Abstract
Inspecting reflective parts is challenging due to strong specular reflections that conceal small porosities and reduce defect visibility. This work presents a framework that combines augmented reality with a deep learning detector. An augmented reality headset is used to capture multi-view images under natural illumination, enabling the operator to adjust the viewpoint and obtain angles that reduce glare. The collected data form a 640 × 480 dataset used to train a yolov8 detection model, integrated into a Robot Operating System 2 architecture for real-time processing. Testing on an independent set of unseen parts yields a precision of 86.70 %, a recall of 87.26 %, and an F1-score of 86.97 %. Additional qualitative examples confirm that the model can identify low-contrast porosities despite reflective surfaces. The results demonstrate the feasibility of AR-assisted acquisition combined with deep learning for real-time inspection of machined aluminum components in a laboratory case study. © 2026 IEEE.

Abstract
Accurate dynamic modeling of ground robots (Unmanned Ground Vehicles - UGVs) is essential for robust control and navigation in agricultural environments, where variations in soil friction and rolling resistance significantly affect system dynamics. This work proposes a Linear Parameter-Varying (LPV) model parameterized by the friction coefficient, identified under different soil conditions using two excitation strategies: Amplitude-Pseudo-Random Binary Sequence (APRBS) and standard maneuvers (SM). A simulated ground robot - the Clearpath Husky - was used under multiple soil friction scenarios within the ROS 2 and Gazebo simulation environment. The results show that the LPV model effectively captures the influence of soil friction, with both LPV APRBS and LPV SM yielding similar RMSE values across scenarios. The results also highlight the feasibility of using SM-based excitation for identifying the robot dynamics. © 2026 IEEE.

Abstract
Building 3D maps in agricultural environments is challenging due to dense vegetation, irregular terrain, lack of landmarks, and unreliable GPS. This paper proposes a Bounding Box-Based 3D Mapping method using collaboration between an Unmanned Ground Vehicle (UGV) and an Unmanned Aerial Vehicle (UAV). The method simplifies crop rows and tree canopies by enclosing their point clouds in 3D bounding boxes, fused with original UAV and UGV data, producing compact maps that preserve essential structures for autonomous navigation and trajectory planning. Evaluation in a simulated Orchard scenario shows that the method could reduce map size by up to 60% while maintaining 83.6% coverage. Multi-robot collaboration proved crucial, with the UGV contributing 74% and the UAV 26% of the merged map. Overall, the proposed method demonstrates potential and deserves further investigation in more complex agricultural scenarios. © 2026 IEEE.

Abstract
Digital twins enable real-time modeling, simulation, and monitoring of complex systems, driving advancements in automation, robotics, and industrial applications. This study presents a large-scale digital twin-testing facility for evaluating mobile robots and pilot robotic systems in a research laboratory environment. The platform integrates high-fidelity physical and environmental models, providing a controlled yet dynamic setting for analyzing robotic behavior. A key feature of the system is its comprehensive data collection framework, capturing critical parameters such as position, orientation, and velocity, which can be leveraged for machine learning, performance optimization, and decision-making. The facility also supports the simulation of discrete operational systems, using predictive modeling to bridge informational gaps when real-time data updates are unavailable. The digital twin was validated through a matrix manufacturing system simulation, with an Augmented Reality (AR) interface on the HoloLens 2 to overlay digital information onto mobile platform controllers, enhancing situational awareness. The main contributions include a digital twin framework for deploying data-driven robotic systems and three key AR/VR integration optimization methods. Demonstrated in a laboratory setting, the system is a versatile tool for research and industrial applications, fostering insights into robotic automation and digital twin scalability while reducing costs and risks associated with real-world testing.

Abstract
The growing reliance on e-commerce and the demand for efficient intralogistics operations have increased the need for automation, while labour shortages continue to pose significant challenges. When combined with the inherent risks of forklift operation, this circumstance prompted businesses to look for robotic solutions for intralogistics tasks. However, robots are still limited when they come across situations that are outside of their programming scope and often need assistance from humans. To achieve the long-term goal of enhancing intralogistics operation, we propose the development of a virtual reality-based teleoperation system that allows remote operation of robot forklifts with minimal latency. Considering the specificities of the teleoperation process and network dynamics, we conduct detailed modelling to analyse latency factors, optimise system performance, and ensure a seamless user experience. Experimental results on a mobile robot have shown that the proposed teleoperation system achieves an average glass-to-glass latency of 368 ms, with capturing latency contributing to approximately 60% of the total delay. The results also indicate that network oscillations significantly impact image quality and user experience, emphasising the importance of a stable network infrastructure.