industrial system integration

Open Industrial Edge (OIE) environments face challenges in multi-vendor module interoperability, including timing uncertainty, insufficient safety certifiability, and scalability limitations for large-scale distributed deployment. Method: This paper proposes a low-code automation architecture integrating Lingua Franca (LF) coordination semantics with the IEC 61499 event-driven model. It innovatively embeds LF’s deterministic semantics into the IEC 61499 function block execution layer to ensure timing robustness and functional safety at design time, and introduces a joint optimization method combining synchronous constraint modeling with time-optimal scheduling. Contribution/Results: Evaluated on the Siemens OIE platform, the approach supports distributed deployment across ≥100 nodes, reduces end-to-end control latency variation by 83%, and achieves SIL2 safety certification. It significantly enhances security, timing determinism, and engineering scalability of modular, distributed automation systems.

Small- and medium-sized enterprises (SMEs) and research institutions face significant challenges in deploying robotic systems due to limited robotics expertise, heavy reliance on external system integrators, complex hardware interfaces, and poor cross-platform reusability. Method: This paper proposes a general-purpose robot software framework based on object-oriented design. Its core innovation is a unified abstraction model—“robot skill”—achieved through layered abstraction and modular encapsulation, which decouples hardware-specific implementations and standardizes control logic across heterogeneous platforms (e.g., Yaskawa Motoman GP4). Implemented in Python, the framework enables rapid integration into smart manufacturing systems. Contribution/Results: Experimental evaluation demonstrates that the framework substantially reduces interface complexity, shortens prototyping and deployment cycles, decreases dependency on external integrators, and improves both development efficiency and skill reusability across robotic platforms.

This study addresses the limitations of current physical human–robot interaction safety standards, such as ISO/TS 15066, which rely on simplified assumptions without a systematic analysis of their theoretical foundations, underlying premises, or impact on system performance. By modeling safety constraints, conducting energy-based safety analyses, and performing numerical simulations, this work uncovers the implicit assumptions embedded in widely used safety criteria and elucidates their practical consequences, highlighting the central role of energy in safety assessment. The research quantifies the performance degradation induced by oversimplified design choices and introduces tunable parameters to optimize safety-critical control strategies. Furthermore, it offers novel insights into energy-driven safety methodologies, significantly advancing the balance between safety and performance in human–robot collaborative systems.

Industrial Control and Automation Software (iCAS) suffers from ambiguous design concepts and a lack of foundational design theory, leading to empiricism and inconsistent engineering practices. Method: This study systematically introduces design theory to iCAS for the first time, establishing a scientifically grounded design-definition framework. Through interdisciplinary literature review and theoretical analysis, it clarifies the ontological boundaries, core semantics, and quality criteria of design; rigorously distinguishes “design activity” from “design language”; and proposes a novel design balance mechanism reconciling system evolvability with real-time operational constraints. Contribution/Results: The work yields a reusable design metatheory for iCAS software engineering, enhancing design process standardization, traceability, and innovation capacity. It advances the field from experience-driven practice toward theory-guided development, providing principled foundations for systematic design methodology and implementation pathways.

Existing formal verification methods primarily target isolated PLC programs, neglecting their continuous dynamic interaction with the physical environment and networked communication among controllers—thus failing to capture the coupling between communication delays and closed-loop control inherent in real-world industrial systems. This work proposes the first unified formal framework integrating PLC semantics, network communication modeling, and continuous physical dynamics. Leveraging hybrid systems theory and discrete-event modeling, we construct a compositional closed-loop model enabling co-verification of cyber and physical components. To mitigate state-space explosion, we introduce a partial-order reduction technique that preserves verification correctness while significantly compressing the state space. Our approach achieves, for the first time, precise modeling and scalable formal verification of networked PLC systems subject to both communication delays and continuous physical dynamics.

This study addresses the challenge of robotic automation in garment manufacturing, where fabric deformability complicates precise manipulation. To overcome this, the authors propose an end-to-end sewing automation framework that integrates digital twin technology, digital thread methodology, and runtime verification. The system automatically generates process parameters and robot trajectories from DXF patterns, validates deployment plans through a digital twin prior to physical execution, and employs interoperable middleware to seamlessly connect collaborative robots with conventional sewing machines. Real-time seam monitoring and collision detection further enhance execution robustness. Evaluated on 2D pocket attachment and 3D shaping tasks for denim shorts, the framework demonstrates substantial improvements in deployment efficiency, scalability, and operator adaptability, while significantly reducing on-site commissioning risks.

This study addresses the persistent gap between theoretical control performance and its practical realization in real-world robotic systems, often caused by inadequate discretization, insufficient real-time guarantees, and weak error handling in control software. For the first time from a software engineering perspective, the authors systematically analyze 184 open-source robotic controllers through code review, empirical analysis, and test evaluation, uncovering common deficiencies in application scenarios, implementation details, and verification practices. The findings reveal that most implementations fail to properly account for critical system constraints, and their testing strategies inadequately validate the theoretical assurances they claim. This work highlights a significant disconnect between implementation quality and theoretical promises, offering concrete directions and practical guidelines for developing reliable, verifiable robotic control software.

The advancement of industrial and collaborative robotics has introduced novel human–robot interaction paradigms, posing new challenges to existing safety standards in functional safety, cybersecurity, and collaborative operation. This study systematically examines the evolution of ISO 10218:2011 to ISO 10218:2025, incorporating ISO/TS 15066, through comparative textual analysis, terminological mapping, and tracking of technical clause developments. For the first time, cybersecurity and protection against unauthorized access are formally integrated into the industrial robot safety standard framework. The work proposes a revised classification for robots and collaborative applications and achieves normative incorporation of ISO/TS 15066, thereby establishing a comprehensive safety framework encompassing mechanical, functional, and digital safety dimensions. This significantly extends the safety requirements for modern networked robotic systems.

Existing data-driven approaches are largely confined to isolated domains—such as design or manufacturing—lacking deep integration between design features and manufacturing process data, thereby hindering sustainability-oriented design optimization. This paper proposes a novel system architecture that, for the first time, enables real-time mapping and fusion of design features with multidimensional manufacturing data—including dimensional error rates, energy consumption, and processing time—and constructs an interpretable association model. Sustainability metrics are embedded into a closed-loop, data-driven design framework integrating IIoT data acquisition, heterogeneous data fusion, feature engineering, and supervised machine learning (regression and classification). Validated on an industrial production line, the approach achieves a 32% improvement in the accuracy of design improvement recommendations, a 14.7% reduction in average energy consumption, and a 21.3% decrease in machining defect rate—significantly advancing intelligent, green, manufacturing-aware design.