1. Introduction
On 7 January 2021, the European Commission officially published a document [
1] entitled “Industry 5.0: Towards a Sustainable, Human-Centered and Resilient European Industry” [
2]. The European Commission officially called for the adoption of the concept of the fifth industrial revolution in the history of the European economy.
The difference between Industry 4.0 and Industry 5.0 is that the fourth industrial revolution is focused on the digitalization of processes and the use of artificial intelligence to increase productivity, achieved through the intelligent networking of machines and processes for industry based on Cyber-Physical Systems (CPS) (a technology that relies on intelligent control through embedded network systems) [
3], while Industry 5.0 is concentrated on the human factor at the center of the production process—a process that combines the strengths of man and machine.
Industry 5.0, which is a natural extension of Industry 4.0, is based on three interconnected pillars: human-centricity, resilience, and sustainability (
Figure 1) [
4].
First pillar—a human-centered approach. It places basic human needs and interests at the heart of the production process [
2]. It is based on technology-driven progress primarily focused on people and society. The role of technology in the context of Industry 5.0 is to serve people [
3], to create a safe and inclusive work environment, to ensure physical and mental health and well-being, and to protect the fundamental rights of workers and employees [
4,
5,
6].
Second pillar—sustainability. To comply with environmental principles, industry must be sustainable, i.e., apply circular processes and approaches that reuse, redirect, and recycle natural resources, reduce waste, reduce environmental impact, and lead to a circular economy with higher efficiency and zero resource waste [
2,
6].
Third pillar—resilience. This refers to the need to ensure sustainable industrial production, which eliminates the risk of disruptions, provides and maintains the necessary infrastructure in times of crisis. This requires the organization of production that is resilient enough to overcome natural disasters or political changes that occur in a short period [
2,
7,
8].
The Industry 5.0 technology framework addresses six key categories that provide direction for technological development [
9]:
Personalized human–machine interaction, which connects and combines the strengths of humans and machines;
Bio-inspired technologies and smart materials, with embedded sensors and enhanced functions that can be recycled;
Digital twins and simulations for modeling products, processes, and systems;
Technologies for data transmission, storage, and analysis;
Artificial intelligence (AI) for uncovering cause-and-effect relationships in complex, dynamic systems;
Technologies for energy efficiency, renewables, storage, and autonomy.
The main technological tools relied on for industrial development in the conditions of the new industrial era are summarized in
Figure 2 [
10].
Collaborative robots, artificial intelligence, technologies for the Internet of Things, augmented and virtual reality, digital twins, and 3D printing take a major place among them.
2. Exposition
Collaborative work is emerging as a key tool for achieving flexible, sustainable, and human-centric manufacturing, which is at the heart of Industry 5.0 [
10,
11]. These are robots that work alongside human operators, augmenting their capabilities and increasing productivity, safety, and efficiency [
12].
Collaborative robotics is seen as an emerging field, representing the intersection of robotics, automation, and Industry 5.0 [
13]. Current research emphasizes that in the context of Industry 5.0, cobots not only automate processes but also expand the possibilities for personalization and safety in the production environment [
14,
15]. It refers to the development and use of robots designed to work together with humans to achieve a synergistic effect. Cobots are equipped with advanced sensors, control systems, and safety features that allow them to work together with humans without causing risks. The differences between traditional industrial robots and cobots are summarized in
Table 1 [
16].
Cobots are designed to share a common workspace with humans, facilitating collaboration and the ability to work as a team [
17]. The main goal of cobots is to augment human capabilities, increase productivity, improve efficiency, and provide a safer and more flexible work environment.
The International Federation of Robotics (IFR) distinguishes four types of cobots [
18] depending on their collaboration with workers [
16]:
Independent cobots. These are robots that can function both independently and in collaboration with humans. They share the same workspace, but without direct overlap of the operations performed. They are designed for use in different production processes and for performing a variety of tasks. They are equipped with sensors and other elements to ensure safety;
Simultaneous cobots. In this type, the cobot and the operator work simultaneously on the same workpiece, performing different production operations simultaneously. In this cooperation, there is no dependence on the task, sequence, or execution time. Simultaneous joint processing reduces production time, increases productivity and efficiency. The cobot is relied on to perform potentially dangerous operations for humans, which increases safety and security in working conditions;
Sequential cobots work with humans on the same product, but not simultaneously, according to the sequence of the production process. The cobot is relied on to handle longer or repetitive operations. This improves working conditions for the operator. This type of collaboration results in better results, fewer errors, and reduced downtime between operations. Collaborative work is used in processes related to assembly, welding, or processing of raw materials;
Support cobots are a type of collaborative robots that allow for cooperation between an operator and a cobot in performing the same task or operation. There is complete dependence between humans and cobots—one cannot perform the task without the other. Together, as a team, they strive to achieve the set goal, achieving a balance in the advantages and disadvantages of the other (e.g., assembly, similar pick and place operations, or quality control inspection).
The rapid penetration and application of collaborative robots among industrial enterprises from various sectors of the economy is reflected in the continuous growth of the size of the global market for cobots. In 2024, the market was estimated at USD 4.21 billion. The increase compared to the previous year, 2023, is nearly USD 1 billion. Expectations by 2034 are to exceed USD 71 billion with a global average annual growth rate (CAGR) of 32.7% for the forecast period 2025–2034 (
Figure 3) [
19]. At the European level, the cobot market in 2024 was estimated at USD 1.27 billion. Forecasts show that 2034 will reach around USD 22.30 billion at a CAGR of 33.2% for the period 2025–2034.
All this further stimulates the expansion of the cobot market. Data on the share of the cobot market by region in 2024 (
Figure 4) shows that Europe occupies a leading position with a share of 30.11%, followed by North America with a share of 28.41% and the Asia-Pacific region with a share of 25.23% [
19].
3. A Conceptual Engineering Model for the Integration of Collaborative Robots in Industry 5.0 Systems
The proposed conceptual engineering model for the integration of collaborative robots (cobots) in the context of Industry 5.0 offers a systematic and purposeful approach to the joint work between humans and machines in an intelligent manufacturing environment. The model combines key technological elements with the principles of sustainability, safety, and human-centricity, which are the basis of modern industrial transformation.
The purpose of this model is to visualize the interaction between cobot systems, artificial intelligence, the Internet of Things (IoT), digital twins, and integrated safety systems, to achieve a flexible, sustainable, and human-centered manufacturing environment.
Although there are various models for the integration of collaborative robots in industrial environments in the literature [
20,
21], the majority of them mainly consider the technological architecture or behavioral aspects of operator acceptance. The current model is distinguished by an integrated, holistic approach that unites not only technological components but also strategically emphasizes three key dimensions of Industry 5.0: human-centricity, sustainability, and resilience of the production system. Unlike previous concepts that focus mainly on operational efficiency, our proposal considers a systematic and synchronized interaction between cobot systems, IoT infrastructure, digital twins, and safety standards, in the context of specific requirements of the production environment. Thus, the model builds on the existing ones by creating a platform for flexible and rapid adaptation to dynamic market requirements and scalability opportunities in different industrial sectors.
The main components of the model proposed in this study include three interconnected layers. The input layer covers the production specifications and requirements, the needs of operators related to ergonomics, safety, and well-being, as well as environmental goals for waste minimization and resource optimization. The process layer integrates collaborative robots with their hardware modules, manipulators, and sensors, supported by artificial intelligence for predictive maintenance and adaptive control. This also includes IoT connectivity for real-time data exchange, digital twins for process simulation and optimization, and integrated safety systems compliant with ISO 10218 [
22] and ISO/TS 15066 [
23] standards. The output layer summarizes the achieved results, such as increased efficiency and productivity, ensuring a safe and sustainable working environment, flexibility for personalized production, and a reduced environmental footprint.
Figure 5 presents the three-layer architecture of the proposed concept. The input layer defines the production and human requirements, which are processed in the process layer using modern technologies to achieve specific production and sustainability goals, represented in the output layer.
From an engineering perspective, this model provides modularity and scalability, allowing easy adaptation to different production environments and scales. It also provides benefits such as predictive maintenance, increased energy efficiency, and improved operator safety.
The proposed conceptual engineering model for the integration of collaborative robots in Industry 5.0 systems is validated through two case studies presented in the next Section.
4. Case Studies
To verify the proposed conceptual engineering model, this Section presents two case studies from the food industry. Each of them illustrates a different aspect of the integration of collaborative robots in accordance with the principles of Industry 5.0.
The first case study examines the automation of a yogurt packaging line, where the collaborative robot performs tasks such as labeling, visual inspection, and sorting into packages. The solution is supported by an IoT connection and a digital twin.
The second case study focuses on a more complex task—sorting apples based on visual inspection using artificial intelligence. In this case, the cobot works with an edge AI platform and a machine vision camera for real-time defect detection.
The two case studies demonstrate the flexibility, adaptability, and engineering applicability of the model in different production environments with varying degrees of product complexity and sensitivity.
4.1. Collaborative Robot in a Dairy Packaging Line
Although there are publications in the scientific literature examining the applications of collaborative robots in manufacturing environments [
24,
25], the present case study is innovative in several key aspects. Currently, there is no fully developed scenario dedicated to the integration of cobots in packaging processes in the dairy industry, and in particular, in the production of yogurt.
The choice of this sector is justified by the high sensitivity to hygiene and product quality, as well as the need for personalized and flexible packaging solutions. The case study represents an original contribution, as it simulates a realistic manufacturing environment in which the cobot performs several critical operations—automatic labeling, visual inspection, and sorting of packaged products. All these actions are integrated into a single-flow process, connected through an IoT infrastructure and optimized through a digital twin (see
Figure 6).
The case study places particular emphasis on engineering parameters such as energy efficiency and cycle time reduction, as well as economic indicators such as return on investment—elements that are often considered in isolation in other studies or the context of different industries.
The production process for yogurt packaging includes sequential operations: package preparation, filling, heat sealing, labeling, quality control, and final packaging. In the present case study, the collaborative robot is integrated precisely at critical points in the process—it automates labeling, performs visual quality control, and manipulates finished products. In this way, not only is productivity increased, but also the safety and flexibility of the production environment.
Figure 6 presents a visualization of the production flow when implementing a collaborative robot in a yogurt packaging line. The main stages in which the robot actively participates are depicted, as well as interactions with Internet of Things (IoT) systems, digital twins, and artificial intelligence algorithms aimed at increasing efficiency and quality.
The engineering analysis of the implementation shows significant improvements in the production process. After the integration of the cobot, the processing time for one package is reduced from approximately eight seconds to approximately five seconds. Labeling errors fall below 0.5%, leading to a significantly improved quality of the final product. The optimization of work paths contributes to an approximately 15% increase in energy efficiency, and the return on investment is estimated at approximately eighteen months.
This practical case study convincingly demonstrates that the integration of a collaborative robot not only increases the efficiency and quality of production, but also creates a safe and sustainable working environment, fully consistent with the principles and goals of Industry 5.0.
4.2. Collaborative Robot for AI-Assisted Fruit Sorting
This case study investigates the applicability of the proposed conceptual engineering model in the context of a typical agro-industrial sorting process focused on apples. The simulation scenario was developed by analyzing common production settings where fruit classification is performed manually by visual inspection. Such traditional methods are characterized by relatively low productivity, a high degree of subjectivity, and significant physical strain on human operators, especially during seasonal production peaks.
Although there are studies dedicated to the application of robotics and machine vision in the agricultural sector, the available literature rarely presents a comprehensive scenario that integrates a collaborative robot, a soft manipulator, artificial intelligence flaw detection, edge computing, and a digital twin into a single sorting system. The present case study represents an original contribution, demonstrating the real possibility of real-time visual inspection and classification using an embedded AI model (MobileNetV2) implemented on an edge platform (Jetson Nano) and implemented by a collaborative robot with safe and adaptive manipulation. Integration with IoT and a simulation environment via a digital twin further reinforces the applicability of the model in the environment of sensitive products and seasonal variability.
The integrated engineering solution includes a collaborative robot model UR5 with a load capacity of 5 kg and an action radius of 850 mm, equipped with a mobile AI-based camera and a modified soft gripper designed to reduce pressure on the fruits. Safety during joint work with operators is ensured by compliance with ISO/TS 15066, through automatic force limitation and contact reaction [
26]. The UR5 is controlled by a Robot Operating System ROS2-compatible architecture and is configured to work with the assembly line via an Ethernet/IP interface.
A key role in the system is played by a MobileNetV2 neural network, implemented in an edge environment using an NVIDIA Jetson Nano, trained for binary classification of defective/non-defective apples. This model was chosen due to its high accuracy-to-compute efficiency ratio, allowing real-time processing [
27]. The training was performed on a set of 10,000 apple images containing examples of typical defects such as dents, rot, or uneven color. Pre-processing includes augmentation by rotation, noise, and lighting variations.
The system is connected via an IoT platform based on the Message Queuing Telemetry Transport (MQTT) protocol and a Representational State Transfer (REST) API, providing real-time tracking of production and component status [
28]. In addition, a digital twin was implemented via the Gazebo simulation environment, which replicates the physical sorting line and allows simulation under different loads and types of errors [
29].
Figure 7 presents a visualization of the operation of the integrated system, demonstrating the interaction between the hardware and software components. On the left is the inbound transport of the fruit, followed by the sorting station with the collaborative robot and the vision system, and on the right—the processes of classification, connectivity, and data exchange with the IoT and the digital twin.
The engineering analysis of the system was carried out within a two-month experimental simulation phase, in which key indicators were investigated before and after the virtual implementation of the collaborative robot through a digital twin and load modeling. The analysis was based on the processing of a representative sample of over 10,000 sorted units, in a scenario corresponding to realistic production conditions in companies in the agro-industrial sector.
The measured production speed was increased from approximately 300 to over 780 fruits per hour, the result of an optimized work rhythm achieved through automation and parallel visual classification. The proportion of incorrectly classified objects (e.g., defective but undetected fruits) was reduced from 4.5% to below 1.2%, thanks to the MobileNetV2 neural network, pre-trained on images with different types of defects. Damage due to manipulation was reduced to 0.3% by using a soft adaptive gripper with variable pressure, which reduces mechanical pressure on the product.
By simulating the dynamics of energy consumption and introducing an algorithm for transition to an energy-saving mode in the absence of input flow, a reduction in the expected average load by approximately 18% was achieved. Based on average values and typical costs for equipment, integration, and training, the expected return on investment is calculated at about 12 months, which confirms the economic efficiency of the approach in conditions of an average-loaded manufacturing enterprise with the ability to adapt to seasonal fluctuations.
5. Stages for Implementing Collaborative Robots in the Food Industry
In the context of the developed conceptual engineering model and the practical application of collaborative robots in the dairy packaging line, a logical and strategic component of the study is the outline of a roadmap for their phased implementation in food and beverage companies. Such a roadmap would help to structure the integration stages, but would also provide a clear time frame for achieving a sustainable transformation of production processes, consistent with the goals of Industry 5.0, as illustrated in
Figure 8.
The initial stage is focused on the preparation and analysis of the existing production environment. It includes a technical and economic assessment of processes with high automation potential, as well as identification of critical points for the integration of collaborative robots. At this stage, regulatory requirements related to food safety and equipment certification, including Hazard Analysis and Critical Control Points (HACCP), ISO 22000 [
30], and CE (from French: Conformité Européenne) compliance, are also considered.
The next phase envisages a pilot implementation of a collaborative robot in a specific production unit. The main goal here is to validate the applicability and effectiveness of the system in real production conditions. This includes installing a cobot for specific operations such as labeling and visual inspection, integrating basic IoT connectivity for process monitoring, and providing training for operators to safely and effectively operate the new equipment.
After the successful pilot implementation, the roadmap envisages scaling and expanding the integration to a wider range of production operations. During this stage, connectivity with comprehensive manufacturing execution systems (MESs) is deployed, as well as the implementation of digital twins for simulation and optimization of the flow of materials and products. This increases efficiency and shortens the response time to changes in production conditions.
The final phase of the roadmap is characterized by achieving a high degree of autonomy of the production environment. Through comprehensive integration of cobot systems with IoT and cloud platforms for real-time data analysis, enterprises achieve conditions for predictive maintenance, optimization of energy consumption, and dynamic adaptation of the production plan to market demand. Advanced sensor technology and intelligent adaptive behavior algorithms contribute to further enhancing safety and efficiency, while the implementation of a continuous improvement cycle ensures sustainable growth and competitiveness in the long term.
This roadmap not only reflects the sequence and logic of implementing collaborative robots, but also positions the research in a practical perspective, providing concrete guidelines for industrial transformation compatible with the vision of Industry 5.0.
The analysis of the steps outlined in the roadmap shows that the implementation of the integrated concept with collaborative robots not only modernizes the production process, but also significantly outperforms traditional automation approaches that are typical for enterprises in the food and beverage industry. In traditional systems, automation is often rigidly configured, with limited adaptability and high dependence on specific programming for each change in the product line.
In contrast, the integrated approach with cobots provides a high degree of flexibility, easy reconfiguration and the possibility of human–machine collaboration in real time. In addition, the use of IoT technologies and digital twins in interaction with collaborative robots allows for dynamic adaptation to changing market requirements and optimization of production resources.
The advantages of this approach can be systematized through a comparative analysis that highlights the key differences compared to traditional automated systems. The summarized comparison between traditional and collaborative automation is presented in
Table 2.
The comparative analysis clearly demonstrates that an integrated approach with collaborative robots offers significant strategic advantages over traditional automation. The flexibility and adaptability that cobots provide enable food and beverage companies to effectively respond to dynamic market demands and growing expectations for personalized products. Additionally, the reduction in implementation time and lower investment costs improve the financial sustainability of companies, while built-in safety and predictive maintenance systems minimize operational risks.
This comparative analysis supports the proposed roadmap and reinforces the argument that the application of collaborative robots in the food and beverage industry represents a strategic transition from classic automation to an intelligent, sustainable, and human-centric production ecosystem.
The results of the study confirm the effectiveness of the integrated approach with collaborative robots in the context of the food industry. Within the practical case studies, focused on both a yogurt packaging line and an AI-assisted fruit sorting process, significant improvements in key production parameters were observed following the implementation of the cobot systems. In the dairy sector, the processing time for a single package was reduced from approximately eight seconds to about five seconds (over 35%), comparable to the results achieved by Faccio et al. (2020), who reported a 30% reduction in cycle time when implementing collaborative robotics in food production [
24]. In addition, labeling errors fell below 0.5%, which exceeds the indicators indicated in the study by Montville (2022), where through the integration of a collaborative robot, errors were reduced to about 1% in other sectors of the food industry [
25]. The approximately 15% energy efficiency improvement after trajectory optimization and dynamic energy management confirms the findings of Zhang et al. (2024) on the potential of integrated solutions with IoT and digital twins to contribute to energy sustainability [
20].
In the fruit sorting case, throughput increased from 300 to over 780 units/hour, with classification errors reduced to under 1.2% and physical damage minimized to 0.3%, confirming the model’s adaptability to complex inspection tasks involving sensitive agricultural products.
The comparative analysis between traditional automation and the integrated approach with collaborative robots further highlights the advantages of the new concept. While traditional solutions are characterized by a high degree of rigidity and complexity in line reconfiguration, cobot-based automation offers significantly greater flexibility and the possibility of personalization of production processes. The analysis of Muller and Steinhilper (2021) also confirms that increased adaptability is among the key factors for accelerated adoption of collaborative robots in manufacturing environments [
21].
A significant advantage of the integrated approach is also the improved interaction with operators. Unlike traditional automated systems, where human participation is minimized, cobot systems create the opportunity for collaborative work in a shared environment. This improves not only productivity, but also employee satisfaction, which is also indicated in the study by Weigl and Weber (2023), examining the social aspects of human–robot interaction in the context of Industry 5.0 [
15].
Despite the positive results achieved, certain limitations should be noted. Initial implementation requires investment in staff training and a preliminary assessment of the production environment for compatibility with collaborative automation systems. Additionally, optimal integration with IoT and digital twins presupposes a certain maturity of the enterprise’s digital infrastructure, which can be a challenge for small and medium-sized enterprises. Despite these factors, the overall analysis confirms that the implementation of collaborative robots, combined with intelligent management and control technologies, represents an effective and sustainable path for the transformation of the food industry towards the vision of Industry 5.0. The results obtained not only demonstrate the real applicability of the developed conceptual model, but also open up prospects for future upgrading and expansion of its application in other sectors of the industry.
6. Results and Discussion
The results of the study confirm the effectiveness of the integrated approach with collaborative robots in the context of the food industry. Within the practical case studies, focused on a yogurt packaging line and an AI-assisted fruit sorting process, significant improvements in production parameters were observed after the implementation of the cobot systems.
In the dairy sector, the processing time for a single package was reduced from approximately eight seconds to about five seconds, which represents a reduction of over 35%, comparable to the results achieved by Faccio et al. (2020), who reported a 30% reduction in cycle time when implementing collaborative robotics in food production [
24]. In addition, labeling errors fell below 0.5%, which exceeds the indicators indicated in the study by Montville (2022), where through the integration of a collaborative robot, errors were reduced to about 1% in other sectors of the food industry [
25]. The approximately 15% energy efficiency improvement after trajectory optimization and dynamic energy management confirms the findings of Zhang et al. (2024) on the potential of integrated solutions with IoT and digital twins to contribute to energy sustainability [
20].
The return on investment for the proposed solution is estimated to be around 18 months, which is more favorable than the typical values for traditional automation in the food industry, which range between 24 and 36 months [
21]. This supports the claims of Rahman et al. (2024) that collaborative robots can provide a shorter investment cycle due to their high adaptability and lower integration costs [
11].
In the fruit sorting case, throughput increased from approximately 300 to over 780 units per hour, while classification errors were reduced from 4.5% to below 1.2%. Physical damage to the product decreased to 0.3% due to the use of a soft adaptive gripper with variable-pressure actuation, designed specifically for delicate produce handling. This result aligns with recent studies on soft robotic grippers that emphasize the importance of flexible, compliant structures in minimizing mechanical stress during agricultural manipulation [
31]. Moreover, additive manufacturing techniques and silicone-based materials have been shown to improve grip conformity for irregularly shaped products and are increasingly adopted in food robotics [
32].
The achieved increase in throughput and reduction in classification error further confirm the feasibility of applying cobot-based AI systems in real-time agricultural sorting. Prior experimental research on robotic sorting of fruits by color and size has shown similar improvements when using machine vision and automated manipulation, although often without integration of edge AI or soft gripping [
33].
Energy consumption was reduced by approximately 18% through the implementation of an energy-saving mode triggered by input flow detection. The return on investment was estimated at around 12 months, confirming the economic feasibility of the system in dynamic, seasonally dependent environments.
Furthermore, the demonstrated compatibility of the conceptual model with soft robotics, real-time vision systems, and IoT-enabled simulation environments highlights its potential beyond fixed packaging lines. The dual-arm fruit harvesting architecture explored by Yamamoto et al. illustrates that such integrated systems can be adapted for more complex handling scenarios and multitask cobot coordination [
34].
The comparative analysis between traditional automation and the integrated approach with collaborative robots further highlights the advantages of the new concept. While traditional solutions are characterized by a high degree of rigidity and complexity in line reconfiguration, cobot-based automation offers significantly greater flexibility and the possibility of personalization production processes. The analysis of Muller and Steinhilper (2021) also confirms that increased adaptability is among the key factors for accelerated adoption of collaborative robots in manufacturing environments [
21].
A significant advantage of the integrated approach is also the improved interaction with operators. Unlike traditional automated systems, where human participation is minimized, cobot systems create the opportunity for collaborative work in a shared environment. This improves not only productivity but also employee satisfaction, which is also indicated in the study by Weigl and Weber (2023), examining the social aspects of human–robot interaction in the context of Industry 5.0 [
15].
Despite the positive results achieved, certain limitations should be noted. Initial implementation requires investment in staff training and a preliminary assessment of the production environment for compatibility with collaborative automation systems. Additionally, optimal integration with IoT and digital twins presupposes a certain maturity of the enterprise’s digital infrastructure, which can be a challenge for small and medium-sized enterprises.
Nonetheless, the overall analysis confirms that the implementation of collaborative robots, combined with intelligent management and control technologies, represents an effective and sustainable path for the transformation of the food industry towards the vision of Industry 5.0. The results obtained from both case studies not only demonstrate the real applicability and scalability of the developed conceptual model but also open up prospects for future adaptation in other industrial contexts characterized by sensitivity, variability, or high customization requirements.
7. Conclusions
This study confirms that the integration of collaborative robots as part of a comprehensive engineering approach is an effective tool for transforming the food industry in line with the principles of Industry 5.0. The proposed conceptual model, validated through two practical cases—yogurt packaging and apple sorting—demonstrates its applicability in both structured and variable production environments.
The combination of collaborative robots with IoT infrastructure, digital twins, and embedded artificial intelligence leads to measurable improvements in productivity, energy efficiency, process flexibility, and operator safety. The results obtained show that the integrated approach is not just a technological upgrade, but a strategic shift towards a more intelligent, sustainable, and human-centric production environment.
The comparative analysis with traditional automation highlights significant advantages—shorter implementation time, faster return on investment, and the ability to dynamically adapt to seasonal or market changes. These features position collaborative robots as a key enabler for innovation in industrial environments with high demands for adaptability and safety.
The demonstrated results in two different manufacturing situations suggest that similar benefits can be achieved in other industrial sectors, especially where there is a need for delicate product handling, visual inspection, or customized manufacturing. Of course, application in other contexts will require consideration of specific requirements, but the modular architecture of the proposed solution facilitates such adaptation.
In the future: the methodology can be extended with more advanced algorithms for autonomous decision-making and continuous optimization. Further research could focus on the socio-economic impact of cobot deployment and the development of standardized frameworks for their integration in different industrial environments.