08 cjece-2019-0002

Carpathian Journal of Electronic and Computer Engineering 12/2 (2019) 41-49
DOI: 10.2478/cjece-2019-0016
ISSN 1844 – 9689 41 https://www.degruyter.com/view/j/cjece
A survey in the design and control
of automated guided vehicle systems
A. P. Vancea
Electric, Electronic and Computer
Engineering, Technical University of
Cluj-Napoca,
North University Center Baia Mare,
Baia Mare, Romania
alexvancea92@gmail.com
I. Orha
Electric, Electronic and Computer
Engineering, Technical University of
Cluj-Napoca,
North University Center Baia Mare,
Baia Mare, Romania
ioan.orha@cunbm.utcluj.ro
Abstract — Automatic guided vehicles (AGVs) play an
important role in the small-scale industry as well as the large-
scale industry in handling materials inside factories from one
place to another. In the last days, the materials to be handled are
more numerous and as production and demand increase, it
strongly influences the transport of materials in desperate need
of a vehicle to distribute, position the materials within the
industry. AGVs are generally installed with wires at ground
level and signals are transmitted through them to be controlled.
Due to the emergence of the AGV, the workload of the human
being gradually decreased and the production efficiency
increased. Thus, the need for an AGV has become more
technologically important in the advanced robotic world.
Normally, these systems are integrated into a global production
system, where is a need to make direct changes in the design and
planning of the floor store to get most of them. But in the rapidly
changing production system and the adaptable floor store, the
implementation of AGV has become very important and
difficult, because it depends on many systems, such as wires,
frequency, total production, etc. Therefore, it is necessary to
develop an independent AGV, which can operate on its own and
make decisions based on changes in the environment.
Keywords — AGV, industry 4.0, vehicle, algorithms,
navigation, IoT;
I. INTRODUCTION
AGV is a material handling system used for horizontal
movement of materials. The AGVs were introduced in 1955.
The use of AGVs has increased enormously since their
introduction. The number of application areas and the
variation of types has increased significantly. AGVs can be
used both indoors and outdoors, participating in tasks such as
production, distribution, transshipment. AGVs are used to
transport materials of all types related to the manufacturing
process. According to Gotting, over 20,000 AGVs have been
used in industrial applications. Deposits and cross docks,
engineering centers are examples of distribution areas. The
AGVs are used in these areas for internal transport as an
example, pallets between the different departments, such as
the reception, storage, sorting and dispatch areas. In
transshipment systems, such as shipping containers, AGVs
deal with the transport of products between different modes of
transport. Getting presented an overview of the technology
available for automation in container terminals. Moreover, the
applicable navy-guidance systems and vehicle guidance are
described in various indoor/outdoor environments. Haefner
and Bieschke said that AGV systems can provide benefits to
both the port and its customers by executing shipping
applications between ships and inland transport, and AGVs
can also be used in the process of outdoor transportation [1].
An example of such a transport system is an automated
underground system with AGVs traveling in the pipes
between companies and an airport [1]. In such systems, we
observe a high traffic density and long tube driving times. It
has even been studied whether AGVs can be used as a
communication system between workstations. AGV
specifications differ depending on the environment. To carry
a container, the capacity of an AGV should be at least 40
tones. For a smaller capacity, it is necessary to transport pallets
to warehouses. Moreover, at terminals for guided automated
container vehicles, they use signs (ALVs). In this paper, we
will discuss the literature concerns - the use of AGVs in
manufacturing and the new fields of application, namely
distribution, transshipment - maintenance and transport
systems.
The most important differences between the traditional
and the new application areas are the number of AGVs used,
the number of transport applications, the degree of occupancy
of the AGV, the distances traveled and the number of pick-up
and delivery points where the transport applications become
available. Panic grades are used to carry a small number of
requests over short distances between selections, delivery
points. Unlike reinforcement systems, a large number of
AGVs have been used to perform a large number of
repetitions, transport tasks to container terminals and
exteriors. In previous studies, a research survey into the design
and control of automated systems of guided vehicles proved
to be concentrated in the literature related to the design and
control of AGV systems for the manufacture, distribution,
transshipment, and transshipment of port systems [2]. It was
concluded that most models can be applied to design problems
at teaching centers. Some of these models and new models
have already proved successful. The new analytical and
simulation fashion must be developed for large AGV systems

to overcome the high computation times, NP complexity,
congestion, bottlenecks and delays in the system and finite
planning horizons [3, 4, 5]. This paper discusses the literature
on different fermentation methodologies for optimizing AGV
systems for two significant scheduling and routing problems
in manufacturing, distribution, transshipment and port
systems. We have classified methodologies into mathematical
methods, simulation studies, heuristic meta-techniques and
approaches based on artificial intelligence. The motivation for
the classification of the programming and the routing is based
on their significance in flexible manufacturing systems.
II. WHAT IS AN AGV?
AGV is the acronym for Automated Guided
Vehicles [6]. Such vehicles are used in warehouses to
transport the goods. The AGVs look like little cars that can
move controlled in a warehouse. Controlled travel is ensured
by special software. With the help of this software, AGVs
know what to do, orient themselves in the warehouse and
avoid collisions both with each other and with other static
objects [7]. There are several types of automatic vehicles
produced by several companies. Each of these can be used for
moving pallets or even for lifting operations. Some of these
automatic vehicles can move shelves with products to be
arranged. The AGV's central processing system issues the
steering control as well as the speed of movement. For the
predefined manufacturing environment, the map is saved in
the AGV memory.
A general AGV system consists essentially of
peripherals vehicle on-site components, stationary control
system. The main components of the AGV system are THE
VEHICLE, ORIENTAL WAY SYSTEM, TRAFFIC
CONTROL, and MANAGEMENT SYSTEM. The flawless
interaction of these components ensuring the efficiency of the
work installation. AGV will guarantee a safe performance, as
well as the load and the surroundings.
A. VEHICLE
The vehicle is the central element of the AGV
because it fulfills the real transport task [8]. The vehicle is
individual depending on the design, purpose and activity
environment. They are used in several scenarios such as AGV
designed to improve the health care system, AGV developed
as a hotel service robot. Amazon has implemented the KWIA
robot for automatic storage and retrieval of products in the
system. The most common uses of these automatic vehicles
are in the production area when supplying the workstations
with raw materials. Also, they are used when picking up
finished or semi-finished products and bringing them to the
storage area. Automatic forklifts can be considered AGVs,
however, the common perception is that AGVs are the kind
of cars that come under the pallet and pick it up [9]. As I said
at the moment, they are used in production environments due
to the high costs of purchasing the systems.
In Fig.1 is presented an example of automatic pallet
truck.
Fig. 1. Automatic pallet truck
In Fig.2 is presented an example of automatic
conveyor.
Fig. 2. Automatic conveyor
The pallet truck is used to move palletized loads on a
predefined route, and the Carrier is used to move a unit load
from one station to another. It is suitable for the automatic
loading and unloading of pallets with mechanized lifting
platform
B. ORIENTAL WAY SYSTEM
The vehicle guidance system is the method by which
AGVs are defined and the vehicles are controlled following
predefined paths. The AGV system uses the guide path, it
chooses a path based on the programmed path [10]. It uses the
data provided by the sensors and is compared with the value
offered by the programmer. When AGV approaches a
decision point, it only has to decide whether to follow the path.

The most used guidance technologies in AGV are:
a. Landmark-based navigation
b. Behavior-based navigation
c. Vision-based navigation
a. Landmark-based navigation
The markup-based navigation technique is based on their
identification and subsequent recognition, distinct features of
an object in the environment that may be previously known or
dynamically extracted. The various technologies used in
navigation based on land marking are incorporated into a
guided wire and tape type system (line tracking robot). In the
wire-guided method, the wires are placed in a small channel
inside the workspace floor. AGV senses the low-frequency
current in the 1-15 kHz range for guidance vehicle. The guide
band or the next AGV line are robots that operate with the
vehicle that detects and follows a predefined line drawn on the
work floor. To track the line drawn on the floor, the robot used
an array sensor that sends the signal to the robot's control
system. According to the input signal, the central system
manipulates the robot to remain in operation while constantly
correcting the wrong movements of the robot through a
feedback mechanism thus forming a simple system of efficient
loops. MA Rahman described in his work an AGV is driven
by a sensor-driven actuator and its position, controlled by 3
infrared sensors connected to a PIC-16F877A
microcontroller, and that the system is automatically using the
inertial sensor the AGV is capable of collision avoidance.
In Bajastani's [11] work described using a robot that
follows the line that describes a cheap and simple navigation
technique using ATMEGA 16 microcontroller completely
automatically in the environment. This gives the robot the
ability to perform 90-degree curves, the ability to capture and
count the junctions. It uses LEDs with adjustable intensity and
light sensors for navigation, thus improving the robot's
performance in low light conditions.
In Fig.3 is presented an example of a robot that
follows a line that describes a cheap and simple navigation
technique based on landmark navigation;
Fig. 3. Landmark-based navigation
b. Behavior-based navigation
This type of paradigm has been accredited and suitable for
a suitable unstructured environment, as it can incorporate with
a large number of sensors. Behaviors for the best navigation
techniques also require a high level of computing power,
neural network, genetic algorithm [12] and more
combinations thereof. The behavioral navigation system uses
laser beam navigation technology for mobility. Laser beam
navigation technology is used to determine vehicle position
and navigation in the system. In LOTHER's work, we
discussed the technique of scanning the workspace to improve
the accuracy of the AGV. An important step considering the
laser navigation is the independence of the reflection marker
technique, which have a low installation cost and which will
allow overcoming the limitation of the currently used triangle
system, a system developed more than ten years ago.
c. Vision-based navigation
It is the latest guidance technology, which works without
continuously defining the path. Uses the reconnaissance
navigation system providing the position and angular speed
of an autonomous mobile robot [13]. Simion has defined the
mobile robot as the device that can move in the environment
with a certain degree of autonomy. Then the navigation
associated with the external sensor that captures information
from the workspace by measuring proximity and visual
image. Malhotra and Al's research discussed the design of a
mobile robot for a dynamic environment [14]. A brain-
focused design for the autonomous robot by integrating the
central system as well as the sensor system was used to detect
obstacles in the warehouse. Ramos discussed the algorithm
for the vision base system using the neural network.
K.Kishor (2010) discussed the smooth motion of the robot in
the workspace.
In Fig.4 is presented an AGV that used a navigation
technique based on vision navigation;
Fig. 4. Vision-based navigation
The conventional robot loses a lot of time in tracking
the guideline, and the speed is restricted and the robot
oscillates a lot, so more time is used to navigate the route. The
controller calculated its current position, then calculated the
error from the target position when the error is raised the
engine will make a high turn if the error will decrease the
engine will make a low turn so that the magnitude of the turn
is proportional to the error [15]. Derivatives control is
implemented to mitigate the actual swing hours.

The basic idea of the algorithm is:
Error = Target Position - Current Position;
P = error × Kp;
D = error - previous error; stores changes from error to
deflected;
Correction = P+D;
Kp = constant proportional to the time error;
The correction term is applied for left and right engine
speeds. The constant in the algorithm has been adjusted to
minimize oscillation and exceedance at the highest possible
speed by adjusting, proportional constant
C. TRAFFIC CONTROL AND MANAGEMENT
SYSTEM
To function efficiently and increase AGV productivity,
the vehicle should be well managed and minimize waiting
time at the loading/unloading station. Traffic control
managed by AGV using on-board vehicle detection and zone
control [16]. Kumanan describes the multiple objective
programming of AGV in a flexible manufacturing
environment, using optimization algorithms. He described
the algorithm for controlling traffic in the workspace. The
genetic algorithm is the algorithm based on the search for the
natural selection process. The ACO algorithm is used to find
the combination near-optimal program that satisfies both the
load balancing between the AGV, depending on the travel
time and the minimization of time. For efficient control, two
types of the control system are used inside the workplace:
1. Stationary control system
2. Peripheral control system
The stationary control system covers all the superior
control components. It maintains the administration of the
transport control, the optimization of the communication of
the program with another control system through a predefined
interface. It is also responsible for consumer interaction and
provides ancillary functions such as graphical visualization
and statistical analysis [17]. The peripheral control systems
manage various equipment onboard the vehicle such as the
battery charging system and load transfer mechanism.
The mathematical model can describe the AVG operation
in the factory/warehouse unit. The efficiency of the AGV can
be measured by the effective actuation time of the AGV from
loading to unloading cycle. For this mathematical model, we
can suppose that AGV moves at a constant speed throughout
the environment and ignores the effect of the deceleration of
the acceleration and other speed differences [18]. The time
for a typical delivery cycle is described as the AGV system:
1. Upload to the pickup station
2. Travel time to the teaching station
3. Unloading at the output station
4. Empty travel time
In Fig.5 is presented the route of an AGV from the
loading moment until the unloading moment known as
delivery time;
Fig. 5. The route of an AGV
To calculate the delivery time of the vehicle in an
environment we use the equation:
Te = T1 + Ld / v + Tu + Le/ v (1)
Where:
Te = delivery time (min / delivery)
Tl = loading time (min)
Ld = remote load loading until unloading
v = speed of the carrier
Tu = the download time of the station
Le = the distance of movement of the vehicle until the start of
the next delivery station.
To find the number of vehicles in an environment we use the
equation:
n = (2)
Where:
n = vehicle number
WL = working load (min)
AT = time available (min)
To find total workload time or the total amount of work
express in term of time we have the expression:
WL = RfTa (3)
Where:
Rf = total constant hourly delivery for the system
Ta = is defined by the time available per hour per vehicle
Using the above equation we can find how many AGV can
fit in certain workspace. It calculate the time require for
completing a task.
III. APPLICATION
Autonomy is the key factor for using AVG in different
fields. It will achieve a high degree of accuracy and accuracy
which will result in a complete reduction of system errors and
improvement time. Flexibility is the key factor that will help
AGV become popular in other material handling systems

[19]. The AGVs are used inside the factories, but they also
expand their spaces in other service sectors:
1. Material handling: used in extremely large car and
electronic factories, at the unloading station;
2. Warehouse: used in the electronic commerce warehouse
for the transport of the material;
3. Commercial: luggage transport to the airport, supermarket,
mall, floor treatment, such as washing, exchange;
4. Energy and defense: transportation of high-risk substances,
mapping of bombs and mines, recovery and elimination of
nuclear and steam generator inspection, pipeline inspection;
5. The medical service: delivers water and food medicine,
administrative reports, handling of hazardous materials, and
disposal of biological waste [20];
6. Personal care: Assistance for persons with disabilities and
early assistance with personal hygiene;
IV. CURRENT STAGE
Automated Guided Vehicles (AGVs) are also known by
other names, such as LGV (Laser-Guided Vehicles), Mobile
Robots, SGVs (Autoguided Vehicles), Guided Cars,
Autonomous Vehicles, Driverless Vehicles and other names
within the context. Industry 4.0;
Regardless of the vehicle structure (forklift, tractor,
trolley, etc.), several AGV guidance systems drive the AGV
and announce the management system with the positioning of
the AGV within the chosen framework. The AGV guidance
system allows the AGV to follow a defined route and to
announce the Management System with its real-time
positioning. Choosing the right AGV navigation technology is
essential, as it will influence the overall cost of the AGV
system.
Possible additional functions of sorting, decision
making, data transfer, data collection, weighing of goods,
organization, procedures, warehouse administration,
administration of storage sites, product recognition, mastering
various models, finding pallets, loading-trucks, security
intelligence, intelligent situational responses (fire alarm
switch, various modes of implementation), faster and more
complex work during shutdown periods (night recharge). The
traceability of logistics processes is extremely up to date. Each
movement is performed and recorded reliably. This creates an
end-to-end process, generating a history that is useful and
necessary for internal audits.
In summary, it can be said that AGV is a powerful and
necessary tool for modern logistics in all branches of industry,
including in the pharmaceutical and food industries [21].
A. Industry 4.0
Industry 4.0 is the subset of the fourth industrial revolution
concerning the industry. The fourth industrial revolution
encompasses areas that are not normally classified as an
industry, such as smart cities, for example. Although the terms
"industry 4.0" and "fourth industrial revolution" are often used
interchangeably, "industry 4.0" refers to the concept of
factories where machines are augmented with connectivity
and wireless sensors, connected to a system that can visualize
the entire line of production and makes decisions on its own
[22]. Essentially, Industry 4.0 describes the trend towards
automation and data exchange in technologies and
manufacturing processes that include cyber-physical systems
(CPS), the Internet of Things (IoT), the Industrial Internet of
Things (IIOT), cloud computing cognitive computing and
artificial intelligence.
The concept includes:
• Smart manufacturing
• Smart factory
• Industrial Internet of Things also called Internet of
Things (IoT) for manufacturing
Industry 4.0 favors what has been called a "smart factory".
Within modular structured smart factories, cyber-physical
systems monitor physical processes, create a virtual copy of
the physical world and make decentralized decisions [23]. On
the Internet of Things, cyber-physical systems communicate
and cooperate and with people in real-time, both internally and
through the organizational services offered and used by
participants in the value chain.
The term "Industry 4.0", shortened to I4.0 or simply I4,
was born in 2011 from a project of the high technology
strategy of the German government, which promotes
computerization of production. The term "Industry 4.0" was
publicly introduced in the same year at the Hannover Fair. In
October 2012, the Industry 4.0 Working Group presented a set
of Industry 4.0 implementation recommendations to the
German Federal Government. The members and partners of
the Industry 4.0 working group are recognized as the founding
parents and the driving force behind Industry 4.0. On April 8,
2013, at the Hannover Fair, the final report of the Industry 4.0
working group was presented. This working group was led by
Siegfried Dais (Robert Bosch GmbH) and Henning
Kagermann (German Academy of Sciences and Engineering).
Because the principles of Industry 4.0 have been applied
by companies, they have sometimes been re-branded, for
example, aerospace parts manufacturer Meggitt PLC has
marked its own M4 Industry 4.0 research project. , will affect
the labor market discussed in Germany under the theme
"Work 4.0".
Industry 1.0 refers to the first industrial revolution. It is
marked by a transition from manual production methods to
machines using steam and water. The implementation of new
technologies has been around for a long time, so the period to
which it refers is between 1760 and 1820, or 1840 in Europe
and the USA. Its effects had consequences on textile
manufacturing, which for the first time adopted such changes,
as well as the iron, agriculture and mining industry, although
it had an impact on the increasingly powerful middle-class
society. It also affected on the British industry at that time.
Industry 2.0; the second industrial revolution or better
known as the technological revolution is the period between
1870 and 1914. It was possible with the extensive rail
networks and with the telegraph that allowed the faster
transfer of people and ideas. It is also marked by the
increasingly present electricity, which allowed the
electrification of the factory and the modern production line.
It is also a period of great economic growth, with an increase
in productivity. However, this has led to an increase in

unemployment, as many workers have been replaced by
factory cars.
The third industrial revolution or Industry 3.0 took place
at the end of the twentieth century, after the end of the two
great wars, as a result of the slowing down of industrialization
and technological advancement compared to previous periods.
It's also called the digital revolution. The global crisis of 1929
was one of the negative economic developments that have
occurred in many industrialized countries since the first two
revolutions. The production of Z1 (electrically operated
mechanical computer) was the beginning of more advanced
digital developments. This continued with the following
significant advances in the development of supercomputer
communication technologies. In this process, where there was
extensive use of computing and communication technologies
in the production process. Cars have begun to abolish the need
for human power in life.
"Industry 4.0" is an abstract and complex term consisting
of several components when we analyze our society and
current digital trends closely. To understand how extensive
these components are, here are some digital technologies that
contribute as examples:
• Mobile devices
• Internet of Things (IoT) platforms
• Location detection technologies
• Advanced human-machine interfaces
• Authentication and fraud detection
• 3D Printing
• Smart sensors
• Big data analysis and advanced algorithms
• Multi-level customer interaction and customer
profiling
• Augmented / portable reality
• Fog, Edge and Cloud computing
• View the data and start the training "in real-time"
Mainly, these technologies can be summarized in four
major components, defining the term "Industry 4.0" or "smart
factory":
• Cyber-physical systems
• Internet of objects
• Cloud computing
• Cognitive calculation
With the help of cyber-physical systems that monitor
physical processes, a virtual copy of the physical world can be
designed [24]. Thus, these systems can make decentralized
decisions on their own and achieve a high degree of autonomy
(for more information, see "Characteristics of Industry 4.0).
As a result, Industry 4.0 networked a wide range of new
technologies to create value.
B. Software-level navigation techniques implemented to
facilitate AGV maneuverability
As the number of AGV systems increases in partial flow
systems, routing problems have increased the complexity of
AGV system management. AGV routing problems are one of
the basic problems in AGV systems. Each AGV should be
guided by efficient, collision-free routing algorithms [25] to
perform their tasks. The routing algorithm should be fast. That
is, an AGV system can have dozens of AGVs running
simultaneously if the algorithm's calculation costs a long time,
other AGVs can be blocked. Algorithms should be able to
solve deadlocks. For example, an AGV with a fork may need
to lower a load to the position that could block other AGVs.
This can be solved by enlarging the corridor which can be
difficult to do if the system is running.
The chosen path of the vehicle should be that it does not
affect the existing active travel schedule. Maxwell and Colab
developed a two-way AGV management system that
calculated the minimum number of AGVs in a time-dependent
environment to maximize efficiency with ignored time and the
collision problem is not solved. Gaskins and Tochoco
developed a time-dependent model for solving the
unidirectional AGV route optimization solution by
minimizing the total travel distance with poor robustness (the
algorithm can easily run in dead blocks). Broadbent and Colab
invented the concept of a short conflict-free route.
An array was generated by applying the Dijkstra algorithm
that describes the time taken by a vehicle to a node. Conflicts
at a node or recovery conflicts can be resolved by slowing
down the vehicle, which is still scheduled [26]. The procedure
can be applied to any type of path, namely unidirectional and
bidirectional models and introduce the concept of virtual
tunnel for the bi-directional path consisting of several
segments of multi-directional paths. This concept allows
multiple AGVs that cross at the same time at an intersection.
Egbelu is one of the first researchers in AGV bi-directional
route planning issues and has discussed various types of bi-
directional AGV travel problems. Egbelu also compared
unidirectional AGV systems and two-way AGV systems in
the same environment. The solution seemed effective in
simple models, but in large-scale maps, deadlocks can be
easily reached. Dowland focused on collision-free problems
using Petri-net to find possible collisions in a given node [27].
The solution is composed of delays and deviations while Endo
used the same mesh with the genetic algorithm. Both solutions
are only effective in small area problems, but when dealing
with large/complex maps, the algorithm is not able to respond
to real-time requests.
AGV routing algorithms can be classified into 2
categories: AGV routing algorithms through wires and
wireless AGV routing algorithms [28]. The first algorithms
are normally modeled by graph theory, and the latter is
modeled by field, space analysis.
Algorithms implemented for navigation:
• Wire-Guided AGV routing algorithms
• Dijkstra Algorithm
• Floyd-Washal Algorithm
• Minimum-spanning-tree

• Time-dependent algorithms
• Genetic Algorithm in shortest path searching
• Wireless-Guided AGV routing algorithms
• A* Algorithm
• Bezier Curve with Genetic Algorithms (BCGA) in
AGV route optimization
• Artificial Potential Field Algorithm (APF) in AGV
routing problem
• Fuzzy logic approach in wireless-AGV path finding
• Particle Swarm Optimization (PSO)
• Neural Network approach
From the algorithm tests, the Bezier Curve solution is the
most unstable algorithm because it is very related to the
complexity of the maps, the particle density, the size of the
curve, the number of AGVs, It is not recommended for
complex maps or multi-AGV systems, but it can be used as
under the control of AGV positions (that is, if we have solved
the route through other efficient algorithms, we can use Bezier
Curves as a precise method of motion control to guide AGV).
Fuzzy logic is one of the easiest algorithms in
programming, accuracy is extremely high and is dependent on
the structure and parameters of the algorithm [29]. The
potential artificial field algorithm showed the adaptability of
complex maps and multi-AGV systems. If proper heuristic
methods are used, he is capable of jumping from blockages.
GA and PSO behaved a little harder in tests, especially when
the facilities are complex. The reason for this phenomenon is
that the algorithms are not as accurate as of the Dijkstra
algorithm. Meanwhile, due to the different heuristic methods
and the initialization of the algorithms, the results might be
different.
C. An example of a custom AGV. Development of a self-
guided tracking vehicle using a carrier wave detection
method
In order to develop the self-tracking of the carrier with an
AGV in an unlimited workspace, a dynamic distance
measurement was adopted, a system that uses the speed
variation between a radio wave and an ultrasonic wave that
propagates in the air. Microchip PIC 18F452 is used as a
control core. To facilitate the targeted functions, the
interaction in combination with high-quality sensors plays an
essential role [30].
By using a wireless radio transmitter and an ultrasonic
transmitter at the waist of the user inclined towards the sensors
in front of the AGV, the distance between the user and the
AGV can be calculated. To avoid collision with an obstacle,
an ultrasound transmitter is installed on both sides of the
AGV. Subsequently, a controller based on Fuzzy Thinking
will operate the motor movement allowing it to track and
avoid obstacles.
Fig.6 shows how the distance between the person and the
robot is calculated;
Fig. 6. The distance between receiver and transmitter
a. The principle of distance calculation
As shown in Fig. 6, the distance between the user and the
ultrasound receivers will be calculated using the varied wave
characteristic of radio and sonic rays. The speed of a radio
beam propagation with a light wave is much higher than the
propagation of an ultrasonic ray with a sound wave. As
shown in Fig. 7, two ray signals (a radio beam and an
ultrasonic beam) are transmitted from the transmitter attached
by the user. Timers (left / right) (ultrasound receivers) will be
operated when the radio wave is detected. The meters will
start to calculate the time until the ultrasonic rays arrive [31].
Fig.7 shows how the distance between the person
and the robot is calculated by using the speed difference of
the radio beam and the ultrasound beam;
Fig. 7. Detection of distance using the speed difference of the radio beam
and the ultrasound beam
b. Self-tracking system
As shown in Fig. 8, to control the movement of the robot
[32] ,which will keep an adequate distance behind the user, a
strategy based on Fussy rule, related to the detection of
distance (dR and dL) is adopted to control the rotation of the
motors [33]. The Fussy rule used in the robotic system is as
follows:
Rule 1: if d R, d L <110 (cm), then the engines stop;

Rule 2: if 110 (cm) ≤ d R, d L <120 (cm) then the engines
slow down;
Rule 3: If 120 (cm) ≤ d R, d L then the engines start;
Fig. 8. Position of the AGV in relation to the left / right ultrasound
received from the transmitter;
c. Kinetic control of the robot
For the robot to be able to follow the user's direction,
control is crucial [34].To act properly the direction of the
AGV, four cases of movement are classified:
Case 1: The signal for the right/left receivers disappears
Case 2: the signal for the receiver on the right side disappears;
the signal for the receiver on the left is detected.
Case 3: the signal for the receiver on the right side is detected;
the signal for the receiver on the left disappears.
Case 4: Signals for right/left receivers are detected.
For the status of case 1, there are three types of events
possible:
(a) An obstacle in front of the robot;
(b) The distance is too close;
(c) The distance is too far to be effective.
In this situation, the robot will wait for the signals to be
received at the left / right receivers.
For the condition of case 2, the robot is behind the user. In
this situation, dL detected is above the user's detectable
range; therefore, the robot will be positioned to the left until
both signals can be detected; Similarly, for the state in case
3, the robot must be on the left and behind the user, so that d
R detected is beyond the user's detectable range; This is why
the AGV will turn right until both (right/left) receiving
signals can be detected.
For the condition in case 4, both dR and dL can be
detected. This means that the distance between the AGV and
the user is within a detectable range. In this situation, it is
essential to keep the AGV directly behind and in the center
of the user.
V. CONCLUSIONS
There are several possible directions for further research.
We can improve the type of AGV guided tape using a
better navigation technique [35].
Any environment and cheap among autonomous robots
can be adopted.
There is a significant amount of difference between the
theoretical and practical time value of the work cycle being
optimized by adopting a different methodology [36].
Besides, one can think of a relaxation of the symbol
holding requirements in the traffic control system, so that
more vehicles can leave different crossing zones
simultaneously and, therefore, the performance of the AGV
system can be improved [37].
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