Robot program interpreter is an important tool for robot to realize real-time control and fast feedback. In order to simplify the process of robot programming and improve the interpretation efficiency of robot program, an easy-to-use and efficient industrial robot program interpreter is designed and implemented in this paper. The interpreter divides the robot program into two parts: variable definition and instruction call. The process of interpretation includes lexical analysis, syntactic analysis, semantic analysis and instruction interpretation modules. Using flex and bison tools to assist in the generation of lexical and syntactic analysis programs, this paper proposes child-sibling notation (CSN) to construct a syntax tree. In semantic analysis, the red-black tree structure of the map container is used to create a symbol table and record variable information. By the way of presetting type checking codes, errors in the program can be reported and handled. Finally, the interpreter traverses the syntax tree with depth-first algorithm, and calls the corresponding control function while interpreting the instruction sentence to execute the motion of robot. The experimental results show that the designed interpreter has high efficiency and stability in interpreting the robot program and meets the operational requirements of industrial robots.

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.

No abstract

The technology of visual servoing, with the digital twin as its driving force, holds great promise and advantages for enhancing the flexibility and efficiency of smart manufacturing assembly and dispensing applications. The effective deployment of visual servoing is contingent upon the robust and accurate estimation of the vision-motion correlation. Network-based methodologies are frequently employed in visual servoing to approximate the mapping between 2D image feature errors and 3D velocities, offering promising avenues for improving the accuracy and reliability of visual servoing systems. These developments have the potential to fully leverage the capabilities of digital twin technology in the realm of smart manufacturing. However, obtaining sufficient training data for these methods is challenging, and thus improving model generalization to reduce data requirements is imperative. To address this issue, we offer a learning-based approach for estimating Jacobian matrices of visual servoing that organically combines an extreme learning machine (ELM) and a differential evolutionary algorithm (DE). In the first stage, the pseudoinverse of the image Jacobian matrix is approximated using the ELM, which solves the problems associated with traditional visual servoing and is resistant to outside influences such as image noise and mistakes in camera calibration. In the second stage, differential evolution is utilized to select input weights and hidden layer bias and to determine ELM’s output weights. Experimental results conducted on a digital twin operating platform for 4-DOF robot with an eye-in-hand configuration demonstrate better performance than classical visual servoing and traditional ELM-based visual servoing in various cases.