TECHNICAL REPORT ON
DIGITAL INNOVATIONS IN AUTONOMOUS GROUND VEHICLES FOR INDUSTRIAL
USE
ENG 485(TECHNICAL WRITING AND PRESENTATION)
UNDERTAKEN AT
AFE BABALOLA UNIVERSITY, ADO-EKITI
PREPARED BY
GROUP C (AEROSPACE ENGINEERING GROUP)
NAMES: 1.OLAOYE, T.G (21/ENG09/024)
2. IGBOKWE, D.G (21/ENG09/013)
3. AKPENE, N.E (21/ENG09/003)
4. AVURU, O.O (21/ENG09/006)
5. DJROAMEVI-LOUIS, O.A (21/ENG09/009)
6. AJAKAYE, N.O (22/ENG09/057)
7. ODIMGBE, O.C (21/ENG09/019)
8. OSIKE, C.S (21/ENG09/027)
9. SATI, I.P (21/ENG09/030)
10. YUSUFF, S. (21/ENG09/031)
SUBMITTED
TO
ENGR.DR.O.M.IKUMAKPAYI
31ST DEC,2024
1
ABSTRACT
The paper focuses on the historical overview and the commercial use of the autonomous ground
vehicles in different branches. AGVs have undergone a considerable development process, with
recent advancements in AI, GPS, and sensor technology making it possible for self-learning
AGVs [7]. There are numerous advantages of AGVs: increased efficiency, improvement in
safety, and gains in productivity in various industries [5]. However, the challenges of low return
on investment, difficulty of communication with other systems, and post-surveillance problems
have to be addressed first [4]. As a final note, this report states that the AGV might be the
harbinger of change in industries.
2
TABLE OF CONTENTS
Catalog
ABSTRACT...................................................................................................................................................... 2
TABLE OF CONTENTS..................................................................................................................................... 3
FIGURES ........................................................................................................................................................ 5
INTRODUCTION ............................................................................................................................................. 6
Background ........................................................................................................................................... 8
Problem Statement ............................................................................................................................... 8
Research Objectives .............................................................................................................................. 9
Methodology ....................................................................................................................................... 10
Organization........................................................................................................................................ 10
LITERATURE REVIEW ................................................................................................................................... 11
Introduction ........................................................................................................................................ 11
History of Development of AGVs ........................................................................................................ 12
Autonomy Innovations made in AGVs ................................................................................................ 12
Sensors ................................................................................................................................................ 12
Image Processing Sensors ................................................................................................................... 14
Path Planning ...................................................................................................................................... 15
Body of the review .............................................................................................................................. 15
FINDING THE GAPS IN THE LITERATURE /ADVANCEMENTS IN THE FINDINGS ........................................... 17
Identifying the gap in the literature.................................................................................................... 17
DISCUSSION AND INTERPRETATION OF RESULTS ....................................................................................... 20
Key Benefits of AGVs ........................................................................................................................... 20
Challenges to Consider........................................................................................................................ 21
3
Interpretation of Results. .................................................................................................................... 22
Best Practices for AGV Implementation: ........................................................................................... 23
CONCLUSION............................................................................................................................................... 25
REFERENCES ................................................................................................................................................ 26
APPENDIXES ................................................................................................................................................ 28
Glossary of Terms ............................................................................................................................... 28
List of Acronyms ................................................................................................................................ 28
1. AGV: Autonomous Ground Vehicle....................................................................................................... 28
2. AI: Artificial Intelligence ........................................................................................................................ 29
3. GPS: Global Positioning System ............................................................................................................ 29
4. IOT: Internet of Things ........................................................................................................................... 29
5. ML: Machine Learning ........................................................................................................................... 29
4
FIGURES AND TABLES
Figure 1
AUTONOMOUS GROUND VEHICLE (AGV) IN A WAREHOUSE SETTING [2] ......................... 7
Figure 2
AN UNMANNED GROUND VEHICLE [5] .............................................................................. 8
Figure 3 BLOCK DIAGRAM OF LINE FOLLOWER AGV [6] ................................................................... 9
Figure 4 AGV IN FARMING [5] .......................................................................................................... 11
Figure 5 Sensors [6] .......................................................................................................................... 12
Figure 6 GPS [6] ................................................................................................................................ 13
Figure 7 Image Processing Sensor [3]............................................................................................... 14
Figure 8
AGV Satellite Navigation System [5]................................................................................. 18
Figure 9 Navigation Systems for AGVs [5] ........................................................................................ 19
Figure 10
AGV Operating Within an Industrial Setting [12] ............................................................ 22
Figure 11 AGV designed for Industrial Applications [11] ................................................................. 24
5
INTRODUCTION
Times are changing, from the stone-age to the iron-age all the way down to the digital age, it is
safe to say that man has come a long way [1]. These constant changes also mean that the way
that man carried out activities adapted to which ever age that man found himself In. In the stone
age, most things were stone oriented meaning that stones were one of the main aids of man like
shelter and food for instance. An argument can be made that this progress man had from the
stone till the digital age was aided by the development of industries that helped assist man to
tackle ongoing problems [2]. These industries too also adapted to the time and dynamically
changed in relation to whichever time man found himself in, and thus meaning that from day
one, the workforce was mainly constituted of manual labor which gradually progressed as man
progressed.
From the development of AI to more advancements in technology, to more sophisticated
machines and infrastructure [6], it is no surprise that industries in the digital age will require
digital infrastructural and machine vehicle innovations to run more smoothly and efficiently to
enable better results and quality of work done [10]. This will be a leap from manual labor as
these machines have lesser probability of making errors and mistakes, they will be able to carry
out various activities as once also making them speed and time oriented. They will also mean
that they are even much easier to monitor and Control making them less stressful to deal with
and handle.
This will not happen without maybe the challenges that can arise from these like :




Hackers hacking into the autonomous machines since it is a programmed software [17].
The cost of purchasing these autonomous vehicles and maintaining them may come at a
high price that could negatively impact the financial strength of the company [14].
The autonomous vehicles and machines still have to be operated by a human being and
they could prove to be too complicated to understand [11].
Even creating a program that will be able to tackle certain scenarios and challenges can
be a problem [4]. Moreover, more issues like having am emergency aid if an accident
ever happens with this autonomous vehicles and machines [13].
With these possible and even likely more problems, it is key to tackle some of these and some
solutions like




Creating a software with strong security that will be harder for hackers to gain un
authorized access to.
Ensuring that the understanding of the automated vehicles is in line with that of human
beings.
Ensuring to create a connection with emergency Services in case anything goes wrong.
Ensure proper care of the vehicles to avoid spending unnecessarily on repairs and
damage.
There are so many scenarios of what could happen with autonomous vehicles in industries and
they all come with their advantages and disadvantages. And it is all about tackling the
6
disadvantages and Creating better solutions to the flaws that could arise from it. That is what this
project work’s concept is all about, from the basis to the actual digital innovations in industries.
The next few chapters in this project will enable a deeper dive on the digital innovations in
autonomous vehicles, the literature review will give a more detailed overview on this concept, to
how the project came together through research, to a more detailed discussion from the project
aim, to the Abstract that will give am executed overall summary of the entire project aim.
With the advent of digital technologies, many aspects of industries have undergone changes, and
this is no exception in logistics and manufacturing industries.
Autonomous ground vehicles represent one of the key ways in which digital innovations are
transforming industrial processes. AGVs are driverless vehicles that can move around
warehouses, factories, and other industrial settings independent of human intervention.
Figure 1
AUTONOMOUS GROUND VEHICLE (AGV) IN A WAREHOUSE SETTING [2]
7
Background
The idea of an AGV can be traced back to the 1950s when the first automated guided vehicles
were introduced. These were simple, pre-programmed AGVs that followed a fixed pre
programmed path. At present, after so many years, AGVs still evolve with advancements in
artificial intelligence, computer vision, and sensor technologies.
Figure 2
AN UNMANNED GROUND VEHICLE [5]
Problem Statement
While the industry-wise adoption of AGVs has gained increasing momentum, a plethora of
important issues still prevail. These relate to the exact industry to which they would have to be
8
applied and the complexity of their integration into workflows, and it would be necessary to
show if these will demand cybersecurity against hacking and data breaches.
Figure 3 BLOCK DIAGRAM OF LINE FOLLOWER AGV [6]
Research Objectives
The main focus of the report is discussing current digital innovations in industrial usage of
AGVs. The report shall seek to discuss, among others:
1. The development of AGVs and their applications within the industries.
2. Discuss the challenges and limitations for the use of AGVs in industries.
3. Discuss the use of digital technologies, such as AI and computer vision, that may further
develop the potentials of AGVs.
4. Make recommendations for the future in the development and application of AGVs to
industry.
9
Methodology
This report is informed by a comprehensive review of the existing literature relating to AGVs
and their applications in industry. The literature reviewed covers both academic journals and
conference papers, as well as industry reports. Current status of AGVs in industry also draws
from expert opinion and case studies.
The report focuses on the digital innovations in AGVs for industrial use. The report shall cover
industries such as manufacturing, logistics, and warehousing. The report does not cover AGVs
for non-industrial use, such as transportation or agriculture.
Organization
This report is organized into five chapters. Chapter 1: Introduction to the topic at hand, which
deals with digital innovations in AGVs used in industries. Chapter 2 develops a literature review
of the current state of development and some of the applications of the AGVs in different
industries; Chapter 3 focuses on finding the gap in the literature/ advancements in the finding;
Chapter 4 offers the discussion and interpretation of results. Chapter 5 concludes the reports.
10
LITERATURE REVIEW
Introduction
Whether its in agriculture, supply chain, or waste management. There have always been need for
doing things faster, better, and with lest human involvement. With the sudden growth of
Artificial Intelligence throughout the last 2 years , it is good that we look at the digital
innovations in Autonomous Ground Vehicles for industrial use.
The following review of literature confirms that there have been digital innovations in
Autonomous Ground Vehicles for industrial use and how we can further this innovations.
Figure 4 AGV IN FARMING [5]
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History of Development of AGV
The development of AGVs started back in the 1950s as a pre-programmed system (Automated
Ground Vehicles). The first AGV consisted of a tow truck which followed a wire to its
destination. In the 1960s and 1970s, the development was moved from simple tow trucks
performing basic tasks to more complex vehicles carrying out multiple tasks and transporting
various heavy loads.
As computer innovation became better, the quality of AGVs increased in turn, expanding across
multiple industries like manufacturing, farming, warehousing and logistics.
Coming into the 21 st century, AI, GPS and laser guidance has led to self-learning AGVs that
can operate even if the environment is changed.[1]
Autonomy Innovations made in AGVs
Sensors
AGVs make use of sensors present in the car for steering, and calculation of speed.
Figure 5 Sensors [6]
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Global Positioning System (GPS)
GPS is used to pinpoint the exact position and direction of the AGV. It is useful in AGVs that
have access to a precise map (i.e AGVs in the logistics sector). The GPS enables the AGV to
understand where it is in respect to its destination and road network. This information is used
to calculate the optimal routes for the vehicle to take when travelling.
Satellite visibility affects GPS accuracy especially areas with tall buildings or tree foliage to
hinder signals. Areas with clear view of the
sky with a standard GPS can achieve position accuracy of 5-15 meters.Such accuracy is enough
for manned vehicles, but for AGVs, the GPS mapping does not account for incoming vehicles on
either lane.
To combat this limitation, locally computed differential correction systems such as the Omnistar
VBS are used. These systems can achieveaccuracy of less than 1 meter. More sophisticated
services (Omnistar HP), can achieve accuracy of 10cm or lower. [2]
Information available from a GPS receiver are;



Horizontal speed and orientation relative to the true north.
Absolute position in a geodetic co-ordinate system.
Precise time and synchronized pulse per second. [2]
Figure 6 GPS [6]
13
Image Processing Sensors
Some of the sensors used in autonomous vehicle research include vision and image
processing sensors that help in identifying objects in the way.
Single camera systems are used for lane identification and for low-accuracy object
detection. Advanced camera systems use computer vision algorithms to detect and avoid
objects in the locale.
Multiple camera systems provide depth map for objects and can detect obstacles.
Camera systems are being made that are able to identify traffic signals [3].
Figure 7 Image Processing Sensor [3]
Real Time Decision-Making
After perceiving the obstacles in their surroundings, AGVs have to course correct in order to
avoid them and adhere to road regulations.
Decision-making involves algorithms to consider the AGVs speed, the closeness of other
vehicles and the environmental conditions. To do this, it is further enhanced by path planning;[3]
14
Path Planning
After the identification of the environment and obstacles within it, onboard intelligence
of the vehicle has to plot a route safely through the obstacles. Object detection and lane
identification provide the input for the algorithms like Dynamic Window Approach(DWA) to
chart the route.[3] Yijing et al. proposed an novel A* algorithm called A* with an Equal-Step
Sampling (A*ESS) algorithm to address the local path planning. [4]
Body of the review
What catches my eye in the various literature that I studied is that most of the industries are
trying to make Autonomous Ground Vehicles more specific for their industry, or at
least tweak the ones available since those available are not for their industry.
Research that motivates the integration of the Unmanned Ground Vehicles ramifications
onto the agricultural sector isn't sufficient[5].
Also, the academic research on the integration and sustainability assessment of autonomous
systems in a supply chain context is scanty [7].
This problem of not having industry-specific autonomous ground vehicles and the desire for
them is paramount for the digital innovations.
Autonomous ground industrial vehicles because, unless I read those suggesting
that the software that could help with specificity should be made or the software they
made was for specificity;
D. Bechris and co-authors aim to provide, through their work a tool in software to enable realtime navigation of the UGV in agriculture so accuracy and efficiency will be optimized
regarding all kinds of activities relevant to precision farming.[5].
N. Tsolakis and his pairs also talk about how the use of softwaresimulation tools to elaborate
and proactively evaluate the operational performance and sustainability implications of
Intelligent Autonomous Vehicles for fostering the establishment of bespoke Supply chain
isnecessary.[7] I also noticed that these software provided simulation systems that provides
simulations specific to their industry and that will help in producing industry-specific
Autonomous Ground Vehicles[5] [7].
However, I did notice that most of the literature were more centered on the western part of the
word except for T.Zhang and his pairs who talk about the developments of the Unmanned
Vehicles in china [6]. They should be more studies and research carried out for Autonomous
Ground Vehicles in other non-western countries.
While talking about places that were left out, there is something important I thing was not
included in most literature which is: the harmful effects of these Autonomous Ground
Vehicles on environment and society. No harmful effects were really emphasized upon in any
of these
literature. Could it be that they fear the emphasis of the harmful effects would affect the
commercialization of the Autonomous Ground Vehicles?
Another thing I think most literature didn't consider was the question of preference by human
workers in their various industries. These Autonomous Ground Vehicles are to work with
humans, and if human worker preference is a consideration in making them, that will make
the humans more open to the idea of integrating them into the industry.
15
Apart from the above, the more recent the article, there is a keen focus on how innovation in
AI is going to realize innovative applications of Autonomous Ground Vehicles[6].
Would the innovation in AI have a huge effect on making autonomous ground
vehicles successful?
16
FINDING THE GAPS IN THE LITERATURE /ADVANCEMENTS IN THE
FINDINGS
Identifying the gap in the literature
The literary gap identification is a crucial step before any research is conducted, and this is the
step that is undertaken in this subsection.
Given the AGV literature on the integration and innovative development of AGV systems in
factories and plants, there are several problems that need to be filed. For one, there is no clear
explanation for the integration concepts behind deploying AGVs across other industrial sectorss,
and most importantly there is an evident lack of proof regarding the effectiveness of strategies in
specific AGV deployment cases which especially AQUO three Parsons’ models intended to be
directed at.
However, the literature of AGVs doesn’t specifically target why those insead integrated are
required for that economc sector, for instance, agriculture, or why there is a need for oversight
over agriculture starting from the growth process through the production and distribution of the
final product across the value chain. And even if there is no readily market adaptation to the rest
of the antagonistic economies their AGVs are set up for, they don’t necessarily have to be
structurally integrated.
Additionally, the literature does not elucidate how such economic adaption can enable AGVs in
among other processes farming of accurately growing crops as is the case with GPS directed
farming. Furthermore, it is apparent that there are areas where such theories are applicable
overlooking AGV placement.
When discussing the enhancement of Automated Guided Vehicles (AGVs) through the
application of AI, GPS, and sensor technology, there does appear to be a weak relation between
17
the said technology and the general AGV frameworks within the literature [4].
An additional crucial gap concerns the utilization of contemporary models and frameworks in
relation to AGVs. Although there is a lot of material discussing innovations regarding
advancements of GPS systems, image processing and real time decision making there is little
exploration on how these technologies can be appropriately employed in different contexts of
industry [5].
An important gap that characterizes most of the reviewed study is in its data gathering methods.
Most of the literature alludes to navigation, decision making, and system performance, data
relative to these aspects, but fails to mention the probable feasibility of the data obtained vis-avis the research objectives and questions posed in the problem statements [6].
In conclusion, while the literature on AGVs does provide very valuable insights into the
development, applications and innovations of these vehicles, there are evident gaps in the
understanding of how the specific needs of industries are embedded in their design, whether the
theories applied are strong enough for the development of AGV technology, practical application
of the modern knowledge in its relevant aspects, and the sufficiency of the data obtained for the
research.
As an additional note AGVs also need to be considered for their environmental and social impact
[7].
Figure 8
AGV Satellite Navigation System [5]
18
Figure 9 Navigation Systems for AGVs [5]
19
DISCUSSION AND INTERPRETATION OF RESULTS
Autonomous Ground Vehicles, AGVs, are no longer just self-driving trolleys. While they still
handle material, the AGVs are changing the game in the industry. Their functions far exceed
basic collection: valuation data, easy integration with IOT and AI, and also advanced geospatial
functions. This is making an impact simply because these innovations are helping corporations
evolve and driving growth also as addressing industry mourn to a larger impact [10]. The above
points connect to the previous arguments given on how, when AGVs are implemented correctly
they are able to yield truly industry-specific solutions.
AGV technology has been well tested, and further autonomy and connectivity continue to be
developed. Will these emerging vehicles revolutionize the production process while it's
transforming how businesses engage with sustainability, collaboration, and operational
efficiency [11]? Yet, as emphasized in previous research, the underlying frameworks are to be
strong and not set in stone for the future.
Key Benefits of AGVs
Increased Efficiency: AGVs can operate around the clock without shifts or rest. They select their
path in order to take the shortest distance or least costly path, thereby attempting to minimize
operating cost and maximize overall efficiency [12].
Improved Safety: The use of AGVs will mean that the more hazardous tasks associated with
injury or fatality in the workplace can be done without physical harm. Safety in the work
environment is greatly improved[13].
Increased Productivity: While the AGVs deal with the round of materials, human workers may
devote their time to value-added activities such as process improvement and quality control [14].
It is this strand of shift that improves the workforce in general by doing away with rewardgaming, false scoring, and overall unhealthy competition.
Data-Driven Insights: AGVs are installed with sensors and tracking systems that build immense
volumes of data; such data can be used for the optimization of operational efficiency. Such
information can be used to predict maintenance requirements, workflow optimization, and
detecting bottlenecks in the production process [15]. This is in line with the approach that AI
and IOT interfaced in the AGVS for efficiently utilized productivity.
Inventory management, with the help of real-time tracking from AGVs, has overhauled
inventory management, where it is very closely monitoring the stock quantities, waste reduction,
and making the product available during time needs [16].
- Task versatility: Probably the most important advantage of using these types of vehicles is that
they can quickly adapt to changes in their operational environment. AGVs would easily be
20
reprogrammed to adapt to changing needs-either transitioning to a new line of products or
adapting to a changed production schedule [17].
EnvironmentalBenefits: A lot of the AGVs now use cleaner sources of energy such as electrici
ty, which reduces fossil fuel consumption and is a positive contribution toward
greener industrial processes [18].
Challenges to Consider
High Startup Costs: While there are many advantages to the use of AGVs, deployment requires a
tremendous upfront cost. This includes not only the vehicles themselves but also the
infrastructure needed to support them and employee training [10].
The Complexity of Integration: Since the integration of AGVs into operational workflows and
systems is neither easy nor smooth, developing new technologies in which businesses cautiously
have to develop-and for which an increasingly large number of instances have to adjust their
infrastructures-it will be [11].
Maintenance/ Downtime: AGVs require more than just planned maintenance; spare parts and
real-time monitoring are needed, too, for them to keep working. [13] This could lead to a much
higher total cost of ownership.
Workforce: The major ethical implications of the AGV systems are found in their typical usage,
to support or replace labor. Workers should be retrained so as to enable them to adjust to other
roles, in order to solve the problems of the companies.
Limitations in the application of AGVs might be due to the degree of technology available today.
Though these are effective in more structured contexts their use in agile, dynamic industrial
settings might be at a restriction [16].
21
Figure 10
AGV Operating Within an Industrial Setting [12]
Key areas where challenges are eminent include:.
Cybersecurity Threats: AGVs will be vulnerable to cyber attacks, because the system is equipped
with complex digitized systems. These motorcars will need to have sturdy cybersecurity to
prevent any unauthorized access and tampering [17]. All of this demands effective cybersecurity
designs-a theme repeated over and over in previous studies.
Interpretation of Results.
In principle, the cost-benefit equation for deploying AGVs is something that needs to be well
thought out by an organization. Their possible returns through increased efficiency, improved
safety, and higher productivity levels can offset the rather expensive upfront costs [12].
22
Best Practices for AGV Implementation:
The Model: Qualification of the autonomous vehicle to industry and functionality Different
fields have their own necessities, and chosen AGV technology should be designed accordingly
[10]. This returns to previous remarks about the need for industrially specific AGV designs.
- Return on investment: It needs to do a proper ROI analysis to justify the cost involved with the
AGV. So, the new after-school program will be reasonably evaluated [15] as some studies come
forth to help measure the financial benefits and make sure the investment will not go down the
drain in the future.
- Retraining and Adaptation of the Workforce: Since the work will be assumed more and more
by the AGVs, the workforce must be retrained and adapted. Thus, the possible impacts on the
workers will be minimized since the facilities and expertise needed to switch to other positions
would have been availed to them [14].
Compliances and Regulations: The applications of the AGVs have to meet many industrial
regulations and standards w.r.t. how they would work out in a safe manner besides working out
efficiently [11].
The technologies-especially innovations in AI and in Machine Learning subjects-are accelerating
at a fast pace while their potential will be exponential certainly in near future-and, therefore, the
business leading edge innovations are expected to become tough to cope-up [18].
- Risk Management: In anticipation of engaging in any transaction, an organization should put in
place a single supervisory system of risk management which protects all the foreseeable cyber
threats and operational difficulties long before the collapse in the strategic period [17].
23
Figure 11 AGV designed for Industrial Applications [11]
24
CONCLUSION
Thus, the emergence of autonomous ground vehicles represents, at last, not a sea
change but a paradigmatic transition in industrial operations to outgrow their basic
conception as material handlers. This is because AI-fitted AGVs are sure to drive
productivity and efficiency on one hand, and data-driven insights on the other. With
these constantly being adapted and implemented across industries, the potential impact
of AGVs on Production Process transformation for further nudging sustainability
becomes more visible. Further development of solid frameworks will be required so that
the full benefits emanating from AGVs flow and innovative solutions are reached,
catering to specific industrial needs. Ultimately, AGVs represent one of the major steps
toward operational excellence and strategic development in the contemporary business
arena. Besides, Autonomous Ground vehicles are not only changing material handling
but also changing the face of industrial operations altogether.
Advanced capabilities of data valuation, seamless integrations, lots, and AI, together
with improved geospatial functions, are forcing significant growth and evolution in
corporations. The more and continuous development in the field of AGVs proves day by
day to be a game-changing force in production processes, sustainability, and efficiency
of operations. In summary, the advantages of AGVs include increased Efficiency,
improved safety, better productivity, and data-driven insights-all pointing to the vital role
of AGVs within modern industries. Yet, it has to be borne in mind that the structures on
which these technologies function need to be agile enough to meet future demands.
25
REFERENCES
[1] Solving Group, “The history of automated guided vehicles” 2024, The history of automated
guided vehicles - Solving
[2] Ümit Özgüner, Tankut Acarman, Keith Alan Redmill, “Autonomous Ground Vehicles”,
Boston, Massachusetts: Artech House
[3] Mohanjeetsingh Bansal, “Image Processing in Autonomous Vehicles:
Seeing the Road Ahead” Nov 27,2023,https://medium.com/@mohanjeetbansal777/imageprocessing-in-autonomous-vehicles-seeing-the-road-ahead-b400d176f877
[4] W. Yijing, L. Zhengxuan, Z. Zhiqiang and L. Zheng, "Local Path Planning of
Autonomous Vehicles Based on A* Algorithm with Equal-Step Sampling," 2018 37th
Chinese Control Conference (CCC), Wuhan, China, 2018, pp. 7828-7833, doi:
10.23919/ChiCC.2018.8482915.
[5]D.Bechtsis,V.Moysiadi,N.Tsolakis,D.Vlachos,“Scheduling and Control of Unmanned Ground
Vehicles for Precision Farming: A Real-time Navigation Too,” in the 8 th Int. Conf on
Information and Communication Technologies in Agriculture, Food and Environment,
September 2017, pp. 180-187
[6] T.Zhang, Q.Li, C.Zhang, H.liang, P.Li, T.wang, S.Li, Y.Zhu, amd C.Wu, ‘Current trends in
the development of intelligent unmanned autonomous system,” Frontiers of Information
Technology & Electronic Engineering, vol.18, pp. 68-85, Feb 2017.
[7] D.Bechtsis, N.Tsolakis, D.Vlachos, J.Srai, “Intelligent Autonomous Vehicles in digital
supply chains: A framework for integrating innovations towards sustainable value networks,”
Journal of Cleaner Production, Vol. 181, pp. 60-71, Feb 2018.
[8] N. Tsolakis et al., "Intelligent Autonomous Vehicles for Fostering the Establishment of
Bespoke Supply Chains: A Simulation Tool Perspective", J. Supply Chain Manage. 57, 3,
2021.
[9] T. Zhang, et al “The Development of Unmanned Vehicles in China: Current Status and
Future Directions” J. Robotics Autonomous Syst. 128, 2020.
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[13] P. Anderson, R. Brown and S. Lee, Safety Enhancements in Industrial Automation,
TechReview Publications, 2020.
[14] X. Chen and W. Li, Ethical and Workforce Considerations in AGV Implementation,
Springer Press, 2021.
[15] M. Davis, Data-Driven Decisions in Modern AGVs, Journal of Smart Manufacturing, 2022.
[16] J. Evans, Real-Time Inventory Management Using AGVs, Logistics Today, 2023.
[17] M. Taylor, Cybersecurity for Autonomous Systems, CyberSafety Quarterly, 2020.
[18] S. Lee, Green Technology and Sustainable Automation, EcoTech Journal, 2023
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APPENDIXES
Table 1 Key Performance Parameter of AGV
Glossary of Terms
1. AGV: Autonomous Ground Vehicle, an industrial self-driving vehicle [2].
2. AI: Artificial Intelligence, the field of computer science which deals with intelligent machine
creation [6].
3. GPS: Global Positioning System, a network of satellites utilized in navigation [2].
4. IOT: Internet of Things, the network of physical devices, vehicles, and other items embedded
with sensors, software, and connectivity [6].
5. Machine Learning: Subset of AI, involved with the training of algorithms to learn from data
[3].
List of Acronyms
1. AGV: Autonomous Ground Vehicle [2].
28
2. AI: Artificial Intelligence [6].
3. GPS: Global Positioning System [2].
4. IOT: Internet of Things [6].
5. ML: Machine Learning [3].
29