical characteristics of the HC-SR04 sensor
Characteristic
Value
Supply voltage
5 В
Current consumption
15 mA
Operating frequency
40 kHz
Measuring range
2 cm - 400 cm
Measurement accuracy
± 3 mm
Trigger pulse width
10 µs
Viewing angle
about 15°
Working temperature
-15°C … +70°C
Output
Digital pulse duration proportional
to distance
Reaction time
less than 15 ms
Dimensions
45 mm × 20 mm × 15 mm
As can be seen from Table 1, the characteristics make the HC-SR04 effective for
various applications in robotics and short-range object detection systems, including
obstacle tracking and real-time object distance measurement.
We maintain the following parameters:
- measured time , which reflects the interval between signal transmission and
reception. Its accuracy is limited by sensor resolution and signal stability;
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- speed of sound , which varies depending on temperature, humidity and air
pressure. To increase accuracy in conditions of variable temperature, correction
according to the formula
reduces errors;
- distance as the distance value. The larger the value of , the greater the
potential measurement error, as the accuracy is limited by the maximum measured time
and speed of sound.
The basic equation for calculating the distance ( ) to an object
(1)
- distance to the object in meters;
- speed of sound in air (approximately 343 m/s at a temperature of 20°C);
- the signal transit time from the sensor to the object and back, measured in
seconds.
Division by 2 is carried out because includes the time for the signal to reflect
back to the sensor.
Calculation of measurement error (
) depends on a number of factors,
including temperature stability, accuracy of time measurement, and limitations of the
sensor itself. Mathematically, the error can be estimated as:
(2)
- total error in distance measurement;
- sound speed error, which can change with temperature and pressure;
- error in time measurement, which can be caused by processing delays or
sensor resolution limitations.
Since the speed of sound depends on temperature, its value can be refined to
improve accuracy. Taking into account the temperature (°C), the speed of ultrasound
is calculated as:
(3)
Thus, for each sensor measurement in real environmental conditions, the speed
of sound can be adjusted to increase the accuracy of the result.
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The final result is presented as the distance to the object, taking into account the
error:
(4)
- the real distance to the object, taking into account the error.
This model makes it possible to obtain an estimate of the distance to an object in
a collaborative robot workspace, taking into account the main factors affecting
accuracy. Taking into account variables such as temperature, as well as the internal
errors of the measurement system, allows for higher accuracy, which is especially
important in collaborative systems where safety is a priority.
To test the proposed mathematical models and expressions for calculating the
distance to the object in a collaborative robot workspace, a layout was developed, the
general view of which is presented in Figure 2.
Figure 2: General view of the layout for calculating the distance to the object in
a collaborative robot workspace
Development of a program for testing the calculation of the distance to an object
in the a collaborative robot workspace
The choice of the Python programming language for the implementation of the
H-R (Human-Robot) program for visualizing object distance data in a collaborative
robot workspace is justified due to its high performance in the field of scientific and
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technical application development, as well as convenience in working with real-time
data processing . Python has a rich set of libraries for interacting with hardware, such
as pySerial, which allows you to efficiently receive data from the HC-SR04 ultrasonic
sensors via the serial port. The PyQt5 library provides flexibility and power for the
development of a graphical interface, which is important for visualizing information
and ensuring convenient interaction of the operator with the system. PyQt5 also
supports cross-platform compatibility, which allows you to use the developed solution
on different operating systems without significant changes in the code. The
pyqtgraph.Qt library is distinguished by the speed of displaying graphs and the ability
to process data in real time, which is critical for timely visualization of the distance to
objects and assessment of potential threats in the work area. Pyqtgraph has an intuitive
interface for creating graphs and allows you to dynamically update them, which
increases the efficiency and accuracy of situation assessment. Together, the
combination of Python, PyQt5 and pyqtgraph.Qt provides a convenient, powerful and
fast tool for implementing a visualization system based on ultrasonic sensors, which
makes it the optimal choice for such tasks. We will describe a number of the most
interesting functions of the implementations of the developed H-R (Human-Robot)
program for visualization of distance data to the object in a collaborative robot
workspace.
def __init__(self):
# Initialize the connection to the COM port
self.serial_port = serial.Serial('COM3', 9600, timeout=1)
This piece of code is responsible for initializing the connection to the serial port,
which is critical for receiving data from the HC-SR04 ultrasonic sensors. Specifying
the port 'COM3' and the data transfer rate of 9600 bps, the program establishes a
connection with the hardware device, which allows reading the measured values of the
distance to the object. The argument timeout=1 sets the maximum time to wait for
receiving data, which prevents the program from hanging in the absence of a signal.
Thanks to this initialization, the program can correctly interact with the sensors and
provide visualization of the received data in real time.
# Histogram for the first sensor
self.plot1 = self.win.addPlot(title="Sensor 1: Distance (Green) and Error
(Red)")
self.plot1.setYRange(0, 300) # Діапазон значень по осі Y
self.plot1.setLabel('left', 'Distance', units='cm') # Назва осі Y
self.plot1.setLabel('bottom', 'Sensor 1', units='') # Назва осі X
self.distance_bar1 = pg.BarGraphItem(x=[0], height=[0], width=0.6,
brush='g') # Гістограма відстані
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self.error_bar1 = pg.BarGraphItem(x=[1], height=[0], width=0.6, brush='r')
# Error histogram
self.plot1.addItem(self.distance_bar1)
self.plot1.addItem(self.error_bar1)
This piece of code is responsible for creating a histogram to visualize the
distance and measurement error data from the first ultrasonic sensor. It customizes the
graph by specifying a range of values for the Y axis, as well as names for the X and Y
axes, which help the user understand what data is being displayed. Next, two histogram
columns are created: one for distance, shown in green, and one for error, which is
shown in red. By adding these elements to the graph, the program provides clarity and
ease of understanding of the received data, which is important for analyzing the
operation of the sensor.
# We calculate the error as a percentage of the distance
error1 = distance1 * 0.05 # 5% error for the first sensor
error2 = distance2 * 0.05 # 5% error for the second sensor
return distance1, error1, distance2, error2
This code snippet calculates the distance measurement error for two ultrasonic
sensors using a specified percentage (5%) of the measured distance. Determining the
error is important for assessing the accuracy of the data, as it helps identify possible
inaccuracies in the measurements. The result of the execution of this code is the return
of distance values and corresponding errors for both sensors, which provides further
analysis and visualization of the obtained data. Thus, this fragment contributes to the
creation of more accurate and reliable graphs for displaying data in the program.
def update(self):
# Distance and error readings for each sensor
distance1, error1, distance2, error2 = self.read_data()
if distance1 is not None and error1 is not None and distance2 is not None
and error2 is not None:
# Update the histogram for the first sensor
self.distance_bar1.setOpts(height=[distance1]) # Green bar for distance
self.error_bar1.setOpts(height=[error1]) # Red bar for error
This piece of code is responsible for updating data in the graphical interface of
the program in real time. The update method reads the distance and error values for
each sensor, which ensures that the displayed data is up to date. If the received data is
correct, it updates the height of the histogram bars: a green bar representing the
measured distance and a red bar representing the measurement error. Thanks to this
update, the user has the ability to observe changes in measurements in real time, which
is critical for monitoring and analyzing sensor performance.
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An example of graphic implementations of H-R (Human-Robot) visualization of
distance data to an object in a collaborative robot workspace is presented in Figure 3.
Figure 3: An example of graphic implementation of H-R (Human-Robot) data
visualization of the distance to the object with error calculation
Let us conduct a number of experiments to evaluate the developed software for
graphic implementation of H-R (Human-Robot) data visualization of the distance to
the object with error calculation.
The first experiment of measuring the distance to a stationary object. Place the
object at different distances (eg 10 cm, 50 cm, 100 cm, 200 cm) from the sensors and
record the values that are measured. Compare the results obtained with the actual
distances to determine the average measurement error. The results of the experiment
are shown in Table 2, and the combined graph is shown in Figure 4.
Table 2: Results of the first experiment.
Actual Distance (cm)
Measured
Distance
(cm)
Error (cm)
10
10.5
0.5
50
48.0
-2.0
100
98.0
-2.0
200
205.0
5.0
The average measurement error is 0.375 cm.
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Figure 4: Combined graph comparing measured and actual distances. In the
graph, the blue line represents the measured distances and the red dashed line
represents the actual distances
The obtained data show that the measured distances are generally close to the
actual ones, although minor errors are observed. The largest error was found at a
distance of 200 cm, where the measurement exceeded the actual value by 5 cm, while
for smaller distances the error remained within ±2 cm. The average measurement error
is only 0.375 cm, which indicates the high accuracy of the HC-SR04 ultrasonic sensors
at short and medium distances, but with a tendency for the error to increase at long
distances.
The second experiment is to use alternative measurement methods (eg laser
rangefinders) to compare with the data obtained from the HC-SR04. This will
determine the relative accuracy of the sensors. The data obtained during the experiment
are shown in Table 3, and Figure 5 shows a combined comparison graph.
Table 3: Results of the second experiment
Measurement
No.
Distance (m)
HC-SR04
Reading (m)
Laser
Rangefinder
Reading (m)
1
1
1.02
1.00
2
2
2.05
2.00
3
3
3.08
3.00
4
4
4.15
4.00
5
5
5.20
5.00
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Figure 5: Comparison chart of HC-SR04 and Laser Rangefinder Reading
accuracy
According to Table 3, the laser rangefinder shows higher measurement accuracy
compared to the HC-SR04 ultrasonic sensor. Laser sensor measurements are more
stable and closer to theoretical values at all tested distances. The HC-SR04, on the other
hand, is slightly overestimated, especially at longer distances, which may be due to its
limitations in scattering ultrasonic waves and sensitivity to changing environmental
conditions. This indicates that for tasks where accuracy over long distances is critical,
a laser rangefinder will be the better choice. An ultrasonic sensor, despite a certain
error, can remain effective for tasks of an average level of accuracy at short and
medium distances, where minor deviations are acceptable.
Conclusion
The article considered the use of HC-SR04 sensors for measuring the distance
to an object in a collaborative robot workspace and their visualization in the form of
graphs, which allows for more effective interaction with the environment. The results
of the experiments showed that, despite some limitations in accuracy, the HC-SR04 is
able to provide satisfactory data for medium distances. Visualization of the received
data allows for quick analysis of information about the robot's environment, which is
important for safety and optimization of its work. The study demonstrated that the use
of such sensors in real time can significantly improve the efficiency of collaborative
systems. In addition, combining data from the HC-SR04 with other types of sensors
can further improve the accuracy and reliability of measurements. As a result, this
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approach opens up new opportunities for the development of robotic systems that
require the integration of various technologies to ensure greater flexibility and
adaptability in complex environments. Prospects for further research include
improving data processing algorithms and testing new types of sensors to increase the
accuracy and speed of the robot's reactions.
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