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145
Measurement Enhancement of Ultrasonic Sensor using
Pelican Optimization Algorithm for Robotic Application
Hind Zuhair Khaleel1, Attarid Khudhair Ahmed1, Abdulkareem Sh. Mahdi Al-Obaidi2,*, Senny Luckyardi3, Dwi Fitria
Al Husaeni4, Rawnaq Adnan Mahmod5, Amjad J. Humaidi1
1Control and Systems Engineering Department, University of Technology- Iraq, Baghdad, Iraq.
2School of Engineering, Taylor’s University, Taylor’s Lakeside Campus, Subang Jaya, Selangor DE, Malaysia.
3Universitas Komputer Indonesia, Bandung, Indonesia.
4Universitas Pendidikan Indonesia, Bandung, Indonesia.
5Ministry of Electricity, Training & Energy Researches Office, Iraq.
Correspondence: E-mail: abdulkareem.mahdi@taylors.edu.my
A B S T R A C T
A R T I C L E I N F O
HC-SR04 ultrasonic sensor is one of the famous low-cost
sensors. It is measured distance from 2 to 400 cm. It depends
on ultrasonic sound waves that are sent by an electronic
device to determine the distance of the object, and the
reflected sound is converted into an electronic signal. This
paper proposed a flowchart of the modern optimization
method Pelican Optimization Algorithm (POA) to enhance
the distance measurement of ultrasonic sensor kind HC-
SR04. In addition, the experimental system designed and
interfacing between MATLAB with Ardunio is implemented
to easily save the measured and desired distances and enter
these distances into the POA as pelicans. The error
comparison between two methods implemented, one
method called the classical method, and another method is
proposed named POA. The results show the minimum
distance error between ultrasonic and object in POA is less
than the error without POA. The best-measured distance
results were achieved and approximately equal to the
desired distances. The proposed method of POA for
ultrasonic distance sensor enhanced by 99.97%. This sensor
accurately detects objects from 2 to 400 cm which can be
used in a robotic application.
© 2024 Tim Pengembang Jurnal UPI
____________________
Keyword:
Arduino,
Distance,
Pelican optimization algorithm,
Ultrasonic sensor.
Indonesian Journal of Science & Technology
Journal homepage: http://ejournal.upi.edu/index.php/ijost/
Indonesian Journal of Science & Technology 9(1) (2024) 145-162
Article History:
Submitted/Received 28 Aug 2023
First Revised 29 Oct 2023
Accepted 28 Nov 2023
First Available online 01 Dec 2023
Publication Date 01 Apr 2024
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1. INTRODUCTION
A sensor is a device that detects input of any kind from the physical world and reacts to a
variety of environmental phenomena (Abulude et al., 2023; Ratsame et al., 2021; Jebur, 2023;
Hasanah et al., 2020). People's daily lives are now better thanks to sensor technology. Based
on the information gathered by sensors, which are devices used to detect changes in the
source or surroundings, the response's design is created. A few sources that could be used
are light, temperature, motion, pressure, and others. Innovative sensor technologies are
employed in several contexts, including daily life, industry, and distance measurement (Javaid
et al., 2021) and in the field of medicine (Shero et al., 2020). Ultrasonic sensor kind HC-SR04
is one of the famous sensors. It is a low-cost sensor that can measure distances between 2
cm and 400 cm. It is a reasonably priced and user-friendly distance-measuring sensor. The
sensor has 2 transducers of ultrasonic. First is the transmitter sound wave and second receiver
wave sound. It has four pins: VCC, Trig, Echo, and GND (for ground) Trig is used for trigger
waves (Hatem et al., 2018).
The most popular Arduino board, the UNO, is powered by the Atmega328 processor and is
compatible with the majority of expansion board shields (Apellido et al., 2021; Sukmafitri et
al., 2019). The Arduino microcontroller comes in a variety of forms. The ATmega 2560 CPU
powers the Arduino Mega, which has more I/O pins and more memory than the UNO. The
Arduino connected and controlled an ultrasonic sensor (Kim et al., 2020). Many researchers
worked in ultrasonic HC-SR04.
Abdulkhaleq et al. (2020) examined the ultrasonic HC-SR04 sensor, one of the well-known
sensors coupled to the Arduino microcontroller, for its resolution capabilities. The essential
components of this sensor are a transmitter and a receiver that operate in the sonar
frequency band. Similar to the radar principle, it's used to show how far away things are.
According to the test, the authors found a characteristic called distance resolution that might
be added to this sensor's datasheet.
Qiu et al. (2022) studied examination of the foundations of each modern ultrasonic ranging
technique, the advantages and disadvantages of each technique, techniques for signal
processing, the effectiveness of the entire system, and important ultrasonic transducer
parameters. Additionally, the ultrasonic ranging systems' error sources and compensatory
strategies are talked about. An overview of the ultrasonic range technique was included.
Latha et al. (2016) presented Arduino board may influence its surroundings by controlling
Liquid Crystal Display (LCD). It perceives the environment by receiving input from a range of
sensors. Using "non-contact" technology, ultrasonic sensors can measure the distance
between target materials or objects in the air. They are simple to use and measure distance
without being harmed. An ultrasonic sensor identified obstacles and determined their precise
distance.
Zhmud et al. (2018) used ultrasonic HC-SR04 sensor that depended on a calculation
STM32VLDISCOVERY board. The traits of comparable models were provided for comparison.
Their sensor distance is used in robot applications. The same ultrasonic sensor type was used
in robot application as in. The robot with many tools is used for surveillance applications (Al-
Obaidi et al., 2021).
Optimization algorithms enable humans to get empathy from enormous volumes of fields
for speeding data. They help to overcome many problems. The most type of optimization
algorithm is the Grey-wolf optimization (Al-Qassar et al., 2021a) whale optimization algorithm
(Al-Qassar et al., 2021b). The Swarm Optimization (PSO), Genetic Algorithm (GA), and Particle
Ant Colony Optimization (ACO) (Ghaleb et al., 2023; Alawad et al., 2022; Humaidi et al., 2018).
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The butterfly optimization algorithm (Abdul-Kareem et al., 2022; Ali et al., 2022). One of the
modern optimization algorithms is called Pelican Optimization Algorithm (POA). Trojovský &
Dehghani et al. (2022) presented a pelican optimization algorithm which is summarized as
(POA) in the year 2022. It was a clever adaptation that allowed the pelican birds to become
skilled hunters, based on the method in which pelicans go about their hunts and the
techniques they employ. Other researchers worked on the modern method called POA
(Algamal et al., 2023).
The following points indicate the contributions of this study:
(i) Development of Pelican Optimization Algorithm for enhancing the measurements of
Ultrasonic sensor.
(ii) Conducting a comparison study between the POA-based method and the classic method.
In addition, we also added bibliometric analysis. Bibliometric is one of the best methods to
understand research trend. Previous studies on bibliometric analysis are presented in Table
1.
Table 1. Previous studies on bibliometric.
No
Title
References
1
Involving Particle Technology in Computational Fluid Dynamics Research:
A Bibliometric Analysis
Nandiyanto et al.
(2023a)
2
Bibliometric Computational Mapping Analysis of Trend Metaverse in
Education using VOSviewer
Muktiarni et al.
(2023)
3
The Use of Information Technology and Lifestyle: An Evaluation of Digital
Technology Intervention for Improving Physical Activity and Eating
Behavior
Rahayu et al.
(2023)
4
Strategies in language education to improve science student
understanding during practicum in laboratory: Review and
computational bibliometric analysis
Fauziah et al.
(2021)
5
How language and technology can improve student learning quality in
engineering? definition, factors for enhancing students’ comprehension,
and computational bibliometric analysis
Al Husaeni et al.
(2022)
6
Mapping of nanotechnology research in animal science: Scientometric
analysis
Kumar (2021)
7
Scientific research trends of flooding stress in plant science and
agriculture subject areas (1962-2021)
Nurrahma et al.
(2023)
8
Introducing ASEAN Journal of Science and Engineering: A bibliometric
analysis study
Nandiyanto et al.
(2023b)
9
A bibliometric analysis of chemical engineering research using
VOSviewer and its correlation with Covid-19 pandemic condition
Nandiyanto et al.
(2021)
10
A bibliometric analysis of materials research in Indonesian journal using
VOSviewer
Nandiyanto and Al
Husaeni (2021)
11
Bibliometric analysis of engineering research using Vosviewer indexed by
google scholar
Nandiyanto and Al
Husaeni (2022)
12
Bibliometric computational mapping analysis of publications on
mechanical engineering education using VOSviewer
Al Husaeni and
Nandiyanto (2022)
13
Research trend on the use of mercury in gold mining: Literature review
and bibliometric analysis
Nandiyanto et al.
(2023c)
14
Domestic waste (eggshells and banana peels particles) as sustainable and
renewable resources for improving resin-based brakepad performance:
Bibliometric literature review, techno-economic analysis, dual-sized
reinforcing experiments, to comparison with commercial product
Nandiyanto et al.
(2022a)
15
Bibliometric analysis of educational research in 2017 to 2021 using
VOSviewer: Google scholar indexed research
Al Husaeni et al.
(2023a)
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No
Title
References
16
A bibliometric analysis of vocational school keywords using VOSviewer
Al Husaeni et al.
(2023b)
17
Bibliometric analysis of high school keyword using VOSviewer indexed by
google scholar
Al Husaeni et al.
(2023c)
18
Bibliometric analysis of special needs education keyword using
VOSviewer indexed by google scholar
Al Husaeni et al.
(2023d)
19
Bibliometric analysis for understanding the correlation between
chemistry and special needs education using vosviewer indexed by
google
Bilad (2022)
20
Bibliometric analysis of engineering research using vosviewer indexed by
google scholar
Nandiyanto and Al
Husaeni (2022)
21
Sustainable development goals (SDGs) in science education: Definition,
literature review, and bibliometric analysis
Maryanti et al.
(2022)
22
Evaluation on research effectiveness in a subject area among top class
universities: a case of Indonesia’s academic publication dataset on
chemical and material sciences
Nandiyanto et al.
(2020)
23
Trends in research related to photonic crystal (PHC) from 2009 to 2019:
A bibliometric and knowledge mapping analysis
Wiendartun et al.
(2022)
24
The evolution of smart working and sustainability in socio-technical
perspective: A scientometrics technology analysis
Mubaroq et al.
(2020)
25
Teaching high school students with/without special needs and their
misconception on corrosion
Maryanti et al.
(2022)
26
Counseling guidance in science education: Definition, literature review,
and bibliometric analysis
Solehuddin et al.
(2023)
27
A bibliometric analysis: research trend of critical thinking in science
education
Misbah et al.
(2022)
28
The bibliometric analysis for identifying future research on habits of
mind topic
Hizqiyah et al.
(2022)
29
Digitalizing museums: A bibliometric study
Yulifar et al.
(2021)
30
The impact of problem-based learning toward enhancing mathematical
thinking: A meta-analysis study
Juandi and Tamur
(2021)
31
Computational bibliometric analysis of english research in science
education for students with special needs using vosviewer
Sukyadi et al.
(2023)
32
Renewable energy online learning: A systematic literature network
analysis
Nasrudin et al.
(2022)
33
Research trend of local wisdom in physics education from 2018 to 2022:
A bibliometric review and analysis
Misbah et al.
(2022)
34
Particle size and pore size of rice husk ash on the resin-based brake pads
performance: experiments and bibliometric literature review
Nandiyanto et al.
(2022b)
35
Teaching the concept of brake pads based on composites of palm fronds
and rice husks to high school students
Anggraeni et al.
(2022c)
36
Natural zeolite as the reinforcement for resin-based brake pad using dual
particle size
Nandiyanto et al.
(2022d)
37
The development of strength training instruction video for virtual
community of students IN pandemic era of COVID19
Rahayu et al.
(2022)
2. METHOD
2.1. Mathematical Model of POA
Pelicans have included the population, which is the basis of the population-based
algorithm (POA). Each pelican population represents a potential solution in population-based
algorithms. According to where they are in the search space, each population of members
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presented values of variables in the problem of optimization. Using Eq. (1), a lower boundary
and upper boundary for a problem are used to initialize the population members at random.
The stages of POA are explained and begin with the Initialization of the population. The
starting position of each pelican in the population is random and may be represented because
pelicans typically hunt for prey within a certain search region.
(1)
where equals from 1 to n, equals from 1 to m, = initial position of the pelican. The n is
the population number, m presented as a problem variable number, = number
randomly between 0 and 1 and , are lower and upper bound of m respectively
(Trojovský & Dehghani, et al., 2022).
The pelican population matrix can be expressed as Eq. (2) and = ith pelican
(2)
The fitness function represented as fitness function matrix as below:
(3)
1) Motion toward preying is called the phase of exploration
The pelicans locate prey in the first phase, and they then fly toward that place. The
scanning of the search of space and an exploration of is power of the POA are made possible
by modeling the pelican's tactical approach. The crucial aspect of POA is the produced
position of prey randomly inside the search for space. A strengthens POA's capability for
exploring a problem-solving domain precisely. Equation (4) uses mathematics to replicate the
aforementioned ideas and the pelican's approach to its target.
(4)
where
= updated position pelican of the exploration phase. = prey location, = random
value equals to 1 or 2.
This factor is randomly changed in each iteration. When this parameter's value is two, a
member experiences greater displacement, which may take them to new regions of a search
space. As a result, a parameter influences how well a POA can explore the search for space.
A pelican's new location is allowed if it increases the fitness function. An algorithm is stopped
from traveling to suboptimal locations during this form of updating, also known as effective
updating. Equation (5) is used to simulate this process.
(5)
where = updated position of ith pelican of exploration phase and = fitness function
value of exploration phase.
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2) Exploitation Phase
When pelicans reached the surface of the water for a second phase, pelicans stretched
their wings to push fish higher before catching them in their throat pouches. Many fish in
attacked regions are caught via pelicans as a result of this tactic. A POA converged to get
better places in the hunting zone as a result of displaying this pelican behavior. This procedure
improved the POA's capability for exploitation and local search. For the algorithm to converge
a better solution, it is necessary to mathematically investigate the locations near the pelican
site. Equation (6) uses mathematics to model the pelican's hunting activity.
(6)
Where = updated position pelican of exploitation phase and = fitness function value
of exploitation phase. The factor R equals 0.2. The
is a coefficientis a pelican
population radius neighborhood of . = counter of iteration, = maximum iteration,
coefficient helps the POA's exploitation power approach to the best overall
solution. In the beginning iterations, this coefficient's value is high, which causes
consideration to be given to a greater area surrounding each member. It lowers as the
method replicates more, resulting in a decrease in the radii for each neighborhood member.
For this reason, POA could adapt for the solutions that are near to a global solution and it
means perfectly global optimal depending on the utilization thought. This enables scanning
regions around every population member with fewer and also, more precise steps. In this
stage, an updated pelican position becomes:
(7)
where = updated position pelican of exploitation phase and = fitness function of
exploitation phase (Algamal, et al., 2023).
2.2. Proposed Flowchart
Figure 1 shows the proposed flowchart of this work. It begins with the initialization of all
the POA parameters. After the experimental set, up as described in sections later. The
ultrasonic sensor reads and evaluates the measured distances and saves these distances in
the MATLAB program, at the same time the measuring tape tool fixed it on the ground and
we manually computed the desired distance between the object and the sensor and save the
results in MATLAB Then the error computed due to Equation (9). This method is called a
classical method. The proposed POA begins with the chosen two distance variables. One
variable is the measured distance and the other is the desired distance. After setting the lower
bound and upper bound of two pelicans’ inputs (measured and desired) distances, the fitness
function of Equation (9) is minimized to evaluate the best error fitness function and best
pelicans of measured and desired distances and end the proposed POA.
2.3. The Principle Work of the HC-SR04 Ultrasonic Sensor
An Ultrasonic sensor type HC-SR04 was used to measure the distance ranging between 2
cm to 400 cm as mentioned in the introduction section. It contains a transmitter to generate
an ultrasonic wave named trigger (Trig) and a receiver called Echo (Seethai et al., 2013) the
principle work of this sensor is seen in Figure 2. The sound speed in the air is approximately
340 m/s. This information coupled with the time interval between sending and receiving the
sound pulses, is used by the Ultrasonic Sensor to calculate an object's distance. The distance
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is divided by two to take into consideration the fact that the sound wave moves both forward
and backward. The sensor distance in cm becomes (Seethai et al., 2013). The speed of sound
is marked as and equals 0.034 cm/second The Time is the time calculated between an
ultrasonic sensor and an object.
Ultrasonic sensor distance =
(8)
Figure 1. Proposed flowchart.
Figure 2. Ultrasonic signal principle work.
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2.4. Experimental Setup
In this work, an experimental setup of the hardware connected is shown in Figure 3. It
consists of the following: a microcontroller board called Arduino Uno which is based on
ATmega328. The breadboard is defined as a solderless tool designed for a temporary model
with the circuit. The Ultrasonic sensor type HC-SR04 is useful for measuring the distance
between the sensor and the object as described in the introduction section. The ultrasonic
sensor pins: trig, echo, VCC, and GND linked to Arduino Uno pins 10, 11, and 5 volts and GND
respectively using four wires. The five-volt power has been supplied to the Arduino Uno using
a USB cable from a laptop. The Arduino Uno interfacing with the MATLAB program on a laptop
is described in the next section.
2.5. Interfacing Arduino and MATLAB
The interfacing between MATLAB and Arduino Uno is implemented in this work using USB.
This is done by setting up the Arduino package on MATLAB program R2018b as seen in Figure
3. The following codes have been written on the MATLAB Command Window to connect our
Arduino with MATLAB as following:
>> a = arduino ()
a =
Arduino with properties:
Port: 'COM5'
Board: 'Uno'
Figure 3. Experimental setup of distance measurement.
2.6. Data Preparing
The dataset of this work includes two distances data. The first one is called measured
distances which are measured from ultrasonic sensors and saved in MATLAB. The second one
is called desired distances which are calculated from the measuring tape tool (put it on the
ground) and it is useful to measure the desired distance between the sensor and the object
manually. The error between measured and desired distances is evaluated as the following
equation (Seethai et al., 2013).
(9)
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2.7. Implementation POA
After the data is prepared and the error is computed between the measured and desired
distances of the ultrasonic sensor, the implementation of POA is executed in the MATLAB
program. The parameters of POA for this work are seen in Table 2. The Eq. (9) has been chosen
and minimized to become a fitness function.
Table 2. The POA parameters of enhanced ultrasonic sensor distance.
n (number of POA
population)
m (number of
variables)
T (maximum
iteration)
minimization
fitness function
431
2
10
2.0*10^(-6) cm
2.8. Bibliometric
The article data used in this research was taken using Publish or Perish 7 with the keywords
"Ultrasonic sensor AND Robotic Application AND Arduino" based on the title and abstract
requirements. Data collection was carried out on December 1, 2023. Each selected article is
a Google Scholar indexed publication published in an International Journal. Article research
years are limited from 2019 to 2023. Bibliometric analysis was carried out using the mapping
analysis method using the VOSviewer application. Detailed steps on how to install and use
Publish or Perish 7 and VOSviewer have been explained in our previous research (Al Husaeni
& Nandiyanto, 2022).
3. RESULTS AND DISCUSSION
Figure 4 shows the development of previous research regarding the use of Arduino
ultrasonic sensors as robotic applications. Figure 4a shows the development of research on
this theme based on search results from the Google Scholar database. It is known that the
number of publications has increased from 2019 to 2021, but the number of publications has
decreased in 2022 and 2023. The total number of publications regarding the use of Arduino
ultrasonic sensors as robotic applications is based on the results Publish or Perish 7 searches
as shown in Figure 4b are 999 with a maximum article search limit of 1000 articles. However,
we re-sorted the articles used as analysis material based on the completeness of the
metadata so that we found 988 that met the research criteria. The detailed number of
publications each year is 2019 totaling 153 publications, 2020 totaling 228 publications, 2021
totaling 257 publications, 2022 totaling 188 publications, and 2023 totaling 162 publications.
Figure 4. Research developments regarding the Arduino ultrasonic sensor as a robotic
application; (a) Research development, (b) Conclusion of search results.
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Figure 5 shows an analysis of topics that are often discussed in research regarding the
use of Arduino ultrasonic sensors as robotic applications. Figure 5a shows a visualization
network of connections between discussion keywords, while Figure 5b shows trends in
research topics based on the number of topic appearances. Based on the results of this
analysis, it is known that research on ultrasonic sensors is connected with other research
topics such as artificial intelligence, Arduino, microcontroller, robotics, and with other types
of sensors such as sonar sensors, pir sensors, temperature sensors, and ultrasound sensors.
Apart from that, research on ultrasonic is also connected to the use of cellular data
transmission such as the internet and Bluetooth.
From these topics we concluded that several topics had the highest number of
appearances, including Ultrasonic sensors discovered 732 times, Arduino mega discovered 58
times, internet discovered 46 times, motorbikes discovered 32 times, Arduino IDE discovered
25 times, robotic systems 24 times, robotics arm 24 times, sonar sensor 24 times, robotics 20
times, ultrasound sensor 21 times, Arduino Uno microcontroller 21 times, obstacle and
detection 21 times.
Figure 5. Analysis of previous research topics; (a) Network visualization; (b) Trend topic
based on frequency of appearance.
The result of our hardware experimental connection has been implemented as seen in
Figures 6 and 7. In these figures, the ultrasonic detects the object for this case study which
the distance between an object and a sensor is 20 cm, and 300 cm respectively. A POA is
implemented in MATLAB program version R2018b and according to Equations (1) to (7) it can
simulate the fitness function and minimum fitness function error between measured and
desired distance. The best minimum fitness error function achieved 2.0*10^(-6) cm at
iteration number 2 as seen in Figure 8 and this best fitness function is labeled as a red star.
The error computed before POA and due to Equation (9) equals 0.00745 cm.
Figure 9 shows the best POA desired and measured distances VS number of the population
which is equal to 431. From this figure, it can be concluded that the measured marked as blue
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color and the desired marked as red color after POA are approximately equal in their results,
and the error between them is near zero. The dimension of the object chosen is 30X20 cm.
The comparison between minimum errors between measured and desired distances due
to Eq. (9) without POA and with POA is seen in Table 3. The improvement results show that
the ultrasonic distance sensor with POA enhanced by 99.9700 %.
(1) Laptop. (2) Arduino cable. (3) Arduino Uno. (4) Breadbord. (5) Ultrasonic sensor. (6
measuring tape tool. (7) Object.
Figure 6. Case study of experimental work implantation for 20 cm detection.
(1) Laptop. (2) Arduino cable. (3) Arduino Uno. (4) Breadbord. (5) Ultrasonic sensor. (6
measuring tape tool. (7) Object.
Figure 7. Case study of experimental work implantation for 300 cm detection.
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Figure 8. POA fitness function error result.
Figure 9. Best POA desired and measured distances.
Table 3. Report of ultrasonic sensor distance performance improvement.
The error of the Classical
method
The error with the
proposed POA
Improvement
0.00745 cm
2.0*10^(-6) cm
99.9700 %
4. CONCLUSION
From this work, it can be concluded the following points. The first point designing and
implementing the ultrasonic sensor system using a pelican optimization algorithm for object
detection and it is low-cost and accurate distances. The second point minimizing the error
between measured and desired distances of the ultrasonic sensor for classical and POA
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methods. The third point is concluding the error of POA is less than the error without POA
(classical method). So that the best distances are achieved. The fourth point achieving high
improvement of POA reached 99.9700 %. The fourth point is that the POA is too fast and
accurate a method because the minimum fitness function is achieved in iteration 2 of 10
iterations. The fifth point is interfacing between MATLAB and Arduino used to speed up the
connection between them and save results easily for use in POA. In future work, the designed
system will be very useful in robotic applications to detect objects and obstacle avoidance. In
addition, in the future, it can be utilized different controller types which enhance the
performance of distance measurement of the ultrasonic sensor (Hameed et al., 2019; Abood
et al., 2023; Humaidi & Abdulkareem, 2019; Hassan et al., 2022; Husain et al., 2023).
5. AUTHORS’ NOTE
The authors declare that there is no conflict of interest regarding the publication of this
article. The authors confirmed that the paper was free of plagiarism.
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