Global localization is one of the important issues for mobile robots to achieve indoor navigation. Nowadays, most mobile robots rely on light detection and ranging (LiDAR) and adaptive Monte Carlo localization (AMCL) to realize their localization and navigation. However, the reliability and performance of global localization only using LiDAR are restricted due to the monotonous sensing feature. This study proposes a global localization approach to improve mobile robot global localization using LiDAR and a dual AprilTag. Firstly, the spatial coordinate system constructed with two neighboring AprilTags is applied as the reference basis for global localization. Then, the robot pose can be estimated by generating precise initial particle distribution for AMCL based on the relative tag positions. Finally, in pose tracking, the count and distribution of AMCL particles, evaluating the certainty of localization, is continuously monitored to update the real-time position of the robot. The contributions of this study are listed as follows. (1) Compared to the localization method only using LiDAR, the proposed method can locate the robot’s position with a few iterations and less computer power consumption. (2) The failure localization issues due to the many similar indoor features can be solved. (3) The error of the global localization can be limited to an acceptable range compared to the result using a single tag.

This study focused on the efficacy of employing a pulsed fiber laser in the curved cutting of thin, non-oriented electrical steel sheets. Experiments were conducted in paraffinic oil by adjusting the input process parameters, including laser power, pulse frequency, cutting speed, and curvature radius. The multiple output quality metrics included kerf width, inner and outer heat-affected zones, and re-welded portions. Analyses of the Random Forest Method and Response Surface Method indicated that laser pulse frequency was the most important variable affecting the cut quality, followed by laser power, curvature radius, and cutting speed. To improve cut quality, an innovative artificial intelligence (AI) approach incorporating a deep neural network (DNN) model and a modified equilibrium optimizer (M-EO) was proposed. Initially, the DNN model established correlations between input parameters and cut quality aspects, followed by M-EO pinpointing optimal cut qualities. Such an approach successfully identified an optimal set of laser process parameters, even beyond the specified process window from the initial experiments on curved cuts, resulting in significant enhancements confirmed by validation experiments. A comparative analysis showcased the developed models’ superior performance over prior studies. Notably, while the models were initially developed based on the results from curved cuts, they proved adaptable and capable of yielding comparable outcomes for straight cuts as well.

As key components of smart factories, intelligent production machines are gradually becoming a major capital asset of companies in the market, offering connectivity, interoperability, and services. However, due to commercial and technological constraints, production machines rarely provide enough information for customers to develop data-driven applications. Therefore, this research proposes that intelligence should be built into them to assist in production control and to allow relevant data to be collected and made available for future use. In order to realize this proposal, in this paper, an operational paradigm for such systems is first outlined. Then, the states of workpieces in a production environment are classified, and the methods of generation, operation, and transfer of instances described. The next step is to build an intelligent product-driven manufacturing system using Data Distribution Service (DDS). Intelligent workpieces and machines collaborate to complete production tasks in such a system. In the course of this collaborative process, an intelligent workpiece continuously collects relevant data and packages it together for the customer on its transformation into a final product. The feasibility of this intelligent product-driven manufacturing system is examined by upgrading a classic manufacturing factory. In addition, the study explores the system architecture, the implementation of a classic system upgrade, the production data collection, and the intellectualization of data assets. In conclusion, this study’s results will help machine builders expand their business models providing customers with production machines that carry data for advanced data-driven applications.

Small and medium-sized enterprises (SMEs) require agile and robust strategies to navigate the difficulties of their evolution toward Industry 4.0. This study proposes a Blockchain-based Shop Floor Control System (BSFCS) that incorporates a Consensus-based Workflow Change Integration (CWCI) mechanism and Smart Contract Service (SCS) design methodology to provide these strategies. The CWCI represents an incremental agile approach, enabling real-time workflow adjustments and smooth, rapid integration of evolving enterprise requirements. The modular architecture and optimized key-state data structures that are features of the SCS design methodology support efficient transaction management, ensuring system feasibility. A prototype implementing the BSFCS was tested on a realistic industrial platform replicating real-world manufacturing conditions. Results from two validation experiments demonstrate significant performance improvements over a standard model. These enhancements include dynamic workflow adaptability, enhanced operational transparency, and efficient handling of real-time changes. These BSFCS characteristics represent requirements SMEs need for a reliable and sequential pathway they can follow implementing Industry 4.0. They show that compared to existing frameworks, the BSFCS framework we propose offers superior adaptability and scalability. It particularly excels in handling dynamic workflow changes and sequential upgrades with minimal disruption to ongoing operations.

Visible light positioning (VLP) technology has emerged as a cost-effective solution for indoor localization, offering reasonable accuracy. However, its performance is limited by real-world optical constraints, noise interference, and challenges in system modeling. This study aims to enhance VLP localization accuracy by processing high-dimensional input features using a Kolmogorov–Arnold Network (KAN) and comparing its performance with that of other modeling approaches. A VLP system was developed using multiple LEDs and receivers, with prime numbers assigned as LED transmission frequencies to enable distinct positional fingerprinting across the spectrum. To achieve accurate localization, four machine learning models were employed: KAN, multilayer perceptron (MLP), random forest regression (RFR), and Transformer. To enhance model accuracy, feature selection based on coverage criteria was employed. This approach helped identify essential features while minimizing the impact of noisy inputs. Experimental results show that the KAN model outperformed the others. Specifically, KAN achieved an overall localization error of 10.04 mm within a 300 mm × 300 mm × 200 mm workspace, and an XY-plane error of 4.48 mm, along with a more concentrated error distribution.