A Multidisciplinary Learning Model Using AGV and AMR for Industry 4.0/5.0 Laboratory Courses: A Study

Abstract

This paper presents the development of a multidisciplinary learning model using automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) for laboratory courses, focusing on Industry 4.0 and 5.0 paradigms. Industry 4.0 and 5.0 emphasize advanced industrial automation and human–robot collaboration, which requires innovative educational strategies. Motivated by the need to align educational practices with these industry trends, the goal of this research is to design and implement an effective educational model integrating AGV and AMR. The methodology section details the complex development process, including technology selection, curriculum design, and laboratory exercise design. Data collection and analysis were conducted to assess the effectiveness of the model. The design phase outlines the structure of the educational model, integrating AGV and AMR into the laboratory modules and enriching them with industry collaboration and practical case studies. The results of a pilot implementation are presented, showing the impact of the model on students’ learning outcomes compared to traditional strategies. The evaluation reveals significant improvements in student engagement and understanding of industrial automation. The implications of these findings are discussed, challenges and potential improvements identified, and alignment with current educational trends discussed.

1. Introduction

The concept of Industry 4.0 and its subsequent evolution, often referred to as Industry 5.0, represent revolutionary changes in the way industrial production is carried out. These changes are driven by the widespread use of advanced technologies such as autonomous guided vehicles (AGVs) and autonomous mobile robots (AMRs), key industrial automation elements. Industry 4.0 is all about digitization and systems integration, where the Internet of Things (IoT), cyber-physical systems (CPS), big data, and artificial intelligence (AI) are enabling the creation of smart factories [1]. These factories are capable of autonomous decision-making, real-time optimization of production processes, and adaptation to changing market conditions and demands. AGVs and AMRs are key components of these smart factories as they enable flexible and efficient logistics within manufacturing plants [2]. These vehicles can autonomously navigate and transport materials and products between different sites, minimizing human intervention, reducing errors, and increasing productivity. AGVs are equipped with advanced sensors and navigation systems to avoid obstacles, optimize routes, and safely interact with people and other machines. Industry 5.0, which is often seen as an evolution of Industry 4.0, emphasizes collaboration between people and machines, focusing on the use of technological innovation to improve working conditions and personalize production. In this context, AMRs are an important element because they enable complex tasks that require high precision, flexibility, and power [3]. Robotic arms can be used for assembly, welding, packaging, quality control and many other applications where human skill and intelligence are combined with robotic precision and performance. AMRs are designed to be easily programmable and adaptable to a variety of tasks, allowing production lines to be quickly reconfigured to meet current needs. Thanks to advanced control systems and machine learning algorithms, these robots can perform tasks with a high degree of autonomy and adaptability [4]. Combining AGV and AMR in industrial processes brings significant efficiency, flexibility, and quality benefits. The integration of these technologies enables the creation of interconnected and intelligent manufacturing systems that can respond to dynamic market changes, optimize resource utilization and minimize downtime. In this way, companies can achieve a higher level of competitiveness and sustainability [5]. Industry 5.0 also emphasizes the humanization of the working environment, where technology supports and complements human skills, leading to improved working conditions and overall employee satisfaction [6]. In the context of these changes, companies must invest in the education and training of their workforce so that they can use new technologies effectively and adapt to the changing work environment. The implementation of AGVs and AMR requires technical training, strategic planning, and reorganization of production processes. Companies need to consider factors such as security, interoperability of systems, cybersecurity, and data protection. Only with such a comprehensive approach can they fully exploit the potential of Industry 4.0 and 5.0 and achieve sustainable innovation and growth [7].

With Industry 4.0 and Logistics 4.0 coming to a reality, more and more equipment has been operating automatically for years. This is true also in the V4 Region, such as in Slovakia and Hungary, for larger multinational companies, where it is worth investing in such equipment due to mass production. Thus, there is a greater demand for robotization and automation in this region. This is true for transport between warehouses and production areas, which is solved by driverless transport vehicles, but automating them is still a challenge today [8]. The difference between the two is essentially in navigation and, due to the difference in maturity, also in the current applicability [9]. This article presents a new multidisciplinary learning model using AGVs or AMRs within laboratory courses. One smaller vehicle would serve as mapping and route forecasting for the larger, actual cargo vehicle [10]. Nowadays, if the industry uses some technologies, soon it should also be implemented in higher education; otherwise, that educational model can be regarded as old-school, and as a result, the students will not choose that course [11].

Using this tendency, this paper introduces a new learning model, which involves mobile robotic systems, i.e., the AGVs and AMRs. Basically, a learning model contains the mobile robots as a part of a logistics system and an option for automating transport. On the other hand, mobile robots in education are used only for understanding the programming, and absolutely not focusing on the use of a mobile robot in an extended, i.e., logistic system. The new learning model uses both sides, which is important for logistics engineering students.

Therefore, Section 2 of this paper briefly summarizes the solutions found in the literature. Section 3 details the uses of the laboratory, AGV, and AMR. Moreover, the section also introduces the new learning model along with one basic and further scenarios. Regarding AGV, there are several navigation options for such machines, which are also listed in Section 3. There is a prototype AGV in the Logistics 4.0 Laboratory of the Institute of Logistics of the University of Miskolc, as shown in the same section. There are also two AMRs within the laboratory; these are introduced in this section. The connection formerly did not work; therefore, the establishment was necessary, as described in the same section. The next, Section 4 summarizes the results, and the final section discusses the newly gained results.

2. Literature Review

The use of AGV routes in forecasting logistics processes has been explored in several studies. Ruiping [12] demonstrated the effectiveness of AGV systems in improving sorting operations in the express industry with increased efficiency and stability. Xiaoming [13] proposed a method for local obstacle avoidance planning in AGV-based logistics systems, enhancing path planning and system safety. Rocha et al. [14] highlighted the potential of AGV systems in increasing production process flexibility and productivity, particularly in industrial coating applications. Gawrilow et al. [15] developed a dynamic routing algorithm for AGVs, focusing on conflict-free routing and considering physical properties and safety aspects. These studies collectively underscore the significant role of AGV routes in enhancing various aspects of logistics processes.

The concept of Logistics 4.0, which integrates technological advancements like the Internet of Things, autonomous vehicles, and artificial intelligence, is a key driver of change in the logistics industry [16]. This shift towards smart infrastructure and the use of big data analytics in logistics processes is a significant trend [17,18]. The implementation of Industry 4.0, of which Logistics 4.0 is a part, is expected to generate innovative solutions and long-term effects on the industry [19]. Using AR (augmented reality) is becoming a crucial question not only in industry but in education [20,21].

Automated guided vehicles are a key component in modern material handling systems, with a variety of types and applications. Vishwakarma [22] provides an overview of the different types of AGVs, including those that follow markers or wires in the floor, use vision, magnets, or lasers for navigation, and can tow objects behind them. Leung et al. [23] discuss the assignment of AGVs with different vehicle types, highlighting the need for efficient allocation of these vehicles. Kamiappan et al. [24] focus on the mechanical design and analysis of AGVs, emphasizing their role in cost reduction in the automobile industry. Butdee and Suebsomran [25] explore the use of image processing for AGV control, particularly in preventing the vehicle from losing its way. These studies collectively underscore the diverse types and applications of AGVs, as well as the importance of efficient design and control mechanisms. A range of studies have explored the development and application of autonomous mobile robots. Hsu and Chao [26] introduced an AMR system for microcontroller education, enhancing students’ project planning and implementation skills. Laurette et al. [27] discussed the supervision and control of AMR intervention robots, focusing on autonomous task management and obstacle avoidance. Yu and Malik [28] developed an AMR with an infrared detector system for navigation in unknown environments, demonstrating its effectiveness in avoiding collisions. Koseoglu et al. [29] presented the design of an AMR based on the robot operating system (ROS), highlighting the successful integration of hardware, electronic communication protocols, and software. These studies collectively contribute to the advancement of AMR technology and its potential applications.

A range of studies have explored the navigation of autonomous mobile robots in various environments. Raja et al. [30] developed a hybrid obstacle avoidance method, combining classical and Fuzzy Logic approaches, which was found to be effective in navigating a robot through obstacles. Hu et al. [31] focused on trajectory models and a smooth guidance algorithm for AMR navigation, particularly in dynamically changing environments. Datta et al. [32] designed an AMR with a manipulator for manufacturing tasks, using a combination of proprioceptive and exteroceptive sensors for navigation. Park et al. [33] proposed a unique approach using passive RFID for AMR navigation, which was found to be reliable and robust in human living environments. These studies collectively highlight the importance of effective navigation methods for AMRs, particularly in unstructured and dynamic environments.

Forecasting plays a critical role in logistics, particularly in inventory management and demand forecasting [34]. It is also essential for enhancing operational performance and sustainability in the face of external conditions [35]. The quality of forecasts significantly influences logistics performance, with user perceptions and application being key factors [36]. Furthermore, the quality of forecasts is a crucial factor in coordinating logistics processes, particularly in modern logistics operators [37].

The Festo Robotino system has been used in various applications, including as a base for walk robot design and simulation [38]. It has also been featured in the Festiwal Robotów 2015, where it likely showcased its capabilities [39]. Furthermore, the Senhance robotic system, which may be related to the Festo Robotino system, has been used to demonstrate its potential in the medical field [40,41].

Recent research has explored various approaches to learning in mobile robots for improved performance in dynamic environments. Some studies have focused on integrating model learning with trajectory planning to enhance safety and efficiency in navigation [42]. Others have investigated autonomous learning of visual object models, enabling robots to adapt to changing environments [43]. Learning techniques have also been applied to control systems, allowing robots to adjust to varying terrains and loads [44]. Additionally, model-based learning approaches have been developed to improve numerical flow models using empirical data collected by mobile robots, incorporating Gaussian processes and Bayesian inference [45]. These advancements in learning algorithms for mobile robots have significant implications for applications in medicine, the defense, industry, and domestic settings, potentially leading to more autonomous and adaptable robotic systems [44]. As can be concluded, neither learning model focuses on a deep, two-part teaching of a mobile robotic system.

3. Methodology

The design of the methodology is based on three factors:

• Former experience during the education of different robot systems
• Steadily changing requirements from the industrial world
• Getting feedback from graduated students

It is important to mention that targeted design methods should be reflected to provide students interested in a certain aspect with a more comprehensive understanding and to calculate the actual teaching effectiveness. Therefore, in this section, not only one basic but two more scenarios are introduced, taking into account the different possibilities of teaching the learning material.

Entering the methodology of developing a learning model is a key step for creating an effective and adaptive education system. This process involves a systematic approach that begins with a thorough analysis of the educational goals and needs of the target group. Based on this analysis, appropriate technologies are selected to support an interactive and engaging learning environment [46]. In parallel with the selection of technology, it is essential to define an evaluation methodology that allows the effectiveness of the learning process to be monitored and measured. This methodology includes both quantitative and qualitative tools such as tests, surveys, and analytical tools to track learners’ progress and feedback from participants. The aim is to ensure that the development of the learning model not only meets academic and pedagogical standards but also effectively supports student learning and development in a dynamic and technology-rich environment [47]. The topic of AGV and AMR is involved in two subjects in two different semesters of the Logistics Engineering MSc at the University of Miskolc:

• Intelligent Material Handling Machines and Systems: Semester 1
• Mechatronics in Logistics: Semester 2

Since the Logistics Engineering MSc held in English only started in September 2023, only one occasion exists for gathering experience. Therefore this is a new principle and a concept that is yet to be applied to the learning process. That is why the results are not yet available to the students. Using the experiences, the process of developing the learning model can be described as follows:

• Preparing for the subject in Semester 1
• Gathering experience from the subject in Semester 1
• Preparing for the subject in Semester 2
• Gathering experience from the subject in Semester 2
• Improving feedback routes
• Creating a learning model for the new semester 2024/25

Mention must be made of the fact that the students come from other parts of the world, like Mongolia and Brazil and, therefore, they have a widespread background in logistics and automation. This background is affected by the following factors:

• BSc degree: Logistics Engineering or other specialization
• Work experience, if it exists
• Language skills, especially the professional vocabulary

Since the practicals are in the laboratory, the technology should also be improved. Regarding AGV and AMR, creating a programming environment and communication way are crucial parts of developing the present learning model. From the next semester, the evaluation will be improved, not only from the midterm tests but also from the start survey, finish survey, and continuous feedback on how the students understand the learning materials.

The curriculum development was performed in two ways:

• Intelligent material handling machines and systems: The curriculum of the Hungarian version of this subject was available; however, the Hungarian MSc students come from the same university, from Logistics Engineering BSc; therefore, a lot of information can be skipped. As mentioned above, international students come from different backgrounds; therefore, a major modification was made to the curriculum.
• Mechatronics in logistics—Semester 2: This is a completely new subject, with only a background from the first author’s mechatronic engineering BSc and MSc degrees. Therefore, the laboratory exercises were completely rethought and created.

Regarding laboratory exercises, they were affected in two ways:

• Intelligent material handling machines and systems: The laboratory exercises focus on machines, especially the available ones, such as AGV, AMR, different types of conveyors, industrial robots, and automated loading machines.
• Mechatronics in logistics—Semester 2: The laboratory has many elements to show, like different types of sensors, actuators, pneumatic systems, PLC, and HMI.

The data were collected from the continuous interaction with students and the different types of tests, like midterm tests and laboratory tests, such as programming. The analysis was conducted focusing on the laboratory courses since these are the crucial and most interesting parts of the learning process [48].

3.1. Thematic of Proposed Subjects within the Learning Model

Firstly, the theme of the subject “Intelligent Material Handling Machines and Systems” from 2023/24 is described here, which contains not only AGV and AMR but also other similar automated machines. There are two parallel parts of the subject: The theoretical part and the practical part. Here, the topics are listed below from the practical part since it contains the topic of AGV and AMR held in the Laboratory of Logistics 4.0:

• Introduction to intelligent material handling machines
• Robotization
• Theory of programming Mitsubishi robot
• Laboratory show, practice of programming of Mitsubishi robot
• Introduction of mobile robots—AGV
• Test 1
• AGV vs. AMR
• Automated cranes, automated conveyors
• Sensors
• Drones
• Test 2

Secondly, the theme of the subject “Mechatronics in Logistics” from 2023/24 is listed below. Although it seems that it does not contain AGV and AMR, several parts of it are taught in the Laboratory of Logistics 4.0, like sensors, motors, and PLC programming:

• Presentation of the concept of mechatronics: History of mechatronics
• Detailing the disciplines of mechatronics
• Overview of sensors: Examining sensors in the laboratory
• Overview of actuators
• Overview of electric motors: Examining actuators in the laboratory
• Overview of pneumatic systems: Assembly of a simple pneumatic circuit in the laboratory
• Overview of hydraulic systems: Overview of mobile machines’ hydraulic systems
• Presentation of the base of PLC systems: Overview of PLC programming languages
• Describing a simple automated process 1.: Creating a PLC program for a simple automated process 1.
• PLC programming in the laboratory—first program; PLC programming in the laboratory—simple automated process 1.
• HMI programming in the laboratory: Practical test—creating PLC program for simple automated process 2.
• Example for using mechatronics in logistics 1.: Example for using mechatronics in logistics 2.
• Design and characteristics of robotic workplaces 1: Design and characteristics of robotic workplaces 2.
• Test

3.2. AGV Generally

Before introducing the AGV from the laboratory during education, the AGVs are introduced generally. The learning model contains the typical structures of AGV listed as follows [49]:

• Forklift vehicle or truck (see Figure 1a)
• Transport vehicle or truck (see Figure 1b)
• Tow vehicle or truck (see Figure 1c)

When we speak about AGVs during education, not only the types but also functions for automated modes must be highlighted. For moving, navigation is a crucial part of the AGV; therefore, it should be a key part of the learning model. As engineering students, they should understand the operational background of machines, too.

The navigation capabilities of an AGV are mostly linked to some external infrastructure with the physical track, such as optical, magnetic, inductive, or. in a newer way, a virtual track, such as LIDAR technology. Examples of using the physical path, like line following or tags or using a virtual path, like laser triangulation or vision guidance, can be seen in Figure 2 and Figure 3, respectively. Navigation options for AMR now offer more options, such as LIDAR (2D or 3D), camera (also 2D or 3D), sonar, radar, etc. The most used method is the SLAM (simultaneous localization and mapping). A summary diagram of possible navigation is shown in Figure 4 [49].

3.3. AGV in the Logistics 4.0 Laboratory of the University of Miskolc

A prototype AGV can be found in the Logistics 4.0 laboratory of the Institute of Logistics of the University of Miskolc, which is the base for education and research and can be seen in Figure 5 from [51]. It belongs to a group of transport vehicles from groups among the AGV types.

Some of its main features are listed as follows, which are also involved in the education:

• During lessons, the difference in using the virtual track from the physical track is explained
• During lessons, the functions of the navigation by LIDAR sensor are explained
• On the top of AGV, a six-degree-of-freedom industrial robot was mounted; it is part of the robotization part of the curriculum
• Conveyor belts serve as physical connectivity to the conveyor system
• Drive: 30V DC drive motors, gear ratio 1:25 (see Figure 6), it is part of the other subjects, such as the actuator
• Spherical wheel
• Safety sensors are part of the other subjects of sensors
• PLC & PC are part of the other subjects on PLC (see Figure 7)

As mentioned in the characterization of this AGV, the navigation uses a LIDAR sensor. This sensor can sense the environment in a 2D horizontal layer. The detected environment can be checked in software; this result can be seen in Figure 8, where only the special reflective mirrors were detected and in Figure 9, where all detected objects can be seen in the software. This methodology is used in education to understand the virtual track.

3.4. AMR in the Logistics 4.0 Laboratory of the University of Miskolc

Within the same laboratory mentioned above, there are also two AMRs, a 2nd generation of Festo Robotino. Although these devices are more than 10 years old, they are still good for educational purposes and for understanding what an AMR is. These AMRs use no external infrastructure, only the onboard sensors, which are ultrasonic sensors around the circular frame and a 2D camera. This is the main difference compared to an AGV, and therefore, a separate lesson is dedicated to showing these features. One of the AMRs also has an electric gripper. These AMRs together can be seen in Figure 10.

Mention must be made of the fact that, formerly, the two AMRs were not connected to any network, since they were not used for years; therefore, it was necessary to find a solution to their task. However, this solution is also implemented into the learning model, since, in this way, the students can understand how such an automated machine should be implemented into an existing logistics system.

A Festo Robotino basically can be controlled and connected in two ways:

• The Robotino works as an AP (Access Point); in this case, other devices can connect to Robotino’s internal AP.
• The Robotino works as a client; in this case, the Robotino should connect to an external AP like a Wi-Fi router

Since their ages are not fully the same, the Robotinos in the laboratory have two different network devices (see Figure 11), but the connection is similar.

The first case is more beneficial if only one device connects with the Robotino. However, within the laboratory, the goal is to connect the AMRs to the AGV and to the main conveyor control system; therefore, the second option is the proper solution. Connecting devices to the network is especially crucial in industry, fulfilling the requirements of IoT (Internet-of-Things). Other advantages are that both Robotinos can be controlled directly from a desktop computer, notebook, smartphone, or tablet. Two examples of successfully established connections can be seen in Figure 12.

A new program was created with Robotino self-programming software to control the Robotino. In this program, the Robotino can be controlled either by a joystick connected to the PC, or by a control panel, which consists of the direction symbols shown on the screen. The program and control panel created in Robotino View software v.4 can be seen in Figure 13. This programming way is a good example for the students, since it can be shown that programming contains not only fully textual writing but also a graphical way can be used.

In this graphical program, the following functions were defined:

• Live camera
• Opening or closing gripper
• Function block the Control panel—onboard control possibility
• Function block Joystick—the value of rotation of the joystick hand is divided by five for more accurate control
• Function block Omnidrive calculates the necessary RPM for the three motors

3.5. Communication between AGV and AMR

As mentioned formerly, in an automated logistic system, several different types work together. In this case, for educational purposes, AGV and AMR are used to show how two different types or systems can work together.

To understand the discrepancy between AGV and AMR, the best way is when both devices communicate with each other. However, before establishing any communication between two devices, the existing and available network system should be understood. In the laboratory for this purpose, only a wireless network, i.e., a Wi-Fi connection, is the proper solution. The two devices do not communicate directly with each other but via a PC; this computer connects through a wired Ethernet. The established information system for this communication is shown in Figure 14.

After understanding the information system, the real system should be understood. Formerly, the AGV and AMR were introduced to the students; however, they should be put near each other. Since the AGV and AMR can be controlled and programmed from a PC, this place should also be considered, as illustrated in Figure 15.

For the communication with AGV, the PLC inside the machines is used. The PC can reach it via the OPC system, as shown in Figure 16. The data from PLC can be read by a Python environment running on a PC.

For the communication with AMR, a graphical program was created. as shown in Figure 17. The data from AMR can be read using the same Python 3.12.4 environment used for communication with AGV.

As can be seen above, three different programming methods can be used. This shows students that programming can be done in several ways.

3.6. The Improved Learning Model Using AGV and AMR

With the background written over, the full learning model over two semesters can be proposed, as in Figure 18. This is the basic scenario for using the learning model.

As can be seen in Figure 18, the transferring knowledge about AGV and AMR is distributed into two basic parts, as in the curriculum of Logistics Engineering MSc in Miskolc. The first part focuses on understanding deeply the definitions of AGV and AMR and showing two examples of them. The second part focuses on understanding which elements can be found in an AGV and AMR, and how to use and program it. Another crucial part of the second main part is connecting AGV and AMR, i.e., two different types of automated material handling machines, as usual in nowadays industrial systems.

The learning model focuses not only on the one-directional transfer of knowledge but also on getting and giving feedback from and to the students, respectively. Another goal of the learning model is also to ensure a feeling of success.

3.7. Further Possible Scenarios Using AGV and AMR

Section 3.6 deals with the ideal case of the new learning model; however, sometimes it occurs that modifications or only one part can be used during education. These can be as follows:

• Students come from abroad only for one semester, typically in the Erasmus program, and they can choose different subjects. In this case, they have only one semester to learn the robotic system, and they usually have different academic backgrounds, for example, Process Engineering or Mechanical Engineering.
• The subjects used in Logistics Engineering MSc and written in Section 3.6 are mandatory. However, in other MSc courses, like Information Sciences MSc or Mechatronics Engineering MSc, they can be indicated as optional subjects. In this case, since the students have deeper knowledge, the structure can be modified. For instance, the Information Science MSc students have higher programming knowledge, while Mechatronics Engineering MSc students have a deeper background in sensors and motors.

Based on the cases, two more scenarios can be indicated depending on the interest of students. Here, the two cases can be distinguished as follows:

(a)

Theoretical orientation: If the students are not interested in programming and details of AGV and AMR, only getting acquainted with the types of mobile robots can be a good choice.

(b)

Practical orientation: If the students have some knowledge about mobile robots and are more interested in programming, this second choice is a good option.

3.8. Personnel Configuration, Level of Difficulty and Difficulties during Teaching

Since the robotic system is very complex, a logistics engineering background alone is not enough for teaching. A better choice is mechatronics engineering, or another engineering with a subsidiary background, like mechanical engineering or electrical engineering.

Also, thanks to the complexity of the robotic system, this subject is neither the easiest nor the most difficult subject. Easier subjects can be based on logistics systems or simulation systems. More difficult subjects can be different fields of mathematics.

• Difficulties during teaching can be listed as follows:

• The programming background of students is not strong; therefore, teaching programming is not only describing a programming language but also contains the logical part. It can be stated that without understanding the logic of programming, the students cannot program.
• Sensors and motors are electronic elements, and the logistics engineering students also do not have a strong background. Therefore, only the basics can be taught since there is no time for teaching and understanding deeper electronics knowledge.

However, these teaching difficulties can be reduced in the case of a more relevant engineering background.

Mention must be made of the degree of students’ interest in these robots before and after learning, their degree of mastery after learning and their willingness. Based on experience, it can be concluded that before such a subject, the students had no clear definition of robots, especially mobile robots. After fulfilling the subjects, although they will probably not program such a robot, they will know some factors about it, and they can use them in the planning of logistics systems. Therefore their willingness increases if they see not only outdated techniques but also new techniques towards Industry 4.0 and 5.0.

4. Results

In the new learning model, the number of two-way feedbacks and a higher ratio of laboratory courses can be highlighted. These have the advantages of increasing the motivation for students to learn the material and, besides, think as engineers. The data is available from the present 2023/24 students; using this experience, the novelty approach for the learning model was created.

The subject of Intelligent Material Handling Machines and Systems originated in the Hungarian language but focused on the theoretical part, not the laboratory. In this strategy, the presentation form was used, and the laboratory was used only for showing some machines without operating them. The new learning model includes less theoretical parts but more laboratory parts. It also contains the operating machines, like the manual and automated movement of AGV, AMR, and other machines.

Since Mechatronics in Logistics is a completely new subject, it has no true origin. A comparison can be made only with the previous experience from many years back in other laboratories as a studentship of the first author. However, Mechatronic engineering students need significantly different learning materials than Logistics engineering students. A mechatronic engineer needs to think more of a machine way, so they focus more on the elements of an automated system. On the contrary, the logistics engineer needs to think more about the process, so they only use the machines in a process and not plan it. Therefore, for them, an element of automated machines has less significance, so they do not need complete subjects for each element, like sensors. actuators, and PLCs and their programming. They should know only a basic background for gathering more information for a decent decision; therefore, this learning model is a good solution for this situation instead of showing these elements only in a presentation form.

Nowadays, the motivation of students has significantly changed, as compared to 5 years ago. Thanks to social media and other similar IT elements, the speed of gathering information has roughly increased. Therefore, especially for Z-generation students, i.e., born after 2000, the listening ratio in a traditional classroom presentation form was decreased. Coincidentally, with the listening ratio, the success ratio also decreased, since the students focus more on other things.

This learning model improves the listening ratio by introducing more surveys and more laboratory exercises. The end-of-class surveys were used in other former subjects, and the results can be measured not in these surveys but in the midterm test since the gained points became higher. Other advantages can be mentioned from the laboratory courses, since if the students can see a machine in real life, not only on photos in presentation, they also understand and imagine these types of machines.

The previous theme was used for two international students and five Hungarian students. The evaluation for them was only one test during the semester due to the standard presentation-type lectures.

The overall results for comparing old, standard and new, practical-focus learning models are summarized in

Table 1. Comparing the old learning model and the new learning model.

	Old Learning Model	New Learning Model
Number of tests	2	4
Type of tests	Written end-of-course test for student evaluation	Written end-of-course test for student evaluation + Practical-activity evaluation test
Number of surveys	0	11, either questionnaire or choosable option type
Number of laboratory activities	1	7
Programming part	0	2
Changing feedback	No	Continuous, but especially at the end of semesters
Advantages	No laboratory is necessary Only delivering presentations needs less effort	Much higher satisfaction, listening ratio Enjoyment Higher ratio for transferring knowledge
Disadvantages	Much lower satisfaction, listening ratio Boring, especially for Z-generation Lower ratio for transferring knowledge	Laboratory with automated machines is necessary—the cost can be high Professional experience needed to show elements

. For comparison, the data is available in Figure 18, Figure 19, Figure 20, Figure 21 and Figure 22. The numbers are given together for two semesters for the basic scenario.

The written test is an evaluation test held at the end of the course. Here the students get more questions, which they have to answer in sentences or drawings. The practical test is an activity evaluation test. The students receive a task, and they have to solve it in a given time.

As can be seen, formerly, no surveys were used; however, they are necessary for getting continuous feedback from the students, examining their interests, and developing the actual curriculum for the next occasion.

The number of laboratory activities is also increasing since they are the key to understanding such a robotic system. The programming part is also introduced; although the logistics engineering student probably will not be a programmer, nowadays, in an automated system, it is important to see how a robot can be programmed.

Mention must be made of the results collection since it is also a very important part of the capability of continuously changing the material based on the interest of students and also developing the curriculum. Formerly, only the written test was available. Basically, this type of test is good for evaluating the knowledge of students; however, it does not give any information or feedback from the students on what they would have liked to learn deeper or vice versa. That is why the new learning model, independent of the scenarios, contains a much higher number and types of tests. The practical test is good feedback if the students understand the programming or not. Furthermore, the surveys are performed using free-of-charge methods. Here, two further methods can be used: a questionnaire or an end-of-class evaluation, a choosable type survey, where the results can be presented instantly.

5. Discussion

The examined topic, i.e., AGV and AMR, nowadays is constantly increasing in area, especially in Western Europe. Although the number of such automated machines is much lower in Middle Europe like in Slovakia or Hungary, the tendency to increase is similar. For a logistics engineer, it is very important to know not only the base of process planning, including machines but also new techniques. Therefore, teaching AGV and AMR is essential in a state-of-the-art learning model. The novelty factor can be increased if a laboratory is available, and the students can examine and try these machines. From the present year, showing the laboratory in 1 h has already had higher satisfaction, but using the new learning model, its factor can be significantly increased.

Preparing this new learning model requires a professional background and extensive teaching experience. Being aware that standard learning methods are becoming increasingly outdated is also crucial. Finally, the present form of subjects and the feeling that students use their smartphones instead of listening are also leading factors in developing the new learning model.

From the viewpoint of students, there are a lot of beneficiaries, like practical-focus learning, which can be used directly in industrial life; working with real devices, which affects not only the learning ratio but also feeling successful; and real looking for battery, Wi-Fi, sensor, motor systems.

From the viewpoint of the teacher, although more efforts are necessary, this new model also has some benefits, like feeling the knowledge transfer is more successful, thanks to the satisfaction of students, getting closer contact with students, thanks to continuous listening and feedback, and finally, but not least, self-development about using such machines, since every situation is different.

The current trends follow, in many cases, and are still in standard presentation form. It must be stated that in some cases, the traditional methods are still the most efficient, for example, fundamental physics. However, in logistics, the continuously changing requirements and availability, the curricula should also be continuously changed. The potential challenge with this learning model is to keep ourselves up-to-date with the necessary professional background and knowledge to handle such a laboratory. Mention must be made of the limitations, too: thanks to rapidly changing technologies, the improvement of a laboratory needs money, and this cannot be available in every case.

Finally, opportunities and recommendations for improvement should be discussed. As mentioned above, the technologies and requirements are always changing; it is enough to think about the COVID period. Therefore, the educational materials and curriculum need to be revised, if possible, every year. It is also recommended that the possibility of increasing the feedback possibilities for students be examined. In my experience, a motivated student likes to express their opinion about the present topic. For engineering teachers it is recommended they discover industrial and other academic partners to see the opportunities in the world outside of university.

6. Conclusions

The paper covered the introduction of a new learning model, which is capable of teaching AGV and AMR with the help of laboratory tools. This learning model does not only contain improving the number of laboratory activities but also the quality of knowledge transferring has increased. To improve the listening ratio and success, new tests, surveys, and feedback elements were added to this new model. Mention must be made of the fact that although mentioning AGV and AMR can occur somewhere, using and showing personally real devices is entirely novel in an educational system.

It can be concluded that thanks Industry 4.0/5.0 technologies, nowadays it is essential to improve the education part, too, like materials and curricula. This new learning model focused on Industry 4.0/5.0 and one of the most crucial elements, t automated machines.

Regarding plans, it can be stated that the educational materials and curricula should be improved continuously. However, improving practical activities cannot be achieved without improving the laboratory. Presently, the AGV and AMR can communicate with each other, and there is an initial solution for communicating with the main controlling PLC, but there are no real solutions for it. All-directional communication will be necessary to show how such a system can be synergically integrated.

It can be stated that the introduced new learning model can be generalized for other types of automated machines, like cranes or drones. Therefore, another future goal is to implement a drone into this practical teaching since these novelties are now only theoretically mentioned. For the next semester, hopefully, a drone can be integrated into the laboratory system, and hence also into education. Although the programming part, sensors, and motors of a drone can be different compared to AGV/AMR, the learning model can be developed with the same structure.