Cloud/VPN-Based Remote Control of a Modular Production System Assisted by a Mobile Cyber–Physical Robotic System—Digital Twin Approach
Abstract
:1. Introduction
- Processing: WP is processed sequentially at stations such as buffering, handling, processing, and sorting/storage.
- Reprocessing: WP failing the Primary Quality Test (PQT) at the sorting and storage WS is transported back by the MCPRS to the buffer WS. This WP undergoes a second cycle of processing to meet quality standards.
- Scrapping: WP failing the Secondary Quality Test (SQT) at the handling WS is classified as scrap. MCPRS ensures this defective WP is segregated for disposal, maintaining production flow efficiency.
- Remote Accessibility: Cloud/VPN-based interfaces provide real-time monitoring and control. The user can observe, plan, and adjust tasks remotely, ensuring flexible and scalable production The user interacts visually with production systems via AR-enhanced Human–Machine Interfaces (HMIs). AR facilitates the real-time visualization of robotic and WS operations.
- HMI-MPS: Dedicated to monitoring and controlling the MPS.
- HMI-MCPRS: Focused on managing MCPRS operations.
- Section 2 introduces the hardware architecture of the MPS assisted by MCPRS, covering its multilevel monitoring and control system based on IoT edge devices, Profibus, LAN, WAN networks, cloud, and VPN connections, along with key operational assumptions.
- Section 3 focuses on the digital twin (DT) representation of the MPS and MCPRS, including task planning using augmented reality (AR) and Synchronized Hybrid Petri Nets (SHPN) models. The formalism and virtual reality (VR) simulation of P/R/S processes and MCPRS movements are also discussed.
- Section 4 details the SCADA system, HMI synchronization, cloud and VPN integration, and the framework for real-time control.
- Section 5 is a discussion that offers insights into the approach’s effectiveness, user experience, cybersecurity, review of the literature in the field, and two other two approaches for comparison.
- Section 6 provides a review of the DT literature using PRISMA and the Systematic Literature Review (SLR).
- Section 7 presents the conclusions, summarizing key contributions and final remarks.
2. Hardware Architecture of MPS with MCPRS Assistance
2.1. Main Devices and Functionalites
- Coordinate and control the overall production flow across the four WSs.
- Control communication between the MPS’s WSs (buffer, handling, processing, and sorting/storage) and MCPRS.
- Control task synchronization between MPS operation and MCPRS.
- Control interfaces with remote systems over OPC-UA, WAN Ethernet, and other networks for cloud-based SCADA and HMI control.
- Be responsible for the operations within its respective WS.
- Communicate with the master S7-1200 PLC via Profibus, which facilitates synchronized, real-time control of individual WS’s tasks.
- Send the status, updates, and task completion to the master PLC, allowing centralized decision-making.
2.2. IoT Edge Devices, Profibus, Profinet, LAN Ethernet, WAN Ethernet, and Networking
2.3. Profibus, LAN Profinet, LAN Ethernet, and WAN Ethernet Communication Networks
2.4. Multilevel Architecture of MPS Assisted by MCPRS
- Cloud/VPN remote operation level. This level includes SCADA and two HMI systems (HMI-MPS and HMI-MCPRS) for both the MPS 200 and the MCPRS. Through a secure VPN connection, users can monitor and control the entire system remotely, accessing real-time data, triggering commands, or troubleshooting any issues. Node-RED enhances this level by providing a customizable dashboard for AR-based task planning, allowing operators to visualize workflows and system performance remotely.
- Local operation level and task flow. On-site task planning is performed using a chart flow system that coordinates and manages processes. Local SCADA/HMIs allow the user at the site to monitor and interact with the system via local interfaces, HMI-MPS and HMI-MCPRS, making it easy to adjust tasks and oversee processes in real time.
- The communication-level and embedded computer, Jetson Nano, acts as an on-board computer for real-time data processing and vision processing for the MVSS, and controls the robotic tasks of the MCPRS. The TP-Link C80 router handles communication within the local network and connects to external networks for broader data exchange. The SIEMENS-IoT 2050 gateway facilitates communication between industrial devices (PLCs, sensors, and actuators) and the cloud, enabling seamless integration with cloud-based services and remote control.
- Control level and Siemens S7-1214 PLC as the main control unit. This is the primary control unit for the entire system. It manages the operation of the MPS 200 and communicates with the other PLCs and devices. The CM 1242 Profibus DP adapter connects the S7-1200 PLC to the distributed S7-300 PLCs across the four workstations via the Profibus DP protocol, enabling fast and reliable control across the network. The switch manages Ethernet traffic between devices in the control network. Four Siemens S7-300 PLCs: Each workstation (buffer, handling, processing, and sorting/storage) is controlled by a dedicated Siemens S7-300 PLC, providing precise control over the respective task processes.
- Process level, MPS 200 WSs, and MCPRS. Each of the four workstations (buffer, handling, processing, and sorting/storage) handles a specific phase in the production line, with the S7-300 PLCs coordinating the operations at each station.
- Silver WP is assumed to pass the PQT and is stored on the middle or bottom shelf of the storage station (WS4).
- Red or black WP is assumed to fail the PQT and are placed on the top shelf of WS4, where they are retrieved by the MCPRS (as shown in Figure 4).
- Red WP is considered suitable for reprocessing after passing the SQT.
- Black WP is assumed to fail the SQT and is classified as rejected, being stored in the handling station.
2.5. Assumptions for P/R/S Operations on MPS
2.6. Assumptions for MCPRS-Assisted P/R/S Operations
3. The Virtual World as a Digital Counterpart of P/R/S Technology on MPS Assisted by MCPRS
3.1. AR and VR as Components of the Virtual World
- VR with SHPN to model P/R/S workflow by using SHPN simulation and the Sirphyco package [29].
3.2. Workflow, Task Planning, Synchronization, and SHPN Structure
- Discrete elements for modeling MPS operations: TPN_1 for processing, TPN_2 for reprocessing, and TPN_3 for scrapping.
- Continuous elements for MCPRS movements: CPN_1 for forward motion and CPN_2 for backward motion.
3.3. SHPN Together with TPN and CPN Models, Formalism, and Simulation
- Forward Displacement (Blue): Moving from WS4 to WS1;
- Backward Displacement (Red): Returning from WS1 to WS4, .
3.4. Virtual Digital Counterpart of the MCPRS
4. Real World of P/R/S on MPS Assisted by MCPRS
4.1. SCADA Monitoring Signals and Syncronization
- (1)
- Data acquisition, to monitor and control all I/O field sensors in the MPS and MCPRS hardware architecture. It includes the acquisition of the sensor readings from MPS WSs, WS1 to WS4, and positioning and quality signals for MCPRS tasks such as pick-and-place and transport.
- (2)
- Data communication provides seamless interaction between devices and sensors over industrial communication protocols. Profibus DP, for MPS, utilizes the SIEMENS CM 1242-5 adapter to connect the S7-1214 master PLC with the S7-300 slave PLCs in MPS, and enables cyclic communication for transferring process data between WSs. Wireless TCP/IP for MCPRS to interfaces subsystems like PeopleBot, Cyton RM, and MVSS with SCADA via Ethernet for real-time task execution.
- (3)
- Data presentations display real-time operational states, sensor readings, and transition conditions through dashboards on HMI-MPS and HMI-MCPRS. Visual tools include transition state visualizations for the SHPN model, graphical timelines of process and transition events, as shown in Figure 14, Figure 15 and Figure 16, and alerts for anomalies or out-of-range sensor readings.
- (4)
- Remote and local control: SCADA transmits validated control commands to field devices for process adjustments and executes synchronization tasks for P/R/S operations, such as processing operations, transporting WPs from WS4 to WS1 for reprocessing or scrapping, activating PeopleBot DT-TTSMC control for precise displacement, and coordinating Cyton RM and MVSS actions for end-effector tasks.
4.2. Real-Time Control of MCPRS
- (A)
- The first control loop (PeopleBot WMR control) controls the movement of the PeopleBot WMR for forward and backward motion between WS4 and WS1. The control method is DT-TTSMC. This ensures precise trajectory tracking under dynamic conditions and integrates real-time data from the robot’s odometer system and onboard. Communication uses the ARIA Mobile Robots Library for command execution, which communicates wirelessly with the remote or local PC-HMI via the SCADA system [36]. Position and feedback data are transmitted from the embedded microcontroller via Wi-Fi to the SCADA server, which computes and sends control commands back to the WMR.
- (B)
- The second control loop (Cyton RM command synchronization). Handles the synchronization of commands between the Siemens S7-1200 PLC, and the Cyton RM. The control method is Modbus TCP communication which uses standard industrial protocols for real-time coordination between PLCs and the Cyton RM. This control loop ensures smooth task execution for robotic arm manipulation. Commands are wirelessly transmitted between the PC-HMI-MCPRS and the Cyton RM through an Ethernet adapter using a specific TCP/IP protocol.
- (C)
- The third control loop (MVSS control for end-effector accuracy) controls the movement of the MVSS to enhance the accuracy of pick-and-place tasks performed by Cyton RM’s end-effector. The control method is an image moments method that processes real-time image data to guide the end-effector in precise positioning. This control uses wireless communication to interface between the PC-HMI-MCPRS and MVSS. The communication is based on real-time updates sent wirelessly to adjust the positioning dynamically.
4.3. Real-Time Control of MPS Assisted by MCPRS
- Picking operations, detection, and precision control: The remote or local PC-HMI-MCPRS calculates positioning commands for the Cyton 1500 robotic manipulator (RM). These commands guide the RM for initial positioning for pickup operations at WS4. In Figure 18, the MVSS steps for WP detection are shown when taking over from WS4. On the upper left side in Figure 18 is vision-based detection by MVSS. Detection utilizes RGB-to-HSV color model conversion for robustness under varying lighting conditions. HSV better handles light changes compared to RGB, crucial during transitions between natural and artificial lighting. Object shape and position are determined using the Ramer–Douglas–Peucker algorithm that simplifies the object contour for shape analysis. Canny Edge Detection identifies the edges of the object for precise contouring. Centroid tracking employs the method of image moments, ensuring efficient 2D tracking. The process is robust for circular objects, consistently identifying their centroid within acceptable error limits. All of this was implemented using the OpenCV libraries [37]. Finally, in the image on the top right of the medallion in Figure 18, the object is tracked, meaning the target has been identified, if both the color and shape conditions were simultaneously met [13].
- Vision-based detection, alignment reference point and placing operations: On the upper left side in Figure 19 is WS1 detection and alignment reference point detection. The WS1 placement relies on detecting a rectangular reference point, contrasting circular object detection at WS4. This step ensures accurate alignment for reprocessing or scrapping. Fine Positioning with MVSS: like positional refinement, this is based on real-time feedback from MVSS. Adjustments in the end-effector positioning minimize error before placement.
- Fine Positioning with MVSS
- Integrated Technologies for Efficiency and Precision
- Trajectory Optimization and Performance
- Deviation Observations:
- Trajectory Precision through Real-Time Feedback
- Transport Operations with PeopleBot WMR
- Challenges and Improvement Opportunities:
5. Discussion
- Remote control and flexibility through the cloud/VPN system. Users can control MPS and MCPRS by AR interfaces for task management. The VPN ensures secure access, while OPC-UA facilitates data sharing with the cloud platform.
- Enhanced workflow optimization by using SHPN simulation allows for systematic modeling of both continuous and discrete processes, helping to optimize task execution and prevent bottlenecks in the system.
- Advanced communication infrastructure by using Profibus DP, Profinet, Ethernet, and OPC-UA ensures real-time, reliable communication across the system, with seamless integration between the production line, robotic systems, and the cloud.
- Interoperability and future scalability with OPC-UA, LAN Profinet, and LAN Ethernet, the system is future-proof, allowing for the easy integration of additional devices, sensors, or workstations as needed.
- Real-time task planning and visualization by planning tasks, simulating workflows, and visualizing the system in real time through AR and VR interfaces, enabling quick decisions and optimizations.
- Enhanced flexibility by using SHPN, TPN, and CPN models allows for flexible task execution, supporting both discrete (stationary WSs) and continuous (MCPRS) operations.
- Fault detection and reprocessing with MCPRS assist in reprocessing defective WPs, reducing downtime and improving overall production efficiency. Visual feedback through MVSS allows the MCPRS to autonomously handle quality control tasks.
- Cloud-based control through OPC-UA and WAN Ethernet, the system can be monitored and controlled remotely, offering cloud-based SCADA/HMI interfaces for enhanced accessibility and control from any location.
- Measure the percentage of tasks successfully completed without interruptions or errors, such as processing, reprocessing, and scrapping workflows. Evaluate the synchronization between the MPS and MCPRS. Use metrics like delay in command execution or deviations between DT simulations and real-world actions.
- Test the latency of the cloud/VPN connection, particularly for critical operations requiring real-time feedback (task planning with AR or reprocessing tasks). Monitor the availability of the system components (PLCs, embedded computer, MCPRS, WSs and DT) under varying operational loads. Measure the number of WPs processed, reprocessed, or scrapped per unit of time.
- Simulate scenarios with varying task complexities and multiple users remotely access the system to test scalability. Evaluate how well the system adapts to changes in production requirements, including the addition of new tasks or equipment.
- Conduct surveys or interviews with remote and local users to understand their experience with AR-based task planning and VR visualizations. Evaluate HMIs in terms of intuitiveness, error rates, and ease of navigation. Measure the time required for new users to learn and operate the system efficiently.
- Count system failures, communication errors, or downtime incidents over a defined period. Conduct penetration testing to evaluate the resilience of the Cloud/VPN infrastructure against cyber threats.
- Compare the DT simulation outcomes with real-world results. Metrics include deviation in task execution times or movements of the MCPRS. Assess the accuracy and usefulness of AI (machine learning)-based maintenance alerts or process optimizations.
- Monitor the energy efficiency of the MPS and MCPRS during operations. Calculate the costs associated with remote operations and compare them to traditional on-site methods.
- Use SHPNs to simulate different production scenarios and validate system responses. Leverage SCADA systems to display and analyze key performance indicators in real time. Compare the system’s performance against industry standards or similar setups.
- Ensure HMIs provide intuitive, real-time visual feedback about the status of the MPS and MCPRS (WP location, task progress, and errors). Include 3D models and AR/VR representations linked to the DT to visualize task planning and execution interactively. Implement customizable dashboards, allowing users to tailor views based on their roles. Use touch-based or voice-controlled interfaces for ease of operation, especially for mobile or wearable devices. Maintain consistency in layout, color schemes, and symbols across various control panels for different systems (MCPRS control vs. MPS task planning). Follow established user interface design guidelines, such as those from ISO standards for industrial interfaces. Include built-in mechanisms for guiding users to avoid common errors (confirmations for critical actions, visual warnings for system constraints). Provide contextual troubleshooting suggestions based on detected issues. Provide contextual troubleshooting suggestions based on detected issues.
- Design hierarchical menus logically, ensuring quick access to critical functions such as emergency stops or manual overrides. Enable multi-layer zoom for detailed and high-level views of the entire MPS and MCPRS workflows. Use automated task suggestions and assistive AI tools to streamline repetitive or complex operations. Offer drag-and-drop task scheduling in AR/VR environments for task planning. Provide auditory, haptic, or visual feedback to confirm actions, such as successful execution of reprocessing and scrapping command or MCPRS movement. Ensure HMIs are accessible to diverse users, including features like multilingual support, adjustable text sizes, and high-contrast modes.
- Include in-app feedback forms or direct links to reporting issues or requesting new features. Implement a feedback loop where operators receive responses to their suggestions or concerns. Track user interaction metrics, average task time, and error rates to identify usability bottlenecks. Regularly test HMIs with end-users during design and after deployment.
- Enable users to manipulate 3D models in AR to visualize and modify workflows dynamically. Allow operators to walk through virtual environments to practice controlling MPS and MCPRS workflows or diagnose issues remotely.
- Log errors to identify areas where HMI complexity may lead to mistakes. Adjust workflows to be more user-friendly based on error patterns. Provide interactive tutorials or simulation-based training directly within HMIs, utilizing DT visualizations. Regularly assess how new operators adapt to the system and refine features to reduce the learning curve.
- Mitigate unauthorized access to sensitive production data stored in the cloud. VPN vulnerabilities, such as default configurations or weak encryption, lead to eavesdropping or unauthorized access. Overwhelm the cloud service with traffic to disrupt operations. Attackers gain higher access levels in the cloud environment. Conduct regular vulnerability scans and penetration tests on the VPN and cloud systems.
- Intercept data between physical and virtual digital twins. Introducing false data to the DT for incorrect task planning or decision-making. Modify task planning or simulations in AR/VR. Monitor the consistency between physical and virtual digital twins with hash-based verification. Use real-time validation for AR/VR interactions to prevent discrepancies.
- Intercept traffic on OPC UA or Profinet and Ethernet networks. Re-send valid commands to disrupt synchronized operations. Encrypt MPS control data to halt operations. Deploy intrusion detection systems for LAN/WAN traffic. Conduct protocol fuzzing for OPC UA and Profinet to identify weaknesses.
- Exploit interfaces to control MCPRS movements or manipulations. Tamper with MVSS inputs to misdirect MCPRS operations. Target vulnerabilities in WMR or RM software. Validate commands with digital signatures. Use redundant sensor fusion to detect anomalies in localization.
- Enforce end-to-end encryption data transmission. Implement multi-factor authentication for cloud and VPN access. Use zero-trust architecture for access control, ensuring only verified users/devices can connect. Regularly apply security patches to cloud platforms and VPN clients.
- Use encrypted data streams between physical and digital twins. Deploy blockchain for immutable record-keeping of DT transactions. Implement role-based access control for AR/VR task planning. Use AI anomaly detection to identify malicious activity in DT simulations.
- Secure RM and WMR firmware with secure boot mechanisms. Introduce runtime integrity checks for MCPRS operations. Use geofencing policies to limit MCPRS operation to specific areas.
- Develop and enforce cybersecurity policies for employees and contractors. Deploy endpoint detection and response tools across WSs and PLCs. Establish an incident response plan and perform regular cybersecurity drills.
- Ensure the implemented cybersecurity measures are effective. Conduct periodic audits and risk assessments. Use penetration testing and red teaming to simulate attacks. Track key performance indicators, such as the number of blocked intrusion attempts or time to detect/respond to threats. Regularly review and update the system against emerging threats.
- We conducted a comprehensive analysis of existing research on digital twin (DT) technology, with a special focus on its integration into modular manufacturing systems (MPS) and mobile cyber–physical robotic systems (MCPRS).
- We highlighted the limited studies on hybrid DT systems that combine modular manufacturing lines and mobile robotic platforms, especially those that use AR/VR and SHPN modeling. We selected two methods for comparison, which are briefly presented below.
- Our proposed method: DT-based MPS and MCPRS integration.
- Key Features:
6. DT-Literature Review Using PRISMA and the Systematic Literature Review
- Introduction. The adoption of DT technology in manufacturing and robotics is pivotal in advancing Industry 4.0 and transitioning to Industry 5.0 paradigms. This literature review systematically analyzes the state-of-the-art methodologies and applications in DT for MPS assisted by MCPRS. The review follows PRISMA guidelines, ensuring a structured and comprehensive assessment of the existing literature.
- Methodology. The literature was reviewed using the PRISMA framework included the following:
- Key Themes and Findings
7. Conclusions
- Develop a DT framework integrating MPS and MCPRS for enhanced task planning, monitoring, and synchronization.
- Implement a hybrid SHPN model to ensure precise synchronization of discrete MPS operations and continuous MCPRS movements.
- Demonstrate the application of AR and VR technologies for real-time task planning, error handling, and operational insight in a modular production setting.
- Validate the system’s capability to reallocate tasks dynamically, including transporting defective workpieces from WS4 to WS1 for reprocessing or scrapping.
- Provide a robust teaching and research platform for Industry 4.0 and 5.0 applications, emphasizing real-time control, predictive analytics, and IoT-enabled manufacturing.
- Innovative DT framework for hybrid systems: The proposed system employs a DT framework that bridges real-world hardware with virtual representations using augmented reality (AR) and virtual reality (VR). This integration enhances real-time task planning, monitoring, and synchronization of operations between MPS and MCPRS.
- Advanced hardware architecture: The architecture features a line-shaped MPS with four workstations (WS1 to WS4) and a MCPRS equipped with a 2DW/1FW-WMR, a 7-DOF RM, and MVSS mounted on the RM’s end effector. This configuration enables efficient workpiece handling, transport, and processing, including reprocessing or scrapping operations.
- Hierarchical control system: The MPS control system integrates four workstation PLCs connected via Profibus DP to a central PLC, ensuring modularity and interoperability. The system extends connectivity with LAN Profinet, LAN Ethernet, and WAN Ethernet using OPC-UA, facilitating seamless local and cloud-based operations.
- SHPN for system coordination: A novel use of SHPN, combining Timed Petri Nets (TPN) for discrete MPS processes and Continuous Petri Nets (CPN) for MCPRS movement, provides a synchronized virtual representation of tasks. The SHPN simulation enables precise synchronization between discrete and continuous system dynamics, validated through real-time operational data.
- Enhanced user interaction with AR and VR: The AR-based task planning interface allows users to interact with production workflows in real time, visualize task schedules, and adapt to errors dynamically. VR-based simulation using SHPN improves transparency, offering an intuitive and immersive environment for understanding and optimizing system behavior.
- The effectiveness of the Cloud/VPN-based remote control of an MPS and MCPRS can be systematically assessed by focusing on functional, performance, reliability, and user-centric metrics. Continuous monitoring and iterative improvement cycles, informed by collected data and feedback, ensure the system remains robust and aligned with Industry 4.0 and 5.0 objectives.
- An effective HMI design ensures seamless integration of cloud/VPN-based remote control, DT visualizations, and MCPRS interactions, offering a balance of functionality and ease of use. Incorporating usability and user feedback mechanisms allows the system to evolve continuously, meeting the dynamic demands of Industry 4.0 and 5.0.
- By integrating strategies for cybersecurity threats and mitigation techniques the cloud/VPN-based remote-control system with DT technology can remain robust, resilient, sustainable, and secure against a wide array of cybersecurity threats (demand of Industry 5.0).
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Study | Focus Area | Key Contributions |
---|---|---|
Mincă et al. (2022) | MPS and robotic systems | Flexible assembly with DT integration |
Simion et al. (2022) | MCPRS | Mobile visual servoing for autonomous tasks |
Segovia et al. (2022) | DT architectures | Real-time data integration and control |
Zhang et al. (2022) | Predictive maintenance | Deep learning-enhanced DT applications |
Filipescu et al. (2023) | SHPN for DT | Task synchronization using hybrid Petri nets |
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Simion, G.; Filipescu, A.; Ionescu, D.; Filipescu, A. Cloud/VPN-Based Remote Control of a Modular Production System Assisted by a Mobile Cyber–Physical Robotic System—Digital Twin Approach. Sensors 2025, 25, 591. https://doi.org/10.3390/s25020591
Simion G, Filipescu A, Ionescu D, Filipescu A. Cloud/VPN-Based Remote Control of a Modular Production System Assisted by a Mobile Cyber–Physical Robotic System—Digital Twin Approach. Sensors. 2025; 25(2):591. https://doi.org/10.3390/s25020591
Chicago/Turabian StyleSimion, Georgian, Adrian Filipescu, Dan Ionescu, and Adriana Filipescu. 2025. "Cloud/VPN-Based Remote Control of a Modular Production System Assisted by a Mobile Cyber–Physical Robotic System—Digital Twin Approach" Sensors 25, no. 2: 591. https://doi.org/10.3390/s25020591
APA StyleSimion, G., Filipescu, A., Ionescu, D., & Filipescu, A. (2025). Cloud/VPN-Based Remote Control of a Modular Production System Assisted by a Mobile Cyber–Physical Robotic System—Digital Twin Approach. Sensors, 25(2), 591. https://doi.org/10.3390/s25020591