Transforming Robots into Cobots: A Sustainable Approach to Industrial Automation

Abstract

The growing need for sustainable and flexible automation solutions has led to the exploration of transforming traditional industrial robots into collaborative robots (cobots). This paper presents a framework for the conversion of conventional industrial robots into safe, intelligent, and sustainable cobots, leveraging advancements in artificial intelligence and computer vision and the principles of the circular economy. The proposed modular framework contains key components such as visual perception, cognitive adaptability, safe human–robot interactions, and reinforcement learning-based decision-making. Our methodology includes a comprehensive analysis of safety standards (e.g., ISO/TS 15066), robot typologies suitable for retrofitting, and sustainability strategies, including remanufacturing and lifecycle extension. A multi-phase implementation approach is laid out for a theoretical design to contribute to the development of cost-effective and environmentally responsible robotic systems, offering a scalable solution for extending the usability and social acceptance of legacy robotic platforms in collaborative settings.

1. Introduction

Over the past few decades, industrial automation has significantly transformed manufacturing processes by increasing precision, efficiency, and productivity [1]. Traditional industrial robots, characterized by their rigidity, high-speed operation [2], and isolation from human workers, have become a staple in sectors such as automotive assembly, electronics, and heavy industry [3]. However, the evolving demands of modern manufacturing, including increased flexibility, human–machine collaboration and adaptiveness, environmental responsibility, and invested capital requirements, to name a few, have exposed the limitations of legacy robotic systems [4]. As industries strive for more adaptable, sustainable, and human-centered technologies, a new paradigm has emerged: collaborative robotics [5].

Collaborative robots, or cobots, are designed to operate safely alongside human workers, enabling shared workspaces and the completion of cooperative tasks without the need for physical barriers. Unlike their traditional counterparts, cobots include advanced sensing, perception, and control mechanisms that allow them to adapt to dynamic environments and interact intelligently with human operators [6]. The adoption of cobots has accelerated in recent years, not only in large-scale manufacturing but also in small and medium-sized enterprises (SMEs), due to their flexibility, ease of integration, and capacity to enhance human productivity [7].

Despite this trend, the replacement of conventional industrial robots with new-generation cobots presents economic and environmental challenges. Many existing robotic systems are still mechanically functional and constitute a significant investment for companies. Disposing of or replacing these robots contributes to electronic waste and contradicts the principles of sustainable development and the circular economy, which advocate for the reuse, remanufacturing, and lifecycle extension of existing assets [8,9].

In this context, the present research proposes a framework for converting traditional industrial robots into “intelligent”, sustainable, and collaborative systems. This transformation leverages current advances in artificial intelligence (AI), computer vision, and reinforcement learning, while being in compliance with safety standards such as ISO/TS 15066 [10]. Rather than replacing outdated systems, this approach seeks to breathe new life into them, equipping industrial robots with the cognitive, perceptual, and control capabilities necessary for safe and efficient human–robot collaboration.

The proposed solution is grounded in a modular design philosophy, allowing the scalable integration of key technologies: visual perception for scene understanding and human detection; adaptive control algorithms to enable safe and context-aware interactions; and AI-driven decision-making systems for continuous task optimization and learning. By enabling the repurposing of legacy robots into modern cobots, this framework not only reduces the environmental footprint of industrial automation but also democratizes access to collaborative robotics, especially for SMEs with limited budgets. It offers a cost-effective, scalable, and ecologically responsible path forward for industries seeking to embrace the Industry 5.0 principle [11] of human-centric, sustainable, and resilient production.

The remainder of this paper is organized as follows: Section 2 presents the methodology used to select and classify the reviewed literature and to define the conceptual framework built for the sustainable retrofitting of industrial robots into collaborative systems. Section 3 provides an in-depth analytical review of the state of the art. It covers international safety standards and regulatory frameworks, the classification and reusability potential of different types of industrial robots, sustainability strategies based on circular economy principles (such as remanufacturing and lifecycle extension), and the role of enabling technologies such as artificial intelligence, computer vision, and advanced control systems. Section 4 introduces the conceptual design of our modular conversion framework, which includes a layered architecture for perception, control, human–robot interactions, and adaptive decision-making. Section 5 outlines the evaluation methodology and benchmarking criteria used, addressing safety compliance, retrofit feasibility, environmental and economic impact, and scalability. Section 6 discusses the implications, challenges, and limitations of the proposed approach. Section 7 presents the main conclusions, and Section 8 outlines future research directions aimed at transitioning this framework from theoretical development to practical implementation.

2. Methodology

This research is currently at the conceptual development stage, focused on building the theoretical foundation and the design principles of a modular framework intended for the sustainable conversion of industrial robots into collaborative robotic systems (cobots). Our methodological approach is structured into three core stages, ensuring that analytical and design-oriented tasks are grounded in multidisciplinary perspectives.

2.1. Analytical Review of the State of the Art

This section presents a comprehensive review of the available academic literature, with the aim of exploring the current state of knowledge regarding the issue we seek to address. The main objective of this review is to determine whether there are documented precedents in which other authors have analyzed or addressed similar topics, which would help to contextualize the research within existing theoretical frameworks and identify possible knowledge gaps.

The methodology used, which is shown in Figure 1, involves the initial preselection of scientific databases recognized for their quality and relevance in the fields of engineering and robotics, such as IEEE Xplore, Scopus, ScienceDirect, and SpringerLink, among others. Once the sources were defined, a set of search criteria was established, including keywords, Boolean operators, and filters for language, publication year, document type, and subject area, in order to narrow the results to relevant publications.

Subsequently, the process continued with the identification, analysis, and selection of articles, conference papers, and systematic reviews that, either directly or indirectly, provide relevant insights into the central objective of the topic. The search was particularly focused on studies that document processes for adapting industrial robots for use as collaborative robots, whether through physical modifications, software upgrades, sensor implementation, or redesigns of their interface and control systems.

This approach not only enables the mapping of current trends and methodological approaches employed by other researchers, but also identifies technological, regulatory, and sustainability dimensions relevant to the proposed transformation of industrial robots. Since the topic has been approached from a commercial rather than an academic perspective, as will be shown later, additional topics include the following:

• Safety standards and regulatory frameworks: A study of international collaborative robot standards such as ISO/TS 15066 and ISO 10218-1/2 [10,12,13] to identify safety requirements and constraints that must be addressed in any conversion effort.
• Industrial robot typologies: A classification of existing industrial robot models is conducted, focusing on their mechanical robustness, the openness of their control architecture, and their potential for hardware/software upgrades.
• Sustainability and circular economy strategies: A review of concepts such as remanufacturing, lifecycle extension, modular design, and environmental impact to ensure the framework aligns with the principles of sustainable robotics.

2.2. Conceptual Design of a Modular Conversion Framework

Based on insights derived from the literature and industrial best practices, a theoretical framework is proposed. This framework is designed to be modular, scalable, and adaptable to various industrial robot types and collaborative scenarios. The framework comprises four theoretical modules:

• A perception layer: A layer designed for visual perception and environmental awareness using computer vision and depth sensing; several works have studied the use of 3D data to solve different vision-based robotics tasks [14,15,16,17,18,19,20].
• A safe interaction layer: This layer includes the principles behind control strategies enabling safe human–robot interactions, compliant motion, and reactive behavior.
• A sensor integration strategy: Guidelines for integrating multimodal sensing systems (e.g., force, tactile, proximity) into robots for enriched situational awareness.
• A cognitive adaptability layer: A theoretical model for embedding AI-based learning and decision-making capabilities into robots for task adaptation and collaboration.

Each module is defined at a conceptual level, with its expected function, internal components, and interactions with the overall architecture described.

2.3. Theoretical Impact and Framework Evaluation Criteria

Although the framework has not yet been implemented or validated, a set of evaluation criteria is defined to guide future empirical phases:

• Safety compliance potential;
• Feasibility of retrofitting across different robot types;
• Alignment with circular economy principles;
• Scalability and modularity;
• Potential for integration into SMEs;
• Expected environmental and economic benefits.

These criteria will serve as a benchmark for assessing the framework’s value in future stages of the research. This methodology outlines the theoretical groundwork necessary to enable the transition from conventional industrial automation to collaborative, sustainable robotic systems. By focusing on safety, modularity, AI readiness, and sustainability, the proposed conceptual framework offers a structured path forward for the responsible retrofitting of existing robotic infrastructure.

3. An Analytical Review of the State of the Art

The transformation of traditional industrial robots into collaborative systems necessitates a detailed understanding of current technologies, regulatory frameworks, and sustainable engineering practices. This section reviews key components that influence the feasibility and direction of robot retrofitting, focusing on a search for publications related to the conversion of industrial robots into collaborative robots and current safety regulations, industrial robot typologies, and sustainable design principles.

3.1. Conversion of Industrial Robots into Cobots

Based on the bibliographic research carried out following the systematic search methodology illustrated in Figure 1, it was determined that topics related to the “conversion of industrial robots into collaborative robots” have been only partially addressed in the available scientific literature to date. The review was conducted by consulting various specialized databases such as IEEE Xplore, ScienceDirect, SpringerLink, and Scopus, among others. A series of predefined keywords and search combinations were applied, aiming to cover all the technical, regulatory, and methodological aspects involved in the conversion process.

However, upon analyzing the results, it was observed that the identified publications do not comprehensively cover the proposed topic. Most of the studies focus only on specific components, such as collaborative programming, robotic cell redesign, or the integration of safety sensors, without considering a holistic perspective on the transition from a traditional industrial robotic system to a collaborative one.

Additionally, there was a limited number of works addressing regulatory frameworks, certification requirements, or the technical challenges associated with adapting existing robots to collaborative environments, especially in industries requiring high levels of safety and operational efficiency. This indicates that the topic is still an emerging area of research with significant potential for future academic and technological contributions.

Table 1 presents a summary of the most relevant aspects covered in the main publications found, along with how they relate to the search criteria defined in this study. This comparison highlights the existing gaps in the current literature and supports the need for a more in-depth and multidisciplinary analysis of the subject.

During the literature review, five recurring thematic areas were identified that help classify the content found according to its technical and conceptual focus. These areas are Security, Control, Interaction, Detection, and Requirements. Each area is described in detail below:

• Security: This category includes publications that address topics related to physical and logical safety in collaborative environments, such as safety standards, collision prevention mechanisms, risk analyses, and compliance with international regulations. Security is a critical component in integrating robots into industrial settings where they interact with humans, ensuring system reliability and incident prevention.
• Control: This includes studies focused on the design, improvement, or implementation of algorithms that govern the dynamic behavior of robots. Topics include motion control, trajectory planning [21], action synchronization, real-time adaptation, and energy management. These algorithms are essential for ensuring the smooth, efficient, and safe operation of robots in environments shared with human operators.
• Interaction: This area refers to research exploring how human–robot interactions take place, both physically and cognitively. It covers user interfaces, multimodal systems (voice, gestures, displays), non-verbal communication, robotic empathy, and user experience design. Well-designed interactions enhance collaboration, reduce user cognitive load, and increase system acceptance.
• Detection: This includes all publications involving the use of sensors as an integral part of the collaborative system. It encompasses proximity sensors, computer vision, force/torque sensors, presence detection, and environmental perception systems. These components enable the robot to perceive its surroundings and respond contextually, enhancing its ability to handle unforeseen events and support safe collaboration.
• Requirements: This category includes works that allow the extraction or definition of desirable features for a collaborative robot (cobot). It may cover technical specifications, user-centered design criteria, industrial environment expectations, performance evaluations, or comparative analyses of different robotic solutions. These publications are useful for identifying functional and non-functional guidelines for the development or enhancement of collaborative systems.

These five areas provide a structured framework for document analysis and help identify technological trends, user needs, and current challenges in implementing collaborative robots in production environments.

Table 1. First-phase documentation consulted, arranged by type of content covered. An “X” indicates that the referenced work addresses the corresponding thematic area.

Ref.	Security	Control	Interaction	Detection	Requirements	Ref.	Security	Control	Interaction	Detection	Requirements
[22]	X	-	-	X	-	[23]	-	-	-	-	X
[24]	X	-	-	X	-	[25]	-	-	X	X	-
[26]	-	X	-	X	-	[27]	-	-	X	-	X
[28]	-	X	-	-	X	[29]	-	X	X	-	-
[30]	-	-	X	-	X	[31]	-	-	X	-	X
[32]	X	X	-	-	-	[33]	-	-	X	-	X
[34]	-	-	X	-	X	[35]	-	-	-	-	X
[36]	-	X	-	X	-	[37]	-	-	-	-	X
[6]	X	-	-	-	X	[38]	X	-	-	-	-
[39]	X	-	-	-	X	[40]	-	X	-	-	X
[41]	X	X	-	-	X	[42]	-	-	X	-	-
[43]	-	X	-	-	-	[44]	-	-	-	-	X
[45]	X	-	-	-	X	[46]	-	-	-	-	X
[47]	-	-	X	-	-	[48]	-	-	-	-	X
[49]	X	X	-	X	-	[50]	-	X	-	-	-
[51]	-	-	-	-	X	[52]	-	-	-	-	X
[53]	-	-	X	-	X	[54]	-	X	-	-	-
[55]	-	-	-	-	X	[56]	-	X	X	-	-
[57]	X	-	-	X	-	[58]	-	-	X	X	-
[59]	X	-	-	-	-	[60]	-	-	-	-	X
[61]	-	-	-	-	X	[62]	-	-	X	X	-
[63]	-	-	-	-	X	[64]	X	-	-	-	X
[65]	-	-	X	-	X	[66]	-	X	-	-	-
[67]	-	X	X	-	-	[68]	-	-	X	-	-
[69]	-	X	-	-	-	[70]	-	-	X	-	X
[71]	-	-	X	-	X	[72]	-	-	-	-	X
[73]	-	X	X	-	-	[74]	-	-	-	-	X
[75]	-	-	-	-	X	[76]	-	-	-	-	X
[77]	X	-	-	-	X	[78]	-	X	-	-	X
[79]	-	-	X	-	-	[80]	-	-	X	-	-
[81]	-	X	X	-	-	[82]	-	-	X	-	-

Table 1. First-phase documentation consulted, arranged by type of content covered. An “X” indicates that the referenced work addresses the corresponding thematic area.

Ref.	Security	Control	Interaction	Detection	Requirements	Ref.	Security	Control	Interaction	Detection	Requirements
[22]	X	-	-	X	-	[23]	-	-	-	-	X
[24]	X	-	-	X	-	[25]	-	-	X	X	-
[26]	-	X	-	X	-	[27]	-	-	X	-	X
[28]	-	X	-	-	X	[29]	-	X	X	-	-
[30]	-	-	X	-	X	[31]	-	-	X	-	X
[32]	X	X	-	-	-	[33]	-	-	X	-	X
[34]	-	-	X	-	X	[35]	-	-	-	-	X
[36]	-	X	-	X	-	[37]	-	-	-	-	X
[6]	X	-	-	-	X	[38]	X	-	-	-	-
[39]	X	-	-	-	X	[40]	-	X	-	-	X
[41]	X	X	-	-	X	[42]	-	-	X	-	-
[43]	-	X	-	-	-	[44]	-	-	-	-	X
[45]	X	-	-	-	X	[46]	-	-	-	-	X
[47]	-	-	X	-	-	[48]	-	-	-	-	X
[49]	X	X	-	X	-	[50]	-	X	-	-	-
[51]	-	-	-	-	X	[52]	-	-	-	-	X
[53]	-	-	X	-	X	[54]	-	X	-	-	-
[55]	-	-	-	-	X	[56]	-	X	X	-	-
[57]	X	-	-	X	-	[58]	-	-	X	X	-
[59]	X	-	-	-	-	[60]	-	-	-	-	X
[61]	-	-	-	-	X	[62]	-	-	X	X	-
[63]	-	-	-	-	X	[64]	X	-	-	-	X
[65]	-	-	X	-	X	[66]	-	X	-	-	-
[67]	-	X	X	-	-	[68]	-	-	X	-	-
[69]	-	X	-	-	-	[70]	-	-	X	-	X
[71]	-	-	X	-	X	[72]	-	-	-	-	X
[73]	-	X	X	-	-	[74]	-	-	-	-	X
[75]	-	-	-	-	X	[76]	-	-	-	-	X
[77]	X	-	-	-	X	[78]	-	X	-	-	X
[79]	-	-	X	-	-	[80]	-	-	X	-	-
[81]	-	X	X	-	-	[82]	-	-	X	-	-

In terms of the safety of collaborative robotics, three of the analyzed publications propose advanced and validated technical solutions aimed at protecting operators and preventing accidents. In particular, the development of hybrid sensors for simultaneous proximity and force detection, the implementation of Time-of-Flight sensor-based safety curtains for end effectors, and active collision assessment systems using adaptive vision [22,24,57] directly address the main limitations of traditional methods and significantly enhance the effectiveness of safety systems in collaborative environments.

In the control section, the most relevant articles comprehensively address key aspects of control in collaborative robotics, from the classification and standardization of control strategies, the implementation of reactive safety mechanisms, and the design of intelligent systems with sensor fusion for safe collaboration to the development of adaptive strategies for teleoperation in varied environments [26,28,56,69]. These approaches combine theoretical foundations with practical applications, laying the groundwork for safe and efficient control systems in human–robot interactions.

In terms of addressing the fundamental aspects of human–robot collaboration, several key contributions stand out. On the one hand, the effects of continuous learning and human perception are explored in relation to robots that adapt their behavior over time [67], as is the impact of joint planning supported by advanced language models on achieving more natural and effective communication [29]. Additionally, a taxonomy of the factors influencing worker performance during collaboration with robots is presented [30], providing a structured foundation for interaction analyses. Finally, the importance of transparency in robotic architectures is highlighted as a key element for enhancing trust and cooperation between humans and robots [31]. These approaches contribute to the development of more adaptive, understandable, and user-centered collaborative systems.

In the field of sensing, several developments stand out as particularly relevant for enhancing safety and efficiency in human–robot interactions. These include human position detection based on depth cameras, which enables real-time person identification and reduces false positives in industrial environments [25], and flexible chain-type ToF sensor modules that facilitate proximity detection on irregular robotic surfaces [58]. Additionally, the integration of hybrid capacitive and piezoresistive sensors allows for the simultaneous detection of proximity and force, optimizing both collision avoidance and safe manipulation [22]. Finally, the implementation of Time-of-Flight sensor-based safety curtains on end effectors offers a precise and standard-compliant solution for protecting critical zones [24]. These innovations represent the key advances made toward safer, more adaptable, and more efficient collaborative systems.

Finally, among the reviewed works, those that comprehensively address the fundamental technical requirements for collaborative robotics stand out. The analysis of quality standards from a user-centered perspective provides clear guidelines for implementing regulations such as ISO 10218 and ISO/TS 15066 in real-world environments [45]. The taxonomy of control strategies offers a structured framework for designing safe and efficient systems while considering technical criteria such as autonomy, human intervention, and risk management [28]. In turn, the energy-focused approach to integrating redundant robots into confined spaces addresses technical challenges related to physical safety and operational efficiency [41]. Lastly, the review of advances and future perspectives in collaborative robotics synthesizes emerging technological trends and key technical requirements for the development of collaborative systems in modern industry [6].

Despite the interest in converting traditional industrial robots into collaborative robots (cobots), there is a notable lack of research and available frameworks that directly and comprehensively address this transformation. During the literature review conducted for this study, very few articles were found that could even partially serve as a basis for upgrading an industrial robot to operate in a collaborative environment, as they generally did not propose complete frameworks for such a task [83,84,85,86,87,88]. Due to this limitation, the search was expanded to include websites featuring news and technological products, where relevant initiatives such as the European project RobERTO were identified. This project, developed between 2016 and 2018, aimed to create an integrated system capable of transforming any industrial robot into a cobot by equipping it with human–robot interaction capabilities and environmental perception, thus enabling its operation without physical barriers and increasing the flexibility and autonomy of production systems [89,90]. However, no technical documentation or further detailed information was found beyond a general description available on the website of Aimen Technology Center and the project’s partners [91].

In addition to the RobERTO project, AIRSKIN technology was identified as another relevant solution. AIRSKIN is a commercial product designed to turn industrial robots into collaborative ones through pressure-sensitive contact sensors [92]. This modular solution is mounted directly onto the robot, allowing it to detect collisions with a sensitivity of up to 5 Newtons, and is compliant with standards such as ISO/TS 15066, EN/ISO 13849-1, and EN/IEC 62061 [10,93,94]. Unlike other systems that require protected workspaces, AIRSKIN enables the removal of physical barriers such as fences and light curtains, potentially reducing floor space usage by up to 90% and allowing operational speeds up to six times faster than those of traditional cobots [92]. It is worth noting, however, that while this product offers an effective means of adapting certain industrial robots for collaborative use, it does not incorporate other key features that fully define a collaborative robot. The combination of these initiatives suggests that there is an emerging, although limited, effort from industry to enable the transformation of industrial robots into collaborative ones, making this a fertile ground for further research.

3.2. Safety Standards and Regulatory Frameworks

Ensuring human safety is paramount in collaborative robotics. The ISO/TS 15066 technical specification, in conjunction with ISO 10218-1/2 [10,12,13], provides guidelines on risk assessment, permissible force thresholds, and safety-rated monitored stops in collaborative scenarios [82,95]. These standards outline the critical parameters for designing safe interactions between humans and robots, especially when physical barriers are removed. Additionally, the integration of safety-rated sensors, redundant control systems, and real-time monitoring protocols is essential to meet these regulatory demands. A retrofitting framework must include compliance mechanisms that align with these norms to be viable in industrial environments.

Human safety remains a cornerstone in the development and deployment of collaborative robotic systems [82], particularly in industrial settings where robots share the workspace with human operators. Unlike traditional robotic environments, where robots are typically enclosed in physical safety cages to prevent unintended contact, collaborative robotics removes many of these physical barriers, necessitating a more sophisticated and stringent approach to safety [95]. In this context, adherence to international technical standards and regulatory frameworks is not only desirable but essential to ensure the acceptance, feasibility, and safe integration of such systems.

The foundational regulatory framework used in collaborative robotics is built upon the ISO 10218 standards (Parts 1 and 2) and the ISO/TS 15066 technical specification [10,12,13]. ISO 10218-1 outlines the safety requirements for the design of industrial robots, while ISO 10218-2 focuses on robot systems, including aspects of their installation, integration, and operation within work cells [96]. However, the publication of ISO/TS 15066 in 2016 marked a significant milestone by providing a detailed technical guide specifically tailored to safe human–robot collaboration.

ISO/TS 15066 expands on the general principles of ISO 10218 and defines specific requirements for risk assessment, permissible force and pressure thresholds, and acceptable interaction types (such as speed and separation monitoring, hand-guiding, safety-rated monitored stops, and power and force limiting) [96]. Moreover, this specification includes biomechanical data on human pain tolerance, serving as an objective reference for establishing impact limits, which is critical in tasks involving direct contact or tool sharing.

3.3. Enabling Technologies: Sensors, Control, and Monitoring

Compliance with these standards necessitates the use of advanced monitoring and control technologies to ensure fast and reliable responses to potential hazards. These technologies include the following:

• Safety-rated sensors such as laser scanners, stereoscopic cameras, and proximity detectors [22,25,58], which allow for the real-time detection of human operators and enable appropriate safety modes based on proximity.
• Redundant control systems that ensure that a failure in one subsystem does not compromise the overall safety of the system [28].
• Safety-rated monitored stop protocols that guarantee the robot can stop immediately upon detecting an anomaly or the presence of a human within its collaborative zone [49,57].
• Human–machine interfaces (HMIs) adapted for safe operation, allowing for intuitive supervision, diagnostics, and collaborative mode configuration.

These technologies must operate under certified safety control architectures, conforming to standards such as IEC 61508 (the functional safety of electrical/electronic systems) [97] and ISO 13849-1 [93], which defines safety Performance Levels (PLs) for safety-related control functions.

3.3.1. Retrofitting and Compliance Challenges

Implementing collaborative robotic systems through retrofitting—upgrading existing industrial robots to enable collaborative functions—presents particular challenges in terms of regulatory compliance. Simply adding sensors or reprogramming behaviors is insufficient; a holistic compliance framework must be embedded, ensuring that both physical and logical components meet international safety standards.

This includes comprehensive risk assessments, the validation of collaborative operation modes, and extensive documentation that provides traceability for all implemented safety measures. In industrial environments, compatibility with existing infrastructure must not compromise the integrity or safety performance of a system.

3.3.2. Emerging Trends and Future Requirements

Currently, new revisions of these standards are underway to accommodate advancements in artificial intelligence, machine learning, and context-aware perception. These innovations enable more intuitive and seamless human–robot interactions, but they also demand the continuous redefinition of safety boundaries and certification criteria.

Simultaneously, regional and supranational regulatory initiatives (e.g., the EU Machinery Regulation 2023/1230, which replaces Directive 2006/42/EC) are strengthening the requirements for the conformity assessment and legal accountability of collaborative robot manufacturers and integrators. This underscores the importance of integrating compliance mechanisms early in the design and development stages of collaborative systems.

3.4. Industrial Robot Typologies and Their Reusability Potential

Not all industrial robots are suitable for conversion into collaborative robots (cobots). While the increasing demand for flexible and adaptive manufacturing solutions pushes the boundaries of industrial automation, the transition from traditional robotic systems to collaborative configurations requires a careful assessment of the robotic typologies available. These typologies can be classified based on their mechanical structure, control architecture, and the potential for upgrades that enable them to safely and efficiently work alongside humans. Understanding these differences not only guides retrofitting strategies but also aids in determining the long-term reusability of robotic systems in collaborative environments.

3.4.1. Robot Typologies and Their Key Characteristics

In the context of converting traditional industrial robots into collaborative robots, three primary robot typologies emerge as candidates for evaluation: articulated robots, SCARA robots, and delta robots. Each typology exhibits unique features in terms of their mechanical structure, programming flexibility, and integration capabilities that determine their adaptability for collaborative tasks [98,99].

Articulated Robots

Articulated robots are perhaps the most common type of industrial robot, known for their multi-jointed arms that allow for a high degree of freedom and flexibility [100]. These robots typically have six or more degrees of freedom (DOF), enabling them to perform complex movements with precision. The key characteristics of articulated robots include the following:

• Mechanical Structure: These robots feature a series of linked arms, often modeled after a human arm, providing high dexterity and reach. The flexibility of their design allows for the execution of a wide range of tasks, from welding to assembly and packaging.
• Payload Capacity: Articulated robots are typically designed to handle heavy payloads, which makes them suitable for demanding industrial tasks. However, this can also limit their applicability in collaborative settings, as their high payloads may pose risks in environments where humans are in close proximity.
• Control Architecture: These robots are usually controlled by sophisticated systems that provide high precision and fine control over motion. However, many traditional articulated robots operate on proprietary control systems, which can limit their ease of integration with open-source platforms like the ROS (Robot Operating System).
• Upgrade Potential: Articulated robots with modular joints and open control architectures (or the ability to retrofit such systems) are ideal candidates for retrofitting into cobots. Their strong mechanical base, combined with their compatibility with additional sensors and safety systems, facilitates their transformation into robots that can safely interact with humans in shared workspaces.

SCARA Robots

Selective Compliance Assembly (SCARA) robots are characterized by their unique mechanical design, which offers both vertical and horizontal movement. These robots typically have a fixed vertical arm and two horizontal arms that move in a coordinated manner, making them particularly suitable for assembly tasks.

• Mechanical Structure: SCARA robots’ design allows for high-speed operations with a focus on lateral precision, which makes them ideal for tasks like pick-and-place, assembly, and packaging. SCARA robots are more compact than articulated robots, making them suitable for applications where space is limited.
• Payload Capacity: SCARA robots usually have a moderate payload capacity compared to articulated robots. This makes them suitable for light- to medium-duty tasks, which often aligns with collaborative work environments, where the robot needs to handle smaller items or tools.
• Control Architecture: SCARA robots typically feature more standardized control systems compared to articulated robots. This makes them easier to integrate with open software architectures like the ROS 1 or version 2, which is crucial for retrofitting these robots for collaborative operations.
• Upgrade Potential: Due to their relatively simple design and control systems, SCARA robots present a strong opportunity for collaborative conversion, particularly when equipped with additional safety-rated sensors and adaptable software systems. Their ease of programming and integration make them an appealing choice for applications where human–robot interaction is necessary.

Delta Robots

Delta robots, also known as parallel robots, consist of three arms connected to a central platform. This design allows for fast and precise movement, typically in high-speed pick-and-place applications. Delta robots are often used in environments requiring high speed, such as food processing, pharmaceutical assembly, and electronics manufacturing.

• Mechanical Structure: Delta robots are characterized by their unique parallel kinematic design, which allows them to achieve high-speed, high-precision movements within limited space. Their structure offers a low payload capacity but excels in speed and agility [100].
• Payload Capacity: Due to their lightweight design, delta robots typically handle smaller payloads, making them suitable for collaborative environments where robots need to interact with delicate or lightweight objects.
• Control Architecture: Delta robots tend to be controlled by highly specialized software, and many are integrated into closed-loop control systems that optimize speed and accuracy. While the closed nature of their systems can pose challenges, there is potential for retrofitting these robots with open-source controllers and adding collaborative capabilities.
• Upgrade Potential: The small payload and speed advantages of delta robots make them ideal for environments where speed and precision are critical, and where human–robot interactions do not involve heavy lifting. Their modular control systems and ability to incorporate safety sensors and cameras make them promising candidates for reuse in collaborative settings.

3.4.2. Key Attributes for Collaborative Transformation

The conversion of industrial robots into cobots is not solely dependent on their mechanical structure, but also on their software and control architecture. The following attributes are essential in determining a robot’s potential for collaborative transformation [98,99]:

• Modular Joints and Flexible Architecture: Robots with modular joints and adaptable designs are ideal for retrofitting. These systems allow for the easier integration of the additional sensors, safety mechanisms, and end effectors needed for safe and efficient human–robot collaboration. Articulated robots with modular arm joints, for instance, can be outfitted with force sensors, proximity sensors, and cameras to enhance their interaction with humans [99].
• Accessible Programming Interfaces: Robots that offer accessible programming interfaces, such as compatibility with the Robot Operating System (ROS) version 1 or 2, allow for easier integration with third-party software, increasing their adaptability in collaborative settings. Open-source programming environments enable more flexibility in developing customized control algorithms for safe human-robot interactions [101].
• Scalable Sensor Inputs: The integration of scalable sensor inputs plays a crucial role in making robots more adaptable to varying environments [102]. By equipping robots with a range of safety-rated sensors (e.g., vision systems, force/torque sensors, LIDAR), their system can dynamically adjust their behavior based on their proximity to humans or objects in the workspace.

3.4.3. Reusability and Retrofitting Strategies

Understanding the mechanical structure and control architecture of existing industrial robots enables more targeted and effective retrofitting strategies. Retrofitting involves not only physical upgrades, such as adding sensors or adjusting the robot’s mobility, but also enhancing the robot’s software to ensure seamless integration with collaborative work environments [83]. Robots with an open and scalable architecture allow for easier an adaptation to meet safety standards and human interaction requirements.

In practice, retrofitting strategies typically include the addition of force-limiting systems, real-time monitoring systems, and safety-rated sensors (e.g., collision detection sensors) to ensure that the robot can safely interact with humans. These upgrades make robots safer, more adaptable, and more efficient in collaborative settings, thereby increasing their reusability across different industries.

3.4.4. Supporting Scalable Deployment

By classifying industrial robots based on their mechanical structure, control architecture, and upgrade potential [100], it is possible to develop targeted retrofitting strategies that support the scalable deployment of collaborative robots. Identifying robots with strong modularity, accessible programming interfaces, and flexible sensor inputs is crucial for making informed decisions about which robots can be upgraded into cobots. This understanding not only enhances our ability to retrofit existing robotic systems but also ensures that these systems can be efficiently deployed across a wide range of industries, fostering the greater adoption of collaborative robotics.

3.5. Sustainable Engineering and Circular Economy Principles

In line with global sustainability goals, robotic retrofitting must adhere to the principles of the circular economy. This includes strategies such as remanufacturing, refurbishment, and lifecycle extension. Key frameworks like the lifecycle assessment (LCA) are considered to quantify the environmental impacts of this transformation. Emphasis is placed on modular hardware upgrades, the reuse of existing electromechanical components, and the reduction of e-waste. Sustainable retrofitting not only lowers environmental footprints but also improves the return on investment for small and medium-sized enterprises (SMEs) [8,9,103], making collaborative robotics more accessible and equitable.

Sustainable engineering and the principles of the circular economy are becoming increasingly critical in industrial settings, especially as industries strive to reduce their environmental footprint while maintaining economic growth [8,104]. The integration of these principles in the context of robotics, particularly in collaborative robotic systems, offers a significant opportunity to align technological innovation with environmental stewardship [105]. By adhering to sustainable practices, robotics can contribute to a more efficient, responsible, and environmentally-conscious future for manufacturing industries.

3.5.1. Sustainable Engineering in Robotics

Sustainable engineering involves the development and deployment of technologies and systems that are designed to minimize their environmental impact throughout their entire lifecycle, from their design and manufacturing to their operation, maintenance, and decommissioning. In the case of robotics, sustainable engineering aims to create robots that are energy-efficient, resource-conserving, and capable of reducing waste during their production and use.

Energy Efficiency in Robotic Systems

A key focus of sustainable engineering is improving energy efficiency, which reduces both operational costs and environmental impact. Robotics systems are often energy-intensive, especially when they involve high-power motors or complex algorithms, which require considerable computational power. Optimizing energy consumption in collaborative robots (cobots) is crucial, especially when these cobots operate in environments where they are active over long periods. Strategies to achieve energy efficiency in robotic systems include the following:

• Low-power actuators and motors: Using energy-efficient actuators that consume less power while maintaining performance is essential for reducing energy consumption in robotic systems. For example, hybrid electric actuators and energy-harvesting technologies, such as piezoelectric devices, could be explored.
• Energy-efficient control algorithms: The design of control algorithms that allow robots to perform tasks efficiently, without excess energy usage, is a fundamental aspect of sustainable engineering. This includes optimizing robot movements to minimize unnecessary motion, using machine learning to predict energy demands, and adjusting robot speeds [106] and workloads based on real-time feedback.
• Regenerative energy systems: In certain robotic systems, such as those used for pick-and-place tasks, regenerative braking systems can capture energy during deceleration and store it for future use, improving the system’s overall energy efficiency.

Material Efficiency and Sustainable Manufacturing

Sustainable engineering also emphasizes the efficient use of materials during the manufacturing process. This can be achieved through the design of robots that use fewer resources, incorporate recyclable materials, and are produced through sustainable manufacturing processes. Key considerations include the following:

• Material selection: The choice of materials for manufacturing robots is crucial. Lightweight materials, such as advanced composites and aluminum alloys, not only improve the robot’s performance by reducing its energy consumption but also minimize material usage. Moreover, recyclable materials can help reduce the ecological impact of robots once they reach the end of their lifecycle.
• Additive manufacturing (3D printing): Three-dimensional printing technologies are becoming a key enabler of sustainable manufacturing in robotics. By using additive manufacturing, it is possible to create complex, lightweight structures that require less material than traditional manufacturing methods [107]. This can significantly reduce waste and energy consumption during the production process.
• Modular design: Designing robots with modular components allows for the easy replacement of damaged or outdated parts without the need to discard the entire robot. This reduces waste and supports sustainability by enabling the reuse of parts and materials, contributing to a more circular approach.

3.5.2. Circular Economy Principles in Robotics

The concept of a circular economy focuses on minimizing waste and making the most of the resources available. It is a shift away from the traditional linear economy model, which typically follows a “take, make, dispose” approach, and instead advocates for the continuous reuse and regeneration of products and materials. The principles of the circular economy are highly relevant to robotics, particularly when considering the lifecycle of robots, their components, and the possibility of extending their use.

Designing for Longevity and Reusability

In a circular economy, designing products to last longer and be reusable is key. For robots, this translates to creating machines that are durable, modular, and upgradeable, so they can be maintained and reused over time rather than being discarded after a set period:

• Durable and modular construction: A focus on building robots that can withstand long-term use while minimizing the need for repairs or replacements is crucial. This may involve using high-quality materials, such as durable metals or advanced polymers, that extend the lifespan of robotic systems.
• Upgradability and refurbishment: Instead of designing robots with a fixed lifecycle, manufacturers are beginning to consider how robots can be upgraded or refurbished. By enabling the reuse of core components such as sensors, processors, and actuators, robots can evolve with changing technologies, extending their functional life and reducing the need for new, resource-intensive manufacturing.

Recycling and Recovery of Robot Components

Another crucial aspect of the circular economy is the recovery and recycling of materials and components at the end of a product’s lifecycle. Robots, like any other high-tech machinery, contain materials and components that can often be repurposed, recycled, or refurbished rather than disposed of. By establishing robust recycling processes, robotics can contribute to reducing e-waste and promoting the reuse of precious metals and components.

• Recycling robot parts: When robots reach the end of their lifecycle, it is important to ensure that valuable materials, such as metals, plastics, and rare earth elements, are recovered and reused. For example, motors, sensors, and circuit boards can often be recycled or refurbished for use in new robots.
• Automated disassembly: Using disassembly-friendly design principles can make it easier to recover and recycle robot components. Automated disassembly processes, potentially utilizing collaborative robots, could facilitate the sorting of materials and components for recycling, which is in line with the principles of the circular economy.

3.5.3. Environmental Impact Reduction Through Robotics

In addition to improving their own sustainability, robotics can contribute to environmental sustainability by enabling more sustainable manufacturing processes in other industries [108]. Cobots, in particular, can play a key role in this area by facilitating precision manufacturing, reducing material waste, and enabling more efficient production lines:

• Precision manufacturing: Cobots can improve the precision of tasks such as assembly, packaging, and material handling, reducing the amount of waste generated during production processes. By using cobots for tasks like waste sorting or waste reduction, manufacturers can optimize resource usage and reduce their environmental impact.
• Sustainability in production: Cobots can help monitor energy use, optimize workflows, and even adjust production speeds to reduce energy consumption. By working alongside human operators, cobots can assist in identifying inefficiencies in production systems, ultimately contributing to greener manufacturing practices.

3.5.4. Embracing Sustainability in Robotics

Sustainable engineering and circular economy principles are central to the future of robotics, especially as industries look to minimize environmental impacts while maximizing efficiency and performance. By focusing on energy-efficient designs, modularity, material reuse, and recycling, the robotics industry can contribute significantly to a more sustainable and circular economy. The integration of these principles in collaborative robotic systems in particular offers an exciting opportunity to transform manufacturing processes while ensuring they align with global sustainability goals. Moving forward, it will be crucial for researchers, manufacturers, and policymakers to collaborate to advance these principles to build a more sustainable future for both robotics and industry as a whole.

3.6. Technological Enablers: AI, Vision, and Control

Recent advancements in artificial intelligence, machine learning, and computer vision have expanded the possibilities of robot retrofitting. Technologies such as deep neural networks, reinforcement learning, and 3D object recognition enable older robot platforms to gain contextual awareness, learn adaptive behaviors, and operate safely in dynamic environments.

These capabilities are essential for enabling real-time decision-making and intelligent task allocation, key traits of next-generation cobots. The integration of such technologies is guided by best practices in software engineering [109], sensor fusion, and ethical AI.

4. Conceptual Design of a Modular Conversion Framework

The growing trend toward flexible, human-centric production environments demands the re-evaluation of legacy robotic assets [110]. The conceptual design of a modular conversion framework addresses this need by proposing a structured methodology by which to transform traditional industrial robots into collaborative systems. This approach not only extends the lifespan of existing robotic infrastructure but also aligns with the Industry 5.0 principles of personalization, human–machine synergy, and sustainability.

4.1. Comparative Analysis of Traditional Industrial Robots and Commercial Cobots

This section presents a comparative analysis aimed at clearly contextualizing the purpose of the retrofit strategy proposed in this study. To this end, we contrast the capabilities and limitations of a traditional industrial robot such as the ABB IRB 120 and IRB 1600 (ABB Ltd., Zürich, Switzerland) with those of widely adopted collaborative robots, including the UR5e from Universal Robots (Odense, Denmark), the KUKA LBR iiwa 7 R800 (KUKA AG, Augsburg, Germany), and the ABB YuMi (ABB Ltd., Zürich, Switzerland).

This comparison, presented in

Table 2. Comparative analysis of traditional industrial robots and commercial collaborative robots (cobots).

Criterion	Traditional Industrial Robot (e.g., ABB IRB 120)	Commercial Cobot (UR5e/LBR iiwa/YuMi)
Payload capacity	3–10 kg	3–10 kg
Repeatability	High (±0.01 mm)	High (±0.03–0.05 mm)
Collaboration without fencing	No (requires fencing or external scanners)	Yes (designed and certified under ISO/TS 15066)
Integrated torque sensors	No	Yes (in each joint)
Speed and acceleration	High (up to 6 m/s, no human contact)	Limited by contact regulation (≈1.5 m/s)
Safe physical design	No (sharp edges, no soft covers)	Yes (ergonomic design, rounded surfaces)
Environmental perception	None or limited (requires external vision)	Partial or integrated, depending on model
Collaborative programming	Not native (requires external PLCs/ROS)	Yes, via intuitive APIs and HMI panels
Integration curve	High complexity	Low, plug-and-play interface
Estimated total cost	Low (if robot is already owned)	High (USD 30,000–60,000)
Aftermarket flexibility/scalability	Limited	High, compatible with AI, vision, cloud modules

, highlights the functional gaps that the proposed layered strategy seeks to bridge, enabling existing industrial robots to progressively acquire collaborative capabilities through modular upgrades.

Current cobots offer an integrated package of collaboration, safety, and agile integration, albeit at a higher acquisition cost. In contrast, traditional industrial robots provide superior mechanical performance and speed but lack inherent collaborative capabilities.

The methodology proposed in this study aims to close this functional gap by enabling existing industrial robots, through a layered, modular approach, to progressively acquire the perception, safety, and interaction features that characterize modern cobots. This retrofit strategy is a viable alternative in contexts where capital constraints, cost considerations, or sustainability goals prevent the direct replacement of equipment.

4.2. Layered Modular Architecture and Design Paradigm

Traditional industrial robots were designed for speed, precision, and repeatability in isolated environments and typically enclosed within physical barriers. However, emerging paradigms in smart manufacturing require these robots to operate safely and adaptively in workspaces shared with human operators. Purchasing new collaborative robots (cobots) can be economically unfeasible for many enterprises, particularly SMEs. Therefore, a conversion strategy based on modular retrofitting is both a practical and strategic solution.

The design paradigm of this framework is built upon four foundational axes:

• Interoperability: The framework must accommodate robots from different manufacturers, with heterogeneous hardware interfaces and proprietary control architectures.
• Safety-Centric Engineering: All modules must inherently support safety-by-design, incorporating features for risk mitigation [111] and real-time monitoring.
• Rapid Deployment: Plug-and-play capabilities, auto-configuration routines, and minimal downtime are essential to reduce operational disruptions.
• Lifecycle-Oriented Modularity: The framework is designed to adapt not only existing robots but also future upgrades, supporting circular economy principles.

The proposed conversion framework is structured as a layered modular architecture, where each layer addresses a distinct functional domain. This design enhances its abstraction, scalability, and fault tolerance, ensuring that individual components can evolve or fail independently without compromising system integrity. The general scheme on which the proposed conversion framework is structured is shown in Figure 2. This figure illustrates the relationships betwen the key components of the system: the production process, the industrial robot, the system users (including operators and collaborators), and peripheral devices. It also presents examples of possible communication interfaces between the hardware and the modules of the framework, which are responsible for executing specific operations on these devices. This schematic aims to provide a comprehensive view of the system’s architecture, emphasizing the flows of information and control among the various components involved in the industrial environment.

4.2.1. Perceptual and Context-Aware Layer

At the foundation lies the perceptual layer, which provides the system with environmental awareness. It uses advanced sensor fusion techniques that combine data from 2D/3D cameras, LiDARs, force/torque sensors, and thermal or ultrasonic sources. This sensory input enables capabilities such as real-time depth-aware human–robot distance estimation and workspace mapping through Visual SLAM. Algorithms like YOLO and Mask R-CNN facilitate human posture recognition, trajectory prediction, and proactive risk assessments [111]. To ensure safety and reliability, sensor redundancy is incorporated for the cross-validation of critical inputs.

4.2.2. Safety Control and the Middleware Abstraction Layer

This layer is responsible for maintaining system safety and managing internal communication. A real-time safety kernel enforces dynamic safety envelopes and fallback protocols, reacting to emerging hazards in milliseconds. Middleware solutions, such as ROS2 or DDS, support seamless inter-process communication, while certified safety PLCs and FSoE modules manage the deterministic execution of safety routines. Redundancy strategies, both in hardware and software, are implemented to ensure fail-operational behavior under fault conditions.

4.2.3. Human–Machine Interaction Layer

Interaction with human operators is governed by this layer, which is designed to be intuitive and flexible. It supports multimodal input methods including gesture control, voice commands, and touchscreen interfaces. Augmented and virtual reality (AR/VR) tools enable immersive telepresence and remote operation, while visual and haptic feedback mechanisms enhance situational awareness and usability during human–robot collaboration.

4.2.4. Cognitive and Adaptive Behavior Layer

This layer equips the robot with learning and adaptive decision-making capabilities. Machine learning modules enable the robot to generalize behaviors from demonstrations using imitation or reinforcement learning techniques [112]. Decision-making under uncertainty is supported through probabilistic reasoning frameworks such as Bayesian inference, allowing the system to dynamically adjust its behavior to human intent or unforeseen environmental changes.

4.2.5. Mechanical and Structural Adaptability

On the hardware side, the framework supports physical retrofitting without compromising the robot’s core structure. Customizable end-effector mounts and brackets provide flexibility in tool integration, while magnetic docking systems simplify sensor attachment. Modular protective shells offer passive impact absorption, contributing to safety in collaborative environments.

4.2.6. Deployment Toolkit and Configuration Management

To streamline the deployment process, the framework includes pre-calibrated templates and reusable software libraries. Parameterized setup wizards simplify the configuration of safety zones and task routines [84]. Simulation environments, such as Gazebo or Unity, support digital twin validation prior to physical deployment. A centralized configuration manager oversees diagnostics, over-the-air (OTA) updates, and operational logging to maintain system health and traceability.

4.2.7. Integration with Industrial Ecosystems

The framework is designed for compatibility with modern Cyber–Physical Systems [113] (CPS) and Industrial Internet of Things (IIoT) environments. It supports edge computing with embedded AI accelerators like NVIDIA Jetson or Intel Movidius and enables cloud-based predictive analytics. Native integration with OPC-UA and Profinet ensures its seamless connectivity with PLCs, MES systems, and other components of the industrial automation stack, allowing coordinated operation across distributed facilities.

5. Evaluation Steps and Benchmarking Criteria

This section builds upon the theoretical evaluation criteria outlined previously, providing a practical methodology to guide future empirical validations of the proposed modular conversion framework. Although the framework remains at a conceptual stage, the evaluation criteria identified are crucial to ensure its comprehensive assessment.

Among these criteria is the cobot’s safety compliance potential, which must align with international standards such as ISO/TS 15066 to guarantee operator protection during implementation. Another important factor is the feasibility of retrofitting across different industrial robot types, taking into account the heterogeneity and technical constraints typical of legacy systems.

Additionally, the framework emphasizes alignment with circular economy principles, promoting sustainability through lifecycle extension and efficient resource use. Scalability and modularity are also prioritized, allowing incremental upgrades that can be adapted to diverse industrial environments, including small and medium-sized enterprises (SMEs).

Economic and environmental benefits are considered as well, with both the cost-effectiveness of retrofitting and its potential impact on resource consumption evaluated [114,115]. Finally, readiness for integrating artificial intelligence capabilities is recognized as a key element for enhancing autonomy and adaptability in future stages of implementation.

To operationalize these criteria, a six-step evaluation process is proposed:

• Step 1: Apply a checklist based on applicable redesign requirements. Review and analyze the necessary requirements [116,117,118] to convert the industrial robot into a collaborative one. The checklist should cover economic, environmental, cost-effectiveness, technical, safety, and operational aspects to ensure the robot’s feasibility to be upgraded to the performance expectations of the collaborative system.
• Step 2: Define the robot’s usage requirements, including production process modifications. Determine how the collaborative robot should be integrated into the production process. It is essential to identify the tasks it will perform, the required changes in the process, and the resulting impact on the production line.
• Step 3: Assess criteria for implementing modifications. Verify the availability of the necessary modules to perform the required changes and evaluate the technical capabilities of operators to implement them. It is important to determine whether adequate resources are available or if additional investments will be needed.
• Step 4: Estimate the development costs of the required components. Calculate the costs associated with the components and adaptations needed to convert the industrial robot into a collaborative system. This includes hardware, software, and any modifications to the workspace’s infrastructure.
• Step 5: Quote the hypothetical purchase and installation of an equivalent collaborative robot. Obtain a reference quotation for a collaborative robot with similar specifications, including the cost of the equipment, its installation, and any necessary workspace adjustments. This will serve as a baseline for comparing both alternatives.
• Step 6: Perform a comparative analysis between modifying the existing industrial robot and purchasing a new collaborative one. Key factors such as reliability, implementation time, costs, and risks must be considered [111]. This evaluation will support informed decision-making that is aligned with the project’s objectives and the available resources.

As an additional metric to support managerial decision-making regarding the feasibility of implementing an industrial robot as a collaborative one on a production line, we propose the estimation of a set of key factors [83], which are presented in

Table 3. Evaluation metrics for the modular conversion framework.

Category	Metric	Description
Safety	Risk of accident	Combination of likelihood and severity used to estimate potential harm.
Quality Attributes	Reliability and performance	Consistency and efficiency in task execution.
Flexibility	Reconfiguration time	Time required to switch between different operational modes.
Usability	Operator learning curve	Time needed for a non-expert to operate the robot safely.
ROI	Cost–benefit ratio	Comparison between retrofit cost and new cobot acquisition.

. These factors allow for a more comprehensive assessment that goes beyond a purely technical or financial analysis, incorporating aspects related to operational efficiency, system safety, flexibility, and the overall impact of productivity. Furthermore, it is important to note that these same factors can later be reused as key performance indicators (KPIs), enabling the establishment of a baseline for ongoing monitoring of the robot’s performance after modification. In this way, this approach not only supports more informed decision-making in the initial stages, but also facilitates a strategy for continuous improvement based on measurable and objective data.

6. Discussion

The proposed modular retrofitting framework for transforming industrial robots into collaborative systems, presented as a sustainable and adaptive alternative, offers a theoretically sound response to industrial pressure to integrate emerging technologies without incurring the high economic and environmental costs of replacing equipment fully. From a doctoral research perspective, this approach stands out for its transdisciplinary focus, combining principles from mechanical engineering, artificial intelligence, computer vision, and the circular economy.

One of the key contributions of this work lies in the explicit recognition of the challenges posed by applying safety standards (e.g., ISO/TS 15066) to systems originally designed for isolated operation. The introduction of specific architectural layers for contextual perception, safety control, and multimodal human–machine interactions reflects a conceptual maturity aligned with Industry 5.0 principles, particularly in its emphasis on human-centric design.

However, in the absence of empirical validation, it is important to emphasize that this study remains at a preliminary research stage, grounded in a systematic review of the state of the art. Accordingly, the proposed strategy should be interpreted as a conceptual hypothesis not yet empirically validated, and its real-world applicability is conditional upon a range of technical, economic, and operational factors discussed in earlier sections.

From an advanced research perspective, this demands critical reflection on the methodological risks associated with translating theoretical design into practical deployment. These risks include, but are not limited to, the following:

• The gap between conceptual design and operational reality: While simulations and conceptual frameworks offer clarity and control, real-world production environments introduce variability, regulatory constraints, and human factors that may challenge the model’s assumptions.
• The technical limitations of legacy hardware: Older robotic platforms differ significantly in their architecture, wear level, and integration capacity, which may affect both the feasibility and cost-effectiveness of retrofitting as discussed.
• The complexities of human–robot interaction: Safety, perception, and real-time adaptability in unstructured environments require rigorous empirical testing, especially under international safety standards (e.g., ISO/TS 15066), which are beyond the scope of this initial theoretical phase.

Addressing these limitations will require subsequent phases of empirical validation, including prototype development, simulated and real-world testing in industrial contexts, and economic benchmarking. Only through such a research agenda can the theoretical contributions outlined here be translated into robust, operationally viable solutions.

Additionally, the framework does not yet sufficiently address the ethical dilemmas associated with AI use in collaborative settings, such as decision traceability, operator privacy, or algorithmic bias in perception systems, which will be crucial for its future adoption.

In summary, this is a visionary proposal that requires future empirical validation, use-case-driven assessment, and the integration of sociotechnical dimensions to ensure its applicability in complex and hybrid industrial contexts.

7. Conclusions

This study presents a robust theoretical proposal for transforming legacy industrial robots into sustainable, safe, and intelligent cobots through a modular and adaptable architecture. Grounded in the principles of sustainable engineering and the circular economy, it offers a viable alternative to the technology obsolescence brought on by the rapid evolution of industrial automation.

From an advanced academic perspective, the main contribution of this study lies in the integration of diverse disciplines to offer a holistic solution to the structural and functional rigidity of legacy robotic systems. The inclusion of a guiding taxonomy and evaluation criteria represents a valuable methodological addition that can steer future implementation efforts.

Nevertheless, to move toward real industrial impact, it will be essential to validate this framework in controlled scenarios, embed ethical assurance and algorithmic accountability mechanisms, and consider the framework’s adaptability to regions with varying levels of technological maturity and industrial infrastructure.

Looking ahead, this work lays the foundation for an applied research agenda focused on democratizing access to collaborative robotics and enhancing the resilience of industrial systems in the face of sustainability, technological inclusion, and the transition toward Industry 5.0.

8. Future Research Directions

Building on the conceptual framework presented in this study and in response to reviewer feedback, several future research directions are identified for transitioning this framework from theoretical design to practical, industry-ready solutions. These directions are essential for validating, refining, and expanding the contributions of this work to both academic and industrial domains:

Empirical Validation and Pilot Implementation: The next logical step is to empirically validate the proposed modular retrofitting framework through prototype development and controlled industrial pilot studies. These implementations should measure key performance indicators such as safety, adaptability, energy efficiency, and user acceptance, allowing for the refinement of both the hardware and software components under real operating conditions.

Integration of Advanced Artificial Intelligence: Research should further explore the integration of advanced AI methods into this framework, including reinforcement learning, robotic foundation models, and continual learning algorithms. These technologies can significantly enhance cobot autonomy, self-adaptation, and responsiveness to dynamic environments and human behavior, thereby improving their functionality in high-variability manufacturing contexts.

Expanded Perception and Sensing Capabilities: Future work must address the need for richer and more reliable sensory inputs, leveraging sensor fusion techniques that integrate depth vision, tactile sensing, force feedback, and contextual awareness. Emerging technologies such as flexible smart skins and AI-generated materials hold promise for enhancing cobot situational understanding and compliance with stringent safety standards.

Lifecycle Assessments and Sustainability: It is essential to conduct comprehensive lifecycle assessments (LCAs) comparing retrofitting versus the full replacement of industrial robots. This includes quantifying the environmental benefits (e.g., carbon footprint, material reuse) and economic metrics (e.g., cost savings, ROI) to substantiate the framework’s alignment with circular economy principles and sustainable manufacturing goals.

Compliance with Regulatory and Ethical Standards: As cobots become increasingly intelligent and autonomous, research must focus on ensuring regulatory compliance (e.g., ISO/TS 15066, ISO 10218-1/2) and developing ethical assurance frameworks. Topics such as algorithmic transparency, operator data privacy, and the mitigation of AI biases in decision-making will be vital to securing industry trust and adoption.

Human Factors and Sociotechnical Integration: Future studies should explore how human operators interact with retrofitted cobots, focusing on usability, training requirements, and long-term adaptation. Multimodal human–machine interfaces (HMIs), including AR/VR, gesture recognition, and voice control, should be developed and tested for their effectiveness in enhancing collaboration and operator well-being.

Scalability and SME-Oriented Deployment: Research should examine scalable strategies for modular toolkit development that facilitate its adoption by small and medium-sized enterprises (SMEs). This includes the creation of digital twins, cloud-based deployment and monitoring platforms, and standardized retrofitting kits that reduce configuration complexity and cost barriers.

Integration with Industrial Ecosystems: Finally, future work should investigate how retrofitted cobots can be seamlessly integrated into larger industrial ecosystems involving the IoT, cloud computing, digital twins, and edge AI. This cross-disciplinary approach will allow for coordinated operations, real-time data sharing, and predictive maintenance across distributed production environments.