Current Issue : April-June Volume : 2026 Issue Number : 2 Articles : 5 Articles
This work underscores the importance of developing and refining machine learning (ML) methods to meet the specific demands of anomaly detection in 5G-powered environments. It addresses key challenges, including the deployment of robotics within industrial settings that require robust low-latency communication and high data throughput. The proposed architecture thus delves into innovative ML-driven approaches that not only optimize anomaly detection but also maintain high performance under the constraints and requirements imposed by 5G-enabled industrial applications. Our experiments demonstrate the effectiveness of these techniques in accurately identifying anomalies while minimizing false positives. The practical implications of integrating anomaly detection into robotics processes are discussed, with potential applications in autonomous driving, warehouse automation, and remote inspection. Finally, this research contributes to the development of robust robotic systems in real-world environments....
Background: Robotic-assisted navigational bronchoscopy (RNB) using the ION system (Intuitive Surgical, Sunnyvale, CA, USA) combined with cone-beam computed tomography (CBCT) (Cios Spin, Siemens Healthineers, Erlangen, Germany) and tool-in-lesion verification enables precise diagnosis of peripheral pulmonary nodules. Integrating RNB with intraoperative frozen section analysis may allow same-day resection, avoiding delays between diagnosis and treatment. Standard airway management with a single-lumen tube (SLT) limits immediate transition to lung resection, whereas initial double-lumen tube (DLT) placement could streamline workflow and improve safety. This study evaluated the diagnostic performance, procedural efficiency, and feasibility of an integrated ION-guided RNB workflow using either SLT or DLT. Methods: In this single-center retrospective study, 36 consecutive patients undergoing ION-guided RNB for pulmonary nodules between August 2024 and June 2025 were analyzed. Airway management (SLT vs. DLT) was selected based on surgical planning. Lesions were targeted using CBCT or C-arm fluoroscopy, and biopsies were performed via forceps or cryoprobes. Frozen section results guided immediate surgical resection when malignancy was confirmed. Results: Thirty-six patients (mean age 64.9 ± 7.9 years; female/male ratio 16/20) with 42 nodules (mean diameter 1.22 ± 0.76 cm) were included; 76.2% were peripheral. Mean RNB time was 58.3 ± 21.3 min. Overall diagnostic yield was 73.0%, significantly higher with DLT versus SLT (84.2% vs. 50.0%, p = 0.035), with more biopsies per patient (7.9 ± 2.2 vs. 3.2 ± 3.1, p = 0.035). No major complications occurred. Conclusions: ION-guided RNB with CBCT and intraoperative frozen section enables accurate, single-session diagnosis and treatment of pulmonary nodules. Upfront DLT placement facilitates procedural efficiency within a streamlined “one-stop-shop” workflow without compromising diagnostic yield....
Objectives: This study aimed to compare survival and outcomes between robotic-assisted and conventional sternotomy myxoma resection. Methods: This retrospective single-center study included 16 consecutive patients undergoing left atrial myxoma resection between April 2019 and June 2024. All procedures were performed by the same surgical team. The robotic approach involved peripheral cardiopulmonary bypass (CPB), Custodiol® cardioplegia, and DaVinci Xi® via right mini-thoracotomy. The primary endpoint was 30-day cerebrovascular accident-free survival. Secondary outcomes included 5-year survival, stroke, pacemaker implantation, bleeding, Intensive care unit, and hospital stay. Results: Sixteen patients were included (8 robotic, 8 sternotomy); median age was 58.0 [IQR 53.2–67.8] in the robotic group and 66.6 [62.0–71.0] years in the sternotomy group, with a similar sex distribution between groups. No significant baseline differences between groups except a lower EuroSCORE II in the robotic group (0.8% vs. 1.3%, p = 0.004). Robotic surgery resulted in significantly longer CPB time (181 vs. 46 min, p < 0.001) and cross-clamp time (67 vs. 31 min, p < 0.001), but similar intensive care unit stay (2.5 vs. 2.6 days, p = 0.95) and hospital stay (8.5 vs. 8.4 days, p = 0.87). At 30 days, stroke-free survival was 100% in both groups (p > 0.9). At 5 years, survival remained 100% in the robotic group versus 86% in the sternotomy group (p = 0.47). No conversions, reinterventions, or major postoperative complications were observed. Conclusions: Robotic-assisted resection of left atrial myxomas appears to be feasible and safe in a selected low-risk cohort, when compared with conventional sternotomy, with excellent mid-term survival despite longer operative times....
Mine rescue robots operate in extremely adverse subterranean environments, where the acquired video data are frequently affected by severe jitter and motion distortion. Such instability leads to the loss of critical visual information, thereby reducing the reliability of rescue decision-making. To address this issue, a dual-channel visual stabilization framework based on the SuperPoint network is proposed, extending the traditional ORB descriptor framework. Here, dual-channel refers to two configurable and mutually exclusive feature extraction paths—an ORB-based path and a SuperPoint-based path—that can be flexibly switched according to scene conditions and computational requirements, rather than operating simultaneously on the same frame. The subsequent stabilization pipeline remains unified and consistent across both modes. The method employs an optimized detector head that integrates deep feature extraction, non-maximum suppression, and boundary filtering to enable precise estimation of inter-frame motion. When combined with smoothing filters, the approach effectively attenuates vibrations induced by irregular terrain and dynamic operational conditions. Experimental evaluations conducted across diverse scenarios demonstrate that the proposed algorithm achieves an average improvement of 27.91% in Peak Signal-to-Noise Ratio (PSNR), a 55.04% reduction in Mean Squared Error (MSE), and more than a twofold increase in the Structural Similarity Index (SSIM) relative to pre-stabilized sequences. Moreover, runtime analysis indicates that the algorithm can operate in near-real-time, supporting its practical deployment on embedded mine rescue robot platforms.These results verify the algorithm’s robustness and applicability in environments requiring high visual stability and image fidelity, providing a reliable foundation for enhanced visual perception and autonomous decision-making in complex disaster scenarios....
The conventional component assembly techniques employed in manufacturing industries typically necessitate laborious manual parameter calibration prior to system deployment, while existing vision-based control algorithms suffer from limited adaptability and inefficient learning capabilities. This paper presents a novel framework for automated large-diameter peg-in-hole assembly through convolutional network-based perception and reinforcement learning-driven control. Our methodology introduces three key innovations: (1) an enhanced deep segmentation architecture for precise identification and spatial localization of peg-end centroids, enabling accurate preliminary peg-in-hole; (2) a hybrid control strategy combining deep deterministic policy gradient (DDPG) reinforcement learning with classical control theory, augmented by real-time force feedback data acquisition; (3) systematic integration of visual–spatial information and haptic feedback for robust error compensation. Experimental validation on an industrial robotic platform demonstrates the method’s superior performance, achieving an Intersection over Union (IoU) score of 0.946 in peg segmentation tasks and maintaining insertion stability with maximum radial forces below 5.34N during assembly operations. The proposed approach significantly reduces manual intervention requirements while exhibiting remarkable tolerance to positional deviations (±2.5 mm) and angular misalignments (±3°) commonly encountered in industrial assembly scenarios....
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