With Industry 5.0, human–robot collaboration has become the center of attention. This has introduced new challenges, where workspaces become highly dynamic, leading to safety concerns for robots and especially for humans. This makes it important to have a realistic and accurate digital representation of a production cell and its components in environments for movement planning and remote supervision. This paper presents a digital twin of a smart production cell, synchronizing objects bidirectionally between a real and virtual workspace with minimal effort using only a single RGB-D camera. A fine-tuned YOLO-based detector identifies tools and items in the scene, estimates their spatial position, and spawns them in Unity relative to the robot via coordinate transformation. Experiments with different scanning velocities demonstrate a mean planar spawn deviation of 5.82mm (standard deviation 2.24mm) at 0.1m/s at 0.7m height, while maintaining a constant depth bias of −0.54mm. Once spawned, objects can be manipulated freely via drag-and-drop within the simulation. Upon confirmation, a motion planning module calculates trajectories to execute these changes physically. Across 95 trials and 1805 object placements, the system achieves 100% success within working bounds, successfully executing complex tasks such as repositioning objects and stacking them into pyramid structures. The presented system provides a framework to see, spawn, and synchronize industrial workspaces, enabling rapid setup and safe remote supervision of smart production cells in highly dynamic industrial environments.
M. Herrmann, Dominykas Strazdas, A. Al-Hamadi· Machines· 0 citations
Simultaneous Localization and Mapping (SLAM) is essential for autonomous systems navigating in human-centric environments, yet conventional systems fail when people and objects move through the scene. This paper introduces SAR-SLAM (Semantic-Aware Recognition SLAM), an RGB-D SLAM framework that robustly handles dynamic scenes containing moving people and objects using dual semantic geometric processing. First, we employ YOLOv8-based semantic segmentation to identify dynamic objects and generate initial detection masks. Second, we apply RANSAC-based Homography analysis to perform geometric motion verification, distinguishing truly moving objects from stationary ones by analyzing feature correspondence patterns. Third, an adaptive fusion mechanism combines both semantic and geometric evidence while incorporating temporal consistency and coverage constraints to maintain system stability. The system is implemented as a modular ROS2 package, enabling smooth integration with robotic systems and compatibility with existing navigation frameworks. SAR-SLAM reduces Absolute Trajectory Error by up to 96% over ORB-SLAM3 on the dynamic sequences of the TUM RGB-D benchmark, and remains competitive with state-of-the-art dynamic SLAM methods across a range of dynamic scenarios.
Basheer Al-Tawil, Magnus Jung, Thorsten Hempel et al.· Robotics· 0 citations
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