Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
Efficient navigation in crowded and dynamic environments is crucial for robot integration into human spaces. AVOCADO (AdaptiVe Optimal Collision Avoidance Driven by Opinion) generates collision-free velocities using Velocity Obstacles and adaptation to the cooperation estimation among agents. However, it assumes holonomic motion and cannot handle non-holonomic constraints, such as those of differential-drive robots. We propose DD-AVOCADO, an extension of AVOCADO that incorporates differential-drive kinematics to compute feasible and safe velocities. The method combines AVOCADO-based planning with a non-holonomic controller and accounts for tracking errors to avoid collisions. Simulation results across diverse scenarios show a significant reduction in collisions and efficient navigation in scenarios with cooperative and non-cooperative agents, and hardware experiments demonstrate its applicability in robot platforms. The method has the potential to be applied to other dynamic models....
Clinical diagnostic laboratories continue to face growing pressure from rising test volumes, increasingly complex testing menus, significant workforce shortages, and expectations for faster turnaround times at sustainable cost. Total laboratory automation (TLA) has become a central strategy for improving efficiency in high-volume laboratories, where integrated systems from Abbott, Roche, Siemens Healthineers, and Beckman Coulter have demonstrated substantial reductions in turnaround time, error rates, and labor requirements. Evidence across multiple health systems shows that TLA improves performance and stabilizes laboratory operations even during workload peaks. Despite these gains, large segments of pre-analytical and post-analytical workflows remain manual, especially tasks related to specimen transportation, bench-level manipulation, instrument tending, and troubleshooting. Recent progress in collaborative robotics (cobots), autonomous mobile robots (AMRs), and hospital service robots demonstrates that these technologies can complement TLA by addressing not only the logistical and dexterous tasks that fixed automation lines cannot reach but also enabling robots that can work safely right alongside humans in a shared space. Cobots have shown sub-millimeter precision in colony picking and other fine-motor tasks, though typically at lower throughputs than dedicated track modules, and AMRs have demonstrated reliable transport of pathology carts and medical supplies through large clinical environments. Meanwhile, humanoid-capable mobile manipulators, like Moxi from Diligent Robotics, deployed in hospitals are already completing hundreds of thousands of supply deliveries, indicating real-world significance. Here, we integrate technical, regulatory, operational, and business perspectives on TLA, collaborative robotics, and mobile platforms. We discuss real-world efficiency gains, regulatory expectations under the CLIA and United States FDA, and the emerging case for hybrid automation ecosystems that combine TLA islands, cobotic workcells, AMRs, and AI-enabled orchestration. We argue that the next decade of laboratory automation will move beyond monolithic tracks with robots toward flexible, modular robotic systems designed to operate safely together with humans and to augment the increasingly strained laboratory workforce. This not only allows clinical staff to dedicate more time to patient care but also ensures greater reliability and scalability for essential services throughout demanding hospital environments....
Applying state-of-the-art RGB object detectors (e.g., YOLOv8) to underwater scenes often yields unstable performance due to scattering, absorption, illumination deficiency, and bandwidth-limited transmission that severely corrupt image contrast and details. Forwardlooking sonar (FLS) remains informative in turbid or low-visibility water, yet its low resolution and weak semantics make conventional fusion architectures costly and difficult to deploy on resource-constrained robots. This paper proposes a paired-sample-free RGB–FLS joint training paradigm based on parameter sharing, where RGB and FLS images from different datasets are jointly used during training without any frame-level pairing or architectural modification. The resulting model preserves the original detector parameter scale and inference cost, and requires only RGB input at test time. Experiments on the SeaClear and Marine Debris FLS datasets under six representative underwater degradation factors (contrast loss, blur, resolution reduction, color cast, and JPEG compression) show consistent robustness gains over RGB-only training. In particular, under severe low-contrast corruption, the proposed training strategy improves mAP50 by more than 14 percentage points compared with the RGB-only baseline. These results indicate that sonar-domain supervision functions as an auxiliary structural constraint during optimization, rather than a conventional multi-source data enlargement. By forcing a shared-parameter detector to fit a texture-poor, geometry-dominant sonar domain, the learned representation is biased away from color/texture shortcuts and becomes more stable under adverse underwater degradations, without increasing deployment complexity....
While collaborative robots are designed to enable flexible and safe human–robot interactions, their comparatively low structural stiffness poses a challenge for high-precision machining and heavy-assembly tasks. Addressing this limitation is essential for enhancing their performance and improving their overall efficiency in manufacturing processes. This paper proposes an approach for enhancing the stiffness by means of situational coupling of two collaborative robots. Therefore, an analysis is conducted to determine the kinematic limitations of coupled collaborative robots. The stiffness of coupled collaborative robots is then modeled using the finite element method. Furthermore, experimental stiffness measurements of a single collaborative robot are conducted to establish a quantitative reference, which is both to validate the model and to quantify the stiffness enhancement achieved through coupling. On the basis of the combined experimental and numerical results, it is demonstrated that the approach of coupling has the potential to enhance stiffness by up to 37.19 times in comparison with a solitary collaborative robot....
Gasless Transaxillary Robotic Thyroidectomy (G-TART) has undergone signicant renement through the adoption of novel strategies to enhance surgical precision and safety. In this paper, we describe a novel technique that integrates dynamic endoscope repositioning, called the “swing technique”, with the use of a specialized intraoperative neuromonitoring (IONM) probe—Modena Robotic Probe—designed for robotic applications. The procedure, performed using the Da Vinci Xi system (Intuitive Surgical, Sunnyvale, CA, USA), incorporates intermient IONM during recurrent laryngeal nerve (RN) dissection. The swing technique involves real-time adjustment of the 30° endoscope between robotic ports to improve visualization within the conned transaxillary (TA) surgical eld, particularly during contralateral dissection. Simultaneously, the Modena Robotic Probe, a custom monopolar stimulation probe developed in collaboration with Dr. anger Medical GmbH for connection to the AVAANCHE® SI2 neuromonitor, allows precise RN mapping and verication throughout the operation. This approach could facilitate accurate anatomical tracking, minimize the risk of thermal or mechanical nerve injury, and enable safe navigation in a narrow operative TA tunnel. The adoption of advanced imaging techniques in conjunction with specialized robotic instrumentation may contribute to enhanced surgical safety and accuracy, emphasizing the importance of procedure-specic robotic approaches in thyroid surgery....
Loading....