The growing automation and demand for customized products in modern manufacturing systems require innovative approaches to synchronize intralogistics with mixed-model assembly lines. This paper explores strategies to integrate production scheduling and sequencing to optimize the flow of materials and processes in flexible manufacturing systems. The study focuses on synchronizing component production subsystems with the main assembly line using Automated Guided Vehicles (AGVs) for material transport. We propose a two-stage approach: first, determining the minimum number of workstations required to maintain production continuity, and second, optimizing the number of AGVs to ensure seamless material flow. The research uses discrete event models and backward scheduling strategies to analyze system performance across various conditions. Results indicate that the proposed method effectively balances production and transport operations, minimizing delays and idle times. The study emphasizes the importance of integrating real-time data, predictive-reactive scheduling, and just-in-time principles to enhance the flexibility and efficiency of manufacturing systems. The work contributes to the advancement of production planning and control in complex, resource-constrained manufacturing environments.

This paper investigates the attitude control problem of Multi-Spacecraft Systems (MSS) under the background of communication link failures caused by network attacks. An attitude error model is established based on the Lie group SO(3) for directed communication topologies with static virtual leaders. A link failure probability calculation method based on pointing errors is proposed, and the fault evolution mechanism within the system is described using Markov chain theory. Furthermore, a logic-based switching fault-tolerant attitude controller on SO(3) is introduced to maintain the coordinated control performance of the system. Finally, simulation results validate the effectiveness of the proposed controller, demonstrating that the method can effectively suppress disturbances and maintain attitude coordination even in the presence of link failures and fault propagation.

Autonomous vehicle navigation is a basic functionality for ground robots, with applications within research and real-world scenarios. This paper discusses the configuration and utilization of ROS 2’s native tools and packages to deploy a complete indoor path planning system on a differential-drive robot in a simulation setup. The BumperBot platform, obtained from an open-source GitHub repository, receives simulated odometry and lidar sensor data, and employs Graph SLAM for mapping and Adaptive Monte Carlo Localization (AMCL) for pose estimation through the slam_toolbox package and configuration files. Path planning and control are implemented using the Nav2 stack, with the addition of a custom Python node for executing autonomous waypoint navigation. Experimental results on the Gazebo simulation platform confirm the usefulness of ROS 2’s modular navigation stack for enabling autonomous point-to-point navigation with minimal custom code. One of the main characteristics of this method is its architectural modularity, allowing for clear separation of mapping, localization, and navigation modes and portability, as demonstrated by adapting the same launch and navigation framework to the QCar2 simulated platform with slight changes. The system that is produced offers a reproducible and extensible basis for future research on simulation-based autonomous navigation, educational robotics, and quick prototyping of novel platforms or behaviors.

This systematic review examines the application of Federated Learning (FL) in Prognostics and Health Management (PHM). Through a structured analysis of all relevant literature using the PRISMA methodology, we analyze research from major academic databases to investigate the current state of FL frameworks, their application to prognostics tasks, and associated implementation challenges. To the best of our knowledge, this is the first review dedicated to exploring the intersection of FL and PHM. Our findings highlight a growing interest in this domain and reveal that applications remains underexplored. This review aims to provide clear insights into the current landscape and guide future research and practical applications in industrial contexts.

Communication between single cells or higher organisms by means of diffusive compounds is an important phenomenon in biological systems. Modelling therefore often occurs, most straightforwardly by a diffusion equation with suitable flux boundary conditions at the cell boundaries. Such a model will become computationally inefficient and analytically complex when there are many cells, even more so when they are moving. We propose to consider instead a point source model. Each cell is virtually reduced to a point and appears in the diffusion equation for the compound on the full spatial domain as a singular reaction term in the form of a Dirac delta 'function' (measure) located at the cell's centre. In this model, it has an amplitude that is a non-local function of the concentration of compound on the (now virtual) cell boundary. We prove the well-posedness of this particular parabolic problem with non-local and singular reaction term in suitable Sobolev spaces. We show for a square bounded domain and for the plane that the solution cannot be H¹-smooth at the Dirac point. Further, we show a preliminary numerical comparison between the solutions to the two models that suggests that the two models are highly comparable to each other.

We present a fully reproducible benchmark protocol for short-horizon motion prediction in collaborative assembly using the QUB-PHEO dataset. The protocol fixes data splits, coordinate normalisation, evaluation metrics and training/evaluation scripts, enabling like-for-like comparison and easy extension to new models or modalities. Comparisons across three input regimes (hands only, hands + surrogate + gaze, and full 214-D aerial context) using standardised metrics reveal how architectural choices and added context affect robustness, beyond headline accuracy. All code, data splits, and pretrained weights are released, validating QUB-PHEO’s suitability for vision-based motion prediction and providing a standardised baseline for the dyadic HRI community.