The separate design of vision algorithms and motion control algorithms in existing vision servo systems limits their application in high-speed and high-precision industrial scenarios. To address this problem, a vision-motion codesign method is proposed for integrating visual servo systems with motion controllers via real-time Ethernet connections. During the engineering design phase of our proposal, we developed a control kernel architecture to clarify the system design logic. Then, we proposed a runtime module for real-time trajectory generation to facilitate the unified deployment of different planning methods in the kernel. On this basis, we implemented a self-adaptive trajectory generation synthesis method capable of dynamically adjusting the motion resources for real-time interpolation. The experimental results demonstrate that in visual servo static tracking experiments with the SCARA robot, our proposed method has a faster response time and smaller impact than other mainstream trajectory generation methods in 1 ms real-time interpolation cycles.

Technological advances in the Internet of Things (IoTs) and the evolution of manufacturing models applied in industrial environments increase the use of concepts, such as plug-and-play in intelligent manufacturing systems for customized products. However, plug-and-play manufacturing modes are subjected to dynamic changes in both products and resources during the runtime phase. Hence, traditional model-based formal verification methods cannot guarantee system dependability before and after the reconfiguration. In addition, customized manufacturing is a product-driven process, which requires product- and equipment-based modeling and analysis to be considered during the product life-cycle management. In this article, a model-data-driven (DD) framework for the formal analysis of production resources is proposed. An IoT-based DD technology is combined with a model-driven approach to ensure dependability verification of formal models in the operational production line phase through real-time resource status feedback. The proposed method is verified experimentally, using a smart factory experimental platform. The results show that the proposed method reduces the time-cost of the formal modeling phase by at least 75%.

Recent technological advancements and the evolution of industrial manufacturing paradigms have substantially increased the complexity of product-specific production systems. To reduce the time cost of modelling and verification and to enhance the degree of uniformity in the modelling process of system components, this article presents a componentised framework for domain modelling and performance analysis based on the concept of “multi-granularity and multi-view” for a production line of personalised and customised products, for plug-and-play manufacturing processes to involving a large number of model input parameters. The coloured Petri net tool is utilised as a simulation tool for mapping domain models to computational models for simulation and performance evaluation. This paper presents a method for setting the input parameters of a production system when using WIP, through-put and cycle time as metrics. The results of the performance analysis demonstrate the applicability of the proposed framework and provide direction for the production line’s layout design and scheduling strategy.

Traditional workflow systems suffer from low flexibility and manual process modeling in highly customized manufacturing, which cannot adapt to dynamic product demands and distributed manufacturing resources. To solve this problem, this paper proposes a meta-mapping based automatic process definition method, which upgrades the two-dimensional process information model to a three-dimensional business workflow model. The method maps the phase dimension to sequential flows and the granularity dimension to parallel nodes, realizing automatic generation of workflow definitions. Meanwhile, formal verification including reachability, safety and liveness is designed to ensure process validity. Experimental results show that the proposed method reduces the process modeling time by 27.45% for complex customized products, and significantly improves the flexibility of service orchestration.