Search Results (19,134)

In the field of robotic fault detection, although the random forest (RF) algorithm is widely adopted, its limited accuracy remains a critical constraint in practical engineering applications. To address this technical challenge, this study proposes a Two-Stages Random Forest (TSRF) algorithm. This approach constructs a hierarchical architecture with a dynamic adaptive weighting strategy, where the class probability vectors generated in the 1st-stage serve as meta-features for the 2nd-stage classifier. Such hierarchical optimization enables the model to precisely identify fault-sensitive features, effectively overcoming the performance limitations of conventional single-model frameworks. To validate the proposed approach, we conducted comparative experiments using a multidimensional kinematic feature dataset from hexapod robot joint fault detection. Benchmark models included geometry-feature-based RF and physics-informed RF as established baselines. Experimental results demonstrate that TSRF achieves a classification accuracy of 99.7% on the test set, representing an 18.8% improvement over standard RF. This significant advancement provides a novel methodological framework for intelligent fault diagnosis in complex electromechanical systems. Full article

Path planning is a complex task in robotics, requiring an efficient and adaptive algorithm to find the shortest path in a dynamic environment. The traditional path planning methods, such as graph-based algorithms, sampling-based algorithms, reaction-based algorithms, and optimization-based algorithms, have limitations in computational efficiency, real-time adaptability, and obstacle avoidance. To address these challenges, hybrid path planning algorithms combine the strengths of multiple techniques to enhance performance. This paper includes a comprehensive review of hybrid approaches based on graph-based algorithms, sampling-based algorithms, reaction-based algorithms, and optimization-based algorithms. Also, this article discusses the advantages and limitations, supported by a comparative evaluation of computational complexity, path optimization, and finding the shortest path in a dynamic environment. Finally, we propose an AI-driven adaptive path planning approach to solve the difficulties. Full article

Ant Colony Optimization (ACO) has been widely used in engineering implementation due to its simplicity and effectiveness. However, it often faces challenges such as slow convergence, susceptibility to local optima, and generating paths with excessive turning points. To address these limitations, this paper introduces a Novel Multi-Strategy Enhanced Ant Colony Optimization algorithm (NMS-EACO) for mobile robot path planning under nonholonomic constraints. NMS-EACO integrates five key strategies: an A*-guided heuristic function, an adaptive enhanced pheromone update rule, a state transition probability under nonholonomic constraints, a smoothing factor embedded in the state transition probability, and a global path smoothing technique. Comprehensive simulation experiments are conducted across six distinct map types, with comparisons made against six existing algorithms through extensive trials.Results demonstrate that NMS-EACO significantly improves convergence speed, enhances global search capability, and reduces path irregularities. These results validate the robustness and efficiency of the proposed multi-strategy method for nonholonomic mobile robot navigation. Full article

Reliable and fast communication between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) is essential for effective coordination in agricultural settings, particularly when human involvement is part of the system. This study systematically compares two communication architectures representing centralized and decentralized communication frameworks: (a) MAVLink (decentralized) and (b) Farm Management Information System (FMIS) (centralized). Field experiments were conducted in both empty field and orchard environments, using a rotary UAV for worker detection and a UGV responding to intent signaled through color-coded hats. Across 120 trials, the system performance was assessed in terms of communication reliability, latency, energy consumption, and responsiveness. FMIS consistently demonstrated higher message delivery success rates (97% in both environments) than MAVLink (83% in the empty field and 70% in the orchard). However, it resulted in higher UGV resource usage. Conversely, MAVLink achieved reduced UGV power draw and lower latency, but it was more affected by obstructed settings and also resulted in increased UAV battery consumption. In conclusion, MAVLink is suitable for time-sensitive operations that require rapid feedback, while FMIS is better suited for tasks that demand reliable communication in complex agricultural environments. Consequently, the selection between MAVLink and FMIS should be guided by the specific mission goals and environmental conditions. Full article

The final phase of the manufacturing process for any artefact involves their surface finishing operations. This phase entails the precise removal of small volumes of material to achieve a specific surface roughness, which is essential for ensuring the artefact’s post-production performance and endurance. For certain tooling, such as molds and dies, the finishing operation can be particularly significant, often equating to fifty percent of the total production time and a fifth of the overall manufacturing cost. In recent years, collaborative robotics has come to the fore. These advanced systems allow manufacturers to harness the positive attributes of robots, such as their repeatability, endurance, and strength, while simultaneously leveraging the unique benefits of human workers, including their process knowledge, problem-solving abilities, and adaptability. This co-operation between human and robotic capabilities has opened new avenues for efficiency and precision in the finishing process. This paper investigates the current advancements in collaborative robotic finishing, providing a comprehensive overview of the latest technologies and methodologies. It also highlights existing research gaps that need to be addressed to further enhance the effectiveness of these systems. Additionally, the paper suggests potential areas for future investigation, aiming to drive continued innovation and improvement in the field of collaborative robotic finishing operations. Full article

As digitalization reshapes industrial ecosystems, small and medium-sized enterprises (SMEs) in resource-based economies face growing pressure to adapt. This study examines the digital maturity of supplier firms within Chile’s Antofagasta mining cluster, a region that plays a central role in national productivity. A structured survey was conducted with 83 companies, using a ten-dimensional model to assess key areas such as data management, processes, personnel, and technology use. Results show that the average maturity level is 2.5 on a five-point scale, placing most firms at an early stage of digital transformation. While data-related capabilities scored relatively high, critical gaps persist in automation, robotics, and cybersecurity. Company size was moderately correlated with digital maturity, but no consistent relationship was observed with revenue growth. Although most firms acknowledge the relevance of digital technologies, few have formal plans or strategies in place. These findings reveal a structural lag that limits the potential of SMEs to engage fully with Industry 4.0, underscoring the need for tailored support policies and collaborative development initiatives in the mining sector. Full article

Background/Objectives: Balance, postural control, and functional independence are essential components for the autonomy of people with neurological conditions. Robotic technologies such as the Lokomat^® have emerged as promising tools in rehabilitation, but their effectiveness when integrated into functional programs requires further evidence. The objective of this study was to evaluate the impact of an intensive robotic intervention on these three functional variables. Methods: A single-group, quasi-experimental pretest–posttest study was conducted with 136 participants who received a robotic rehabilitation intervention using the Lokomat^® device, and focused on functional tasks over several weeks. Balance (using the Berg scale), postural control (using the PASS), and functional independence (using the Barthel index) were assessed, comparing pre- and post-intervention results using parametric and non-parametric tests. Results: The results showed statistically significant improvements in all three variables after the intervention. The mean Berg score increased from 11.76 to 21.91 points (p < 0.001), postural control increased from 15.53 to 21.90 points (p < 0.001), and the Barthel index increased from 24.71 to 41.76 points (p < 0.001). In all cases, the effect sizes were large (d > 0.90). Conclusions: A rehabilitation program including intensive, task-oriented Lokomat^® training was associated with improvements in balance, postural control, and functional independence. Given the single-group design without a control arm, these findings reflect associations and do not establish causality. Full article

In addressing the limited absolute positioning accuracy of industrial robots, which stems from the discrepancy between the nominal kinematic model and the physical entity, this paper proposes a novel paradigm for online compensation based on data-driven error prediction. The present study utilized a KUKA KR4 R600 robot as the experimental platform to construct a high-fidelity digital twin system capable of real-time synchronization. Within this framework, a new machine learning model, termed the Global Configuration-Error Forest (GCE-Forest), was developed and validated. The fundamental principle of GCE-Forest, based on the Random Forest algorithm, is its offline learning of the complex, highly non-linear mapping from the robot’s six-dimensional joint space configuration to its three-dimensional end-effector Cartesian error space. This facilitates online, feedforward, and predictive compensation for the nominal trajectory during robot operation. Through rigorous comparative experiments, the superiority of the proposed GCE-Forest was established. The final outcomes of dynamic trajectory tracking validation demonstrate that the system, by accurately predicting a mean nominal error of 0.1977 mm, successfully reduced the average spatial positioning error of the end-effector to 0.0845 mm, achieving an accuracy improvement of 57.25%. This research provides comprehensive validation of the method’s robust performance, offering a low-cost, non-invasive, and highly effective solution for significantly enhancing robotic accuracy. Full article

Manifold learning is a significant computer vision task used to describe high-dimensional visual data in lower-dimensional manifolds without sacrificing the intrinsic structural properties required for 3D reconstruction. Isomap, Locally Linear Embedding (LLE), Laplacian Eigenmaps, and t-SNE are helpful in data topology preservation but are typically indifferent to the intrinsic differential geometric characteristics of the manifolds, thus leading to deformation of spatial relations and reconstruction accuracy loss. This research proposes an Optimization of Manifold Learning using Differential Geometry Framework (OML-DGF) to overcome the drawbacks of current manifold learning techniques in 3D reconstruction. The framework employs intrinsic geometric properties—like curvature preservation, geodesic coherence, and local–global structure correspondence—to produce structurally correct and topologically consistent low-dimensional embeddings. The model utilizes a Riemannian metric-based neighborhood graph, approximations of geodesic distances with shortest path algorithms, and curvature-sensitive embedding from second-order derivatives in local tangent spaces. A curvature-regularized objective function is derived to steer the embedding toward facilitating improved geometric coherence. Principal Component Analysis (PCA) reduces initial dimensionality and modifies LLE with curvature weighting. Experiments on the ModelNet40 dataset show an impressive improvement in reconstruction quality, with accuracy gains of up to 17% and better structure preservation than traditional methods. These findings confirm the advantage of employing intrinsic geometry as an embedding to improve the accuracy of 3D reconstruction. The suggested approach is computationally light and scalable and can be utilized in real-time contexts such as robotic navigation, medical image diagnosis, digital heritage reconstruction, and augmented/virtual reality systems in which strong 3D modeling is a critical need. Full article

This work introduces a comprehensive vision-based framework for autonomous space debris removal using robotic manipulators. A real-time debris detection module is built upon the YOLOv8 architecture, ensuring reliable target localization under varying illumination and occlusion conditions. Following detection, object motion states are estimated through a calibrated binocular vision system coupled with a physics-based collision model. Smooth interception trajectories are generated via a particle swarm optimization strategy integrated with a 5–5–5 polynomial interpolation scheme, enabling continuous and time-optimal end-effector motions. To anticipate future arm movements, a Transformer-based sequence predictor is enhanced by replacing conventional multilayer perceptrons with Kolmogorov–Arnold networks (KANs), improving both parameter efficiency and interpretability. In practice, the Transformer+KAN model compensates the manipulator’s trajectory planner to adapt to more complex scenarios. Each component is then evaluated separately in simulation, demonstrating stable tracking performance, precise trajectory execution, and robust motion prediction for intelligent on-orbit servicing. Full article

The primary objective of the presented study is to develop a comprehensive system for the acquisition of surface electromyographic (sEMG) data and to perform time–frequency analysis aimed at extracting discriminative features for the classification of hand gestures intended for the control of a simplified bionic hand prosthesis. The proposed system is designed to facilitate precise finger gesture execution in both prosthetic and robotic hand applications. This article outlines the methodology for multi-channel sEMG signal acquisition and processing, as well as the extraction of relevant features for gesture recognition using artificial neural networks (ANNs) and other well-established machine learning (ML) algorithms. Electromyographic signals were acquired using a prototypical LPCXpresso LPC1347 ARM Cortex M3 (NXP, Eindhoven, Holland) development board in conjunction with surface EMG sensors of the Gravity OYMotion SEN0240 type (DFRobot, Shanghai, China). Signal processing and feature extraction were carried out in the MATLAB 2024b environment, utilizing both the Fourier transform and the Hilbert–Huang transform to extract selected time–frequency characteristics of the sEMG signals. An artificial neural network (ANN) was implemented and trained within the same computational framework. The experimental protocol involved 109 healthy volunteers, each performing five predefined gestures of the right hand. The first electrode was positioned on the brachioradialis (BR) muscle, with subsequent channels arranged laterally outward from the perspective of the participant. Comprehensive analyses were conducted in the time domain, frequency domain, and time–frequency domain to evaluate signal properties and identify features relevant to gesture classification. The bionic hand prototype was fabricated using 3D printing technology with a PETG filament (Spectrum, Pęcice, Poland). Actuation of the fingers was achieved using six MG996R servo motors (TowerPro, Shenzhen, China), each with an angular range of ${180}^{\circ }$, controlled via a PCA9685 driver board (Adafruit, New York, NY, USA) connected to the main control unit. Full article

Background: To compare the costs of open retropubic radical prostatectomy (RRP), robotic-assisted radical prostatectomy (RALP), intensity-modulated radiation therapy (IMRT), low-dose brachytherapy (LDBT), stereotactic body radiotherapy (SBRT), cryotherapy (Cryo), and high-intensity focused ultrasound (HIFU) for low/intermediate-risk prostate cancer (PCa), from the healthcare system perspective. Methods: This retrospective, IRB-approved study compared the costs and charges of primary treatment options for localized PCa at Duke University Hospital between January 2018 and December 2019. We identified cases by querying the relevant disease, procedural, and charge codes from Duke Finance. Consecutive cases with NCCN high-risk disease, prior treatment, or missing institutional financial information were excluded. Costs were calculated from the point at which the treatment option was selected until the last treatment session (SBRT and IMRT) or hospital discharge (other modalities). All modalities except RRP were considered technology-intensive. Results: A total of 552 patients with a mean age of 65.0 years met the inclusion criteria. NCCN risk categories included 85 (13%) low, 218 (41%) favorable-intermediate, and 249 (46%) unfavorable-intermediate risk cases. RALP, RRP, Cryo, and HIFU were single-session treatments, whereas IMRT, SBRT, and LDBT were delivered over multiple sessions. IMRT and SBRT were the most expensive modalities, followed by RALP, HIFU, LDBT, Cryo, and RRP. The number of sessions (ρ = 0.55, p < 0.001) and being technology-intensive (ρ = 0.58, p < 0.001) were significantly correlated with treatment costs. Conclusions: In this cohort of PCa patients, treatment costs were highest for IMRT and SBRT, followed by RALP, HIFU, LDBT, Cryo, and RRP. The number of treatment sessions was a significant predictor of higher costs. Full article

Background/Objectives: Minimally invasive spine surgery (MISS) is complex and requires proficiency with a variety of technological and robotic modalities. Acquiring these skills is a long and involved process, often with a steep learning curve. This paper seeks to characterize the state of MISS training in neurosurgical and orthopedic residency programs, focusing on their effectiveness at minimizing substantial learning curves in the field, as well as highlighting potential areas for future growth. Methods: We conducted a scoping review of the PubMed, Scopus, and Embase databases utilizing the PRISMA extension for scoping reviews. Results: Of the 100 studies initially identified, 16 were included in our final analysis. MISS training types could be broadly grouped into four categories: virtual simulation (including AR and VR), physical models, hybrid didactic and simulation, and mentored training. Training with these modalities led to improvements in resident performance across multiple different MISS techniques, including percutaneous pedicle screw fixation, MIS dural repair, MIS-TLIF, MIS-LLIF, MIS-ULBD, microscopic discectomy/disk herniation repair, percutaneous needle placement, and surgical navigation. Specific improvements included reduced error rate, operation time, and fluoroscopy exposure, as well as increased procedural knowledge, accuracy, and confidence. Conclusions: The incorporation of MISS training modalities in spine surgery residency leads to increases in simulated performance and could serve as a means of overcoming significant learning curves in the field. Full article

Background: Dissection around the superior mesenteric artery (SMA) is a key step for local clearance of periampullary cancers in pancreaticoduodenectomy (PD). Since the 2000s, SMA-first approaches have gained popularity in open surgery to allow early vascular control and resectability assessment. With the rise of robotic pancreaticoduodenectomy (RPD), various SMA dissection techniques have been adapted to the robotic setting. Objective: To map current evidence on SMA dissection techniques in RPD and summarize technical variations. Eligibility Criteria and Sources of Evidence: A PubMed search identified 116 records. After title and abstract screening and full-text review, 27 studies focusing on SMA dissection for periampullary tumors in RPD with sufficient technical detail were included. Studies on open/laparoscopic PD, lacking technical description, or reporting duplicate techniques were excluded. Charting Methods: Data were charted based on the SMA approach type, surgical details, and institution. Results: Among the 27 included studies, multiple approaches were identified—anterior, right posterior, left posterior, uncinate, and mesenteric—each adapted to the robotic platform. Techniques varied in exposure, lymphadenectomy, and vessel control. Conclusions: This scoping review reveals diverse SMA dissection strategies in RPD. While technical innovation is progressing, further studies are warranted to standardize approaches and assess their oncologic and surgical outcomes. Full article

In recent years the use of delayed controllers has increased considerably, since they can attenuate noise, replace derivative actions, avoid the construction of observers, and reduce the use of extra sensors, while maintaining inherent insensitivity to high-frequency noise. Therefore, it is important to continue improving the tuning of these controllers, including properties such as performance, fragility and robustness that may be beneficial for this purpose. However, currently most studies prioritize tuning using only the performance property, some others only the fragility property, and some less only the robustness property. This work provides the first rigorous joint analysis of performance, fragility, and robustness for a class of systems whose characteristic equation is a quasi-polynomial of degree two, filling a gap in the current literature. Thus, necessary and sufficient conditions are proposed to improve the tuning of delayed-action controllers by ensuring a exponential decay rate on the convergence of the closed-loop system response (performance) and by ensuring stabilization and/or trajectory tracking in the face of changes in system parameters (robustness) and controllers gains (fragility). To illustrate and corroborate the effectiveness of the proposed theoretical results, a real-time implementation is presented on a mobile prototype consisting of an omnidirectional mobile robot, to streamline/guarantee trajectory tracking in response to variations in controller gains and robot parameters. This implementation and application of theoretical results are possible thanks to the proposal of a novel delayed nonlinear controller and some simple but strategic algebraic manipulations that reduce the original problem to the study of a quasi-polynomial of degree 9 with three commensurable delays. Finally, our results are compared with a classical proportional nonlinear controller showing that our proposal is relevant. Full article