Transforming Robots into Cobots: A Sustainable Approach to Industrial Automation
Abstract
:1. Introduction
2. Methodology
2.1. Analytical Review of the State of the Art
- 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
- 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.
2.3. Theoretical Impact and Framework Evaluation Criteria
- 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.
3. An Analytical Review of the State of the Art
3.1. Conversion of Industrial Robots into Cobots
- 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.
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 | - | - |
3.2. Safety Standards and Regulatory Frameworks
3.3. Enabling Technologies: Sensors, Control, and Monitoring
- Redundant control systems that ensure that a failure in one subsystem does not compromise the overall safety of the system [28].
- Human–machine interfaces (HMIs) adapted for safe operation, allowing for intuitive supervision, diagnostics, and collaborative mode configuration.
3.3.1. Retrofitting and Compliance Challenges
3.3.2. Emerging Trends and Future Requirements
3.4. Industrial Robot Typologies and Their Reusability Potential
3.4.1. Robot Typologies and Their Key Characteristics
Articulated Robots
- 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
- 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
- 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
- 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
3.4.4. Supporting Scalable Deployment
3.5. Sustainable Engineering and Circular Economy Principles
3.5.1. Sustainable Engineering in Robotics
Energy Efficiency in Robotic Systems
- 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
- 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
Designing for Longevity and Reusability
- 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
- 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
- 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
3.6. Technological Enablers: AI, Vision, and Control
4. Conceptual Design of a Modular Conversion Framework
4.1. Comparative Analysis of Traditional Industrial Robots and Commercial Cobots
4.2. Layered Modular Architecture and Design Paradigm
- 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.
4.2.1. Perceptual and Context-Aware Layer
4.2.2. Safety Control and the Middleware Abstraction Layer
4.2.3. Human–Machine Interaction Layer
4.2.4. Cognitive and Adaptive Behavior Layer
4.2.5. Mechanical and Structural Adaptability
4.2.6. Deployment Toolkit and Configuration Management
4.2.7. Integration with Industrial Ecosystems
5. Evaluation Steps and Benchmarking Criteria
- 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.
6. Discussion
- 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.
7. Conclusions
8. Future Research Directions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
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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 |
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. |
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Fernandez-Vega, M.; Alfaro-Viquez, D.; Zamora-Hernandez, M.; Garcia-Rodriguez, J.; Azorin-Lopez, J. Transforming Robots into Cobots: A Sustainable Approach to Industrial Automation. Electronics 2025, 14, 2275. https://doi.org/10.3390/electronics14112275
Fernandez-Vega M, Alfaro-Viquez D, Zamora-Hernandez M, Garcia-Rodriguez J, Azorin-Lopez J. Transforming Robots into Cobots: A Sustainable Approach to Industrial Automation. Electronics. 2025; 14(11):2275. https://doi.org/10.3390/electronics14112275
Chicago/Turabian StyleFernandez-Vega, Michael, David Alfaro-Viquez, Mauricio Zamora-Hernandez, Jose Garcia-Rodriguez, and Jorge Azorin-Lopez. 2025. "Transforming Robots into Cobots: A Sustainable Approach to Industrial Automation" Electronics 14, no. 11: 2275. https://doi.org/10.3390/electronics14112275
APA StyleFernandez-Vega, M., Alfaro-Viquez, D., Zamora-Hernandez, M., Garcia-Rodriguez, J., & Azorin-Lopez, J. (2025). Transforming Robots into Cobots: A Sustainable Approach to Industrial Automation. Electronics, 14(11), 2275. https://doi.org/10.3390/electronics14112275