Unsuccessful overtaking maneuvers on two-lane rural roads are one of the major causes of road accidents in the 21st century. The complexity of this maneuver merits the adoption of a thorough method for developing a proposed assistance system to prevent accidents and consequently reduce the high number of fatalities and the associated economic costs. This study aims to introduce an intelligent Driver Overtaking Assistance System (DOAS) to assist drivers in performing overtaking maneuvers safely. The study also will introduce a method to assess the impact of all the influential variables related to the driver, vehicle, traffic, road, and the surrounding environment. In momentary driving situations, the DOAS uses the communicated information via Hello beacon messages (HBM) and a set of input sensors to measure the possibility of overtaking the preceding vehicle(s) proactively by considering whether the distance gap to the oncoming vehicle is sufficient for overtaking. Besides, the proposed system is a vehicle-based safety system based on the collection of contextual information from the driving vicinity to acquire all relevant information regarding the ambient driving environment and the vehicles involved in the overtaking. To do this, DOAS uses a Bayesian Network (BN) to model overtaking maneuvers. The work presented shows high accuracy and promising results in aiding safe overtaking, with significant improvements to overtaking maneuvers on two-lane rural roads.

This study investigates the impact of different aggregation operators (commonly referred to as fuzzy operators) on the application of fuzzy signatures. Fuzzy signatures are specialized multidimensional data structures that symbolically represent data. As a use case, the study focuses on robot environment representation and path planning, presenting the results obtained by applying various aggregation operators including $minimum$, $maximum$, $algebraic$ $product$ and $arithmetic$ $mean$ on the normalized values obtained from the robot sensors. The comparison highlights their effects on the computational load and path lengths of the path planning task. The findings reveal that the most efficient aggregation operator, in terms of computational load and the path length, is the $algebraic$ $product$ aggregation operator. Specifically, the $algebraic$ $product$ consistently yielded the shortest paths (as low as 22 nodes) and the lowest execution times (down to 0.0913 s), demonstrating superior efficiency compared to the $maximum$ operator, which resulted in path lengths up to 34 nodes and execution times reaching 0.1923 s. This represents an improvement of up to 35.3% reduction in path length and 52.5% reduction in execution time when comparing the $algebraic$ $product$ to the $maximum$ operator based on observed extreme values. Furthermore, this work provides the first empirical comparison of fuzzy aggregation operators specifically for fuzzy signature-based mobile robot path planning.

This paper introduces a hypergraph-based formalism for modeling kinematic and dynamic structures in robotics, addressing limitations of the existing formats such as Unified Robot Description Format (URDF), MuJoCo-XML, and Simulation Description Format (SDF). Our method represents mechanical constraints and connections as hyperedges, enabling the native description of multi-joint closures, tendon-driven actuation, and multi-physics coupling. We present a tensor-based representation derived via star-expansion, implemented in the Hypergraph Model Cognition Framework (HyMeKo) language. Comparative experiments show a substantial reduction in model verbosity compared to URDF while retaining expressiveness for large-language model integration. The approach is demonstrated on simple robotic arms and a quarter vehicle model, with derived state-space equations. This work suggests that hypergraph-based models can provide a modular, compact, and semantically rich alternative for the next-generation simulation and design workflows. The introduced formalism reaches 50% reduction compared to URDF descriptions and 20% reduction compared to MuJoCo-XML descriptions.

This paper introduces a pioneering approach to robot environment representation by integrating a novel hypergraph-based method for modeling fuzzy signatures with a quadtree-like structure for obstacle detection. By structuring fuzzy signatures through hypergraphs, we establish a robust framework that not only streamlines information representation but also simplifies the aggregation-based decision-making process. This synergy is applied to the domain of mobile robotics, where accurate and efficient environment representation is essential. Utilizing a quadtree-like structure for data organization, our technique systematically evaluates feature points against a set of fuzzy operations, determining the significance of obstacles and reconstructing the environmental model through the traversal of the quadtree-like structure. Furthermore, the hypergraph-based formalism sets the stage for a future transition to a tensor-based representation of fuzzy signatures, as envisioned in future work.

The integration of extended reality (XR) into industrial robotics requires robust middleware solutions capable of bridging heterogeneous systems, protocols, and user interactions. This paper presents a novel middleware framework designed to connect industrial robots with XR devices such as the HoloLens. The architecture employs a hybrid communication layer that combines MQTT (Message Queuing Telemetry Transport) and ØMQ (Zero Message Queue), leveraging the Sparkplug Robotics API model for robot data and publisher–subscriber streaming for XR camera feeds. A Redis cache database is introduced to ensure efficient data handling and prevent data corruption. On the robot side, the system is built on ROS 2 (Robot Operating System) and connects to proprietary industrial protocols through dedicated bridges, enabling seamless interoperability. Spatial alignment between physical robots and XR overlays is achieved using ArUco marker-based synchronization, while real-time kinematic and process data are visualized directly in XR. The middleware further supports bidirectional interaction, allowing users to adjust parameters and issue commands through XR devices. Beyond functionality, safety considerations are incorporated by integrating human–robot interaction safeguards and ensuring compliance with industrial communication standards. The proposed solution demonstrates how middleware-driven XR integration enhances transparency, control, and safety in robotic manufacturing processes, laying the foundation for greater efficiency and adaptability in Industry 4.0 environments.

Industrial robotic systems increasingly operate as heterogeneous ecosystems in which production, maintenance, quality assurance, safety, and human–machine interaction are coupled through shared data and cross-layer constraints. Existing modeling approaches remain structurally fragmented: hierarchical taxonomies support decomposition, graph-based models primarily encode pairwise relations, and analytical layers are commonly attached as external pipelines. This paper proposes a hypergraph-based framework for the multi-contextual state representation of industrial robotic systems. The framework combines a multi-layer problem taxonomy, a formal definition of context as an active semantic processing unit, and a directed hypergraph model with signed incidence for representing dependency, interpretative, compositional, and cross-layer constraint relations without binary decomposition. The model is instantiated on grasping and maintenance examples and translated into a numerical interface for downstream analytical processing. Quantitative results are also reported. Benchmarking shows near-linear compile-time and star-expansion scaling, while comparison with pairwise encodings confirms lower representational overhead for higher-order relations. In a canonical grasping scenario, one-cycle hypergraph-grounded inference remains in the microsecond range on CPU, with a median latency of 2.264 µs. These results indicate that the proposed framework is computationally tractable as a representational substrate for context-aware analysis. The contribution of the paper is not a new control algorithm, but a formal representation and numerical translation layer for future learning-based and rule-based analytical methods.