Search Results (413)

This article examines the medium to long-term enrollment patterns of an AI-based platform designed to support children in learning robotics and participating in a national robotics competition in Romania. Drawing on registration and participation data covering students and teachers across urban and rural schools between 2020 and 2025, the study documents a consistent pattern: an initial period of high enrollment and rapid adoption followed by a steady decline over time. A key feature of the initiative is that hardware, platform access, and learning resources were provided entirely free of charge, allowing cost-related explanations for the decline to be set aside and structural and human factors to be examined directly. The paper makes two primary contributions. First, it proposes a System Dynamics framework grounded in innovation diffusion theory as a first-generation calibration model for understanding AI-enabled educational robotics adoption in a resource-constrained national context. The model is designed to be progressively tested and refined as anonymized aggregate data accumulates, and it relies exclusively on anonymized aggregated public data in accordance with GDPR requirements. Second, it advances the hypothesis that an AI-based educational platform, even one from which all financial barriers have been removed, will experience sustained enrollment decline in the absence of adequate human teacher involvement. The empirical trajectory and model outputs are consistent with this hypothesis and motivate further investigation. This represents a hypothesis-generating and framework-building paper. The framework reveals pronounced urban-rural disparities and differential outcomes by age of entry. All findings are presented as model-generated hypotheses rather than empirically demonstrated conclusions. The paper invites researchers gathering comparable data from similar initiatives in other countries to collaborate in testing and refining the model. The central conclusion is cautiously optimistic: AI may support robotics education adoption, but it is not a substitute for dedicated teachers, and without sustained investment in human capital, even a financially accessible platform is insufficient to maintain long-term enrollments. Full article

Dual 6 degrees of freedom (6-DOF) series-parallel cooperative robot actuators are core execution components in modern intelligent manufacturing systems, which are widely used in high-end manufacturing scenarios such as aerospace precision assembly, laser precision machining, and core component assembly of new energy vehicles. However, in actual manufacturing processes, the pose deviation between theoretical model prediction and actual motion execution of the actuator, caused by kinematic model mismatch, unquantified core parameters, incomplete error processing chain, and complex on-site environmental interference, severely restricts the assembly accuracy, product qualification rate and production efficiency of the manufacturing system. To address these critical pain points of robot actuators in precision manufacturing systems, this paper proposes a four-layer hierarchical decoupling multi-modal fusion calibration algorithm for high-precision pose control of dual series-parallel robot actuators. The algorithm integrates singular value decomposition (SVD) for cross-structure coordinate alignment of heterogeneous actuators, chaotic mapping-enhanced particle swarm optimization (PSO) for nonlinear error suppression of the actuator system, attention-enhanced deep residual network (DRN) for unmodeled residual learning of the actuator, and Kalman filter (KF) for dynamic noise reduction in the manufacturing process. Meanwhile, a full-chain error transfer model of the actuator system in the manufacturing process is constructed, and the core parameters of the algorithm are quantified via dimensional sensitivity analysis and orthogonal experiments. Experimental results show that the static position error of the actuator system after calibration reaches 1.4 ± 0.08 mm, and the static pose error reaches 0.0059 ± 0.0003 rad in the laboratory environment; in the engineering application of laser precision machining in an actual manufacturing line, the position error and pose error only increase by 8.6% and 6.8% respectively, maintaining high stability in industrial manufacturing scenarios. Compared with mainstream calibration methods, the proposed algorithm reduces the position error and pose error of the actuator by up to 55.7% and 17.9% respectively, with lower computational complexity and higher engineering reproducibility. This work constructs an end-to-end error suppression chain with quantitative parameter criteria for the series-parallel actuator system in manufacturing systems, which provides a reliable high-precision calibration solution for industrial dual-robot cooperative manufacturing and has important guiding significance for improving the motion accuracy and operation stability of actuators in precision manufacturing systems. Full article

Robot control based on visual information perception has been a hot topic in the field of industrial robots, and the use of visual servoing technology to guide robots for high-precision spatial localization of machined workpieces has a wide range of application value. Aiming at the camera hand–eye calibration error and robot repositioning error, which have a large impact on the spatial localization and navigation accuracy, and when the binocular camera Z-direction accuracy is not high enough and the viewing angle is limited, etc., we propose a spatial visual servoing algorithm based on an orthogonal vision system that combines an eye-in-hand camera and an eye-to-hand camera in a hybrid configuration. By extracting sub-pixel image features in real time and deriving directionally decoupled interaction matrices, a linear controller is designed to guide the robot in the XY-plane and Z-direction separately. This decoupling strategy enlarges the convergence domain, avoids local minima caused by coupled degrees of freedom, and enhances system stability. To this end, the intrinsic calibration and hand–eye calibration of two cameras placed orthogonally are carried out firstly, and the accuracy of hand–eye calibration is not too demanding; then the sub-pixel level image position of the target is extracted in real time and the interaction matrix is derived and a linear controller is designed to control the robot’s motion; finally, the experiments of spatial localization accuracy are completed on the KUKA iiwa to validate the effectiveness of the method. Full article

Multimodal sensor fusion is increasingly used to improve observability for fault diagnosis and prognostics, enabling Remaining Useful Life estimation in complex mechatronic and robotic systems. Yet, real-world deployments remain vulnerable to sensor faults and data integrity issues—including bias and drift, miscalibration, dropouts, saturation, cross-talk, time desynchronization, and domain shift—which can propagate through fusion pipelines and lead to optimistic validation and poor generalization. These challenges are particularly consequential in safety- and health-adjacent applications such as collaborative robots, wearable/rehabilitation devices, and human-centric mechatronic systems where decisions based on faulty sensing may affect both reliability and user safety. This review synthesizes the state of the art on (i) sensor fault taxonomies and fault models relevant to multimodal fusion, (ii) fault-aware fusion strategies spanning data-, feature-, and decision-level integration, and (iii) how sensor faults and uncertainty impact diagnosis and remaining-life estimators. We will conduct a systematic scoping review of peer-reviewed literature, extracting sensor modalities, fault characterization or injection protocols, fusion architectures, validation settings (simulation, hardware-in-the-loop, bench, and in-field/on-body studies), and reporting completeness. Beyond summarizing methods, we provide practical reporting standards for sensor-fusion-based diagnosis and prognostics, including a minimum disclosure set covering synchronization, fault ground truth, missingness handling, leakage controls, uncertainty calibration, and task-relevant metrics. Reusable checklists and evidence tables are included to support more comparable, reproducible, and deployment-ready research. Full article

To overcome the inherent limitations of conventional offline programming in adapting to dimensional deviations and assembly-induced errors during robotic welding of ship structures, this paper proposes a point-cloud-enhanced visual scanning paradigm that enables automatic weld seam identification and collision-free trajectory planning. A dedicated monochromatic vision system is rigidly integrated onto a six-axis industrial robot, enabling high-fidelity feature extraction and geometric contour reconstruction for the precise localization of multi-configuration weld seams. The proposed approach substantially reduces manual teaching operations, enhances environmental adaptability in unstructured shipbuilding workshops, and improves global positioning accuracy. The core technical contributions are threefold: (1) systematic design and precision calibration of the integrated robotic vision system, including a hand–eye calibration procedure; (2) development of a hybrid 2D image-3D point cloud processing pipeline that combines SURF and FLANN for image stitching with RANSAC-based plane segmentation and PCA-driven contour reconstruction; and (3) extensive experimental validation across five distinct workpiece configurations. These results confirm the system’s strong applicability for intelligent and efficient shipbuilding welding, significantly outperforming conventional offline programming, which exhibits deviations exceeding 5 mm under identical conditions. Quantitative error analysis demonstrates that the online recognition method achieves a weld localization root mean square error (RMSE)of 0.82 mm, a standard deviation of 0.45 mm, and a verified maximum absolute deviation of 1.5 mm. Full article

Experimental validation delivers five quantified outcomes. First, optical pheromone detection achieves 88.7% ± 0.6% accuracy (n = 150, 95% CI), and the dual-modality combined channel achieves 86.1% ± 0.9% (n = 200), with robustness confirmed under 50/60 Hz flicker interference, rapid 200–1200 lux light transitions (485 ms settling), and reflective glare spots. Second, the MQ-135 chemical channel calibration holds R² ≥ 0.999 across temperatures of 15–35 °C and humidity of 30–90%, with maximum voltage drift of 0.093 V at the highest temperature. Third, 3.2× CNN inference speedup through 8-bit quantisation runs at 15 FPS within 1.8 W. Fourth, peripheral subsystems draw a measured mean of 1.19 W ± 0.02 W (n = 60, 95% CI); the complete per-robot system, including the Jetson Orin Nano compute rail, draws 6.15 W ± 0.09 W, enabling six-hour missions from the 55.08 Wh battery. Fifth, localisation across ten trials yields the mean position error 0.074 m and RMSE 0.081 m with 97.5% map coverage; physical multi-robot tests with 5–8 robots confirm map convergence times of 120–210 steps with collision rates below 0.042 per robot per step. To the best of our knowledge, no prior physical swarm platform has simultaneously demonstrated this combination of capabilities under comparable constraints. Full article

Stroke is the second leading cause of death globally and a major contributor to lower-limb disability, affecting gait, balance, and functional independence in elderly populations. While robot-assisted rehabilitation has demonstrated effectiveness in motor recovery, access remains limited due to high costs and geographic barriers, particularly in Latin America. This study presents ExoKnee, a low-cost knee exoskeleton designed through biomimetic principles and 3D-printed fabrication as a proof-of-concept device targeting gait rehabilitation in elderly adults. The system integrates a single-degree-of-freedom pneumatic actuator controlled by electromyography (EMG) signals from the quadriceps muscle, enabling knee flexion and extension (90° to 180°). The design was evaluated through finite element analysis and dynamic simulations in MATLAB/Simulink R2024a under constant, stepwise, and sinusoidal reference inputs in a digital-twin environment. Expert validation using the Content Validity Coefficient yielded a mean score of 0.8747, reflecting preliminary expert agreement on the conceptual design’s coherence and relevance. The prototype demonstrated controlled movements through a 6-bar pneumatic system with EMG-triggered relay activation, validated at the proof-of-concept level through simulation and single-subject threshold calibration. ExoKnee addresses critical gaps by offering an anthropometrically informed, biosignal-driven, and locally manufacturable rehabilitation platform for low- and middle-income countries, pending clinical validation. Future work will focus on clinical trials and adaptive EMG control strategies. Full article

Conventional industrial manipulators are often costly and come with steep learning curves, which limits their scalability in hands-on robotics education. This paper presents a compact and modular vision-guided sorting platform based on a 4-DOF SCARA robot, designed for rapid assembly, reconfiguration, and beginner-friendly deployment in laboratory courses. A collaborative visual perception strategy is proposed, which introduces a lightweight YOLOv8 algorithm for robust material category recognition, while HSV-based color segmentation and Hough circle localization are utilized to extract sub-pixel centroid features. The pixel measurements are mapped to the robot base frame through an integrated nine-point hand–eye calibration model, and joint commands are generated via a joint-space quintic polynomial interpolation algorithm to ensure continuity and avoid kinematic singularities. The overall system adopts a hierarchical architecture in which the vision host communicates target commands to a motion controller via TCP/IP, while joint actuators are driven through a CAN bus. Feasibility is first verified in a Webots digital prototype with synchronized conveyor and manipulator control, and is then validated on a physical platform equipped with a compliant TPU-based soft gripper to improve grasp tolerance under localization noise. Experiments demonstrate that the system achieves an average recognition accuracy of 98.1% and a mean positioning error of 0.189 mm. The proposed platform provides an extensible testbed for teaching kinematics, perception-to-control integration, and modular robotic system development. Full article

Magnetometers are widely used in robotics and localization systems but are susceptible to magnetic disturbances generated by nearby ferromagnetic objects, which degrade their accuracy. Traditional calibration methods often fail in dynamic environments, such as those encountered by mobile robots. This paper investigates a dipole model-based disturbance compensation method using a magnetometer array with increased sensor density, extending prior configurations with fewer sensors. The method leverages a detection system to locate disturbing objects, models them as magnetic dipoles, and estimates their parameters through optimization. Experimental validation was performed using magnetic fingerprints of metallic objects in multiple configurations. The results show that increasing sensor density significantly improves compensation performance, reducing magnetic field errors to below 6.64 μT and heading errors to 0.31 rad in most scenarios. In low-to-moderate disturbance scenarios, the four-sensor array achieved heading error improvements of approximately 13% compared to the uncompensated case. In contrast, the proposed nine-sensor array achieved improvements exceeding 50%. In highly complex scenarios involving multiple overlapping disturbances, performance degrades, highlighting limitations of the dipole-based model. These results indicate that increasing sensor density enhances robustness and suggest that adopting compact array geometries may further improve performance in highly disturbed scenarios. Full article

With the continuous advancement of industrial intelligence, the application of hydraulic robots is becoming increasingly widespread, and the demand for their health diagnosis and maintenance is becoming more urgent. By integrating digital twin (DT) and deep learning technologies, this paper presents an intelligent fault diagnosis method for hydraulic robots based on multi-source information fusion. Firstly, a fault diagnosis architecture and solution for hydraulic robots based on DT technology are proposed. Secondly, a DT model of the hydraulic robot, which incorporates a 3D model and an attribute model with virtual–physical synchronization capabilities, is established, and a calibration method for the twin model is explored. Next, for four typical faults—leakage in the hydraulic system, valve sticking, damping hole blockage, and filter blockage—fault mechanism analysis and evolution process simulation are conducted on the established DT model. A multi-source high-quality dataset, covering normal operating conditions and multiple fault scenarios, is constructed to drive the data twin model. Finally, a feature extraction method combining Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention mechanisms is proposed. This is followed by using a Random Forest (RF) classifier to achieve accurate fault diagnosis for various hydraulic system failures. The experimental results validate the effectiveness and practicality of this method. Full article

With the continuous growth of global population and increasing pressure on food security, the transformation toward precise and intelligent agricultural production has become an inevitable trend. In this context, accurate identification of field weeds is crucial for improving crop yields and reducing agricultural inputs. However, agricultural Internet of Things (IoT) edge devices are generally subject to strict constraints in terms of power consumption, storage, and real-time performance. Existing lightweight convolutional neural networks often struggle to simultaneously achieve high accuracy and low resource consumption for fine-grained weed identification tasks. To address this challenge, this paper proposes a hardware aware lightweight convolutional neural network named Weed-Aware Ghost Enhanced Network (WAGENet) for microcontroller deployment. The network synergistically integrates Ghost low-cost feature generation, Mobile Inverted Bottleneck Convolution (MBConv) for deep semantic extraction, Squeeze and Excitation (SE) and Coordinate Attention (CA) dual attention mechanisms for channel space joint calibration, and Atrous Spatial Pyramid Pooling (ASPP) for multi-scale context fusion. It constructs a progressive feature abstraction system from shallow textures to high-level semantics. On the public DeepWeeds dataset, WAGENet achieves 95.71% classification accuracy and 93.80% F1 score with only 0.163 M parameters and 2.43 × 10⁸ multiply accumulate operations (MACC), attaining a parameter efficiency of 587.19%/M and significantly outperforming existing mainstream lightweight models. The model has been successfully deployed on the STM32H7B3I microcontroller development board, achieving a single inference latency of 94.63 ms, an internal Flash footprint of only 686.95 KiB, and a single inference energy consumption of 41.45 mJ. Experimental results demonstrate that WAGENet achieves a trade off among accuracy, latency, and energy consumption under strict resource constraints, providing a reproducible microcontroller deployment paradigm for battery powered field robots, drones, and other agricultural IoT edge devices. Full article

Hybrid stepper (HB) motors are widely used in precision actuation systems such as cryogenic refrigerator robotic arms. Under cryogenic working conditions, the core loss characteristics of magnetic materials change significantly, while conventional core loss models calibrated at room temperature can hardly provide reliable prediction accuracy. In this work, the electromagnetic properties of 35SW1900 non-oriented silicon steel were measured from 25 °C − 100 °C using a BROCKHAUS Epstein frame system. Variations in permeability, core loss and coercivity with magnetic flux density, temperature and frequency were obtained. An improved core loss model was developed by introducing a flux-dependent exponent and dual temperature correction coefficients for hysteresis and eddy current losses. Experiments place the prediction error of the proposed model within 4% under cryogenic conditions. Compared with the classical Bertotti model, the proposed model effectively reduces high-frequency deviation caused by the temperature-dependent material properties and skin effect. The core loss of silicon steel increases by 15–30% at −100 °C compared with room temperature, which is mainly attributed to the decrease in resistivity and the strengthening of domain wall pinning. This paper provides an accurate loss prediction method and design references for HB motors applied in ultralow temperature working conditions. Full article

This paper proposes an adaptive residual extended Kalman filter method optimized by a multi-strategy improved parrot optimization algorithm (MIPO-ARKEKF) to improve the kinematic parameter calibration accuracy and efficiency of robotic polishing systems. To address the limitations of the standard extended Kalman filter (EKF), such as truncation-error accumulation during repeated linearization and sensitivity to manually selected noise parameters, an integrated improvement framework is developed. Specifically, a gradient stabilizer based on state-estimation increments is introduced to alleviate estimation degradation caused by accumulated truncation errors, while the proposed MIPO algorithm is employed to adaptively optimize the process and measurement noise covariance matrices, thereby improving the robustness of parameter identification under practical measurement uncertainty. The calibration process is established on the basis of high-precision external measurement data obtained from the robotic polishing system. In benchmark-function tests, MIPO demonstrates superior convergence performance. In physical experiments based on a KUKA KR210 R2700 robot, the proposed MIPO-ARKEKF method reduces the root mean square positioning error from 0.8927 mm to 0.4858 mm, corresponding to a 45.58% improvement in accuracy. Compared with representative hybrid calibration methods, the proposed method achieves comparable compensation accuracy while reducing computation time by 34.88% to 65.08%. Practical polishing experiments on ultra-low-expansion glass lenses further verify that the proposed method effectively improves end-effector trajectory tracking accuracy and polishing quality, providing an efficient solution for high-precision robotic polishing. Full article

Electrotactile feedback is a compact approach for providing tactile cues in robotic teleoperation, but personalized calibration remains time-consuming because tactile perception varies across users. To address this problem, this study develops a subject-informed multi-layer finite element model of fingertip electric-field distribution coupled with a neural-response model and proposes a simulation-derived configuration-ranking method termed the Perceived Correctness Score (PCS). A gradient boosting regression model is then used to recommend among 36 candidate electrode diameter–spacing combinations. Validation was conducted using a custom-developed 3 × 2 multi-channel fingertip electrotactile stimulation system in a shape/area recognition task involving six healthy subjects. The predicted PCS showed a moderate positive correlation with the measured mean recognition accuracy across configurations (Pearson r = 0.48, p < 0.05). The model achieved Top-1 exact matching for three of six subjects and Top-5 coverage for five of six subjects. Compared with conventional exhaustive psychophysical calibration, the proposed method reduced the average configuration time from 122.7 min to 16.0 min, corresponding to an efficiency improvement of 87.0%. These results show that model-guided ranking can substantially reduce the burden of individualized electrotactile configuration. Full article

Additive manufacturing enables the development of low-cost, self-built robotic systems; however, their performance is typically not characterized by validated metrics. The paper presents a photogrammetric concept intended for system-independent application to characterize planar positioning accuracy and repeatability without access to internal controller data. The method is based on a Raspberry Pi 4 camera system, image processing in Python 3.12.0 and OpenCV 4.12.0, and a universal additively manufactured robot tool attachment. Two position estimation strategies are investigated: a marker-based approach using ArUco markers and a markerless blob-analysis method based on a ruby sphere. Camera calibration is evaluated using different patterns, with a compact CharUco board exhibiting the lowest RMS reprojection error (~1 px). Experimental validation follows selected elements of ISO 9283:1998 and comprises 30 repetitions at five target poses for linear and axial motion strategies. The results show lower positional deviations for marker-based methods compared to the markerless approach, with a two-marker configuration yielding the lowest mean deviation under the investigated conditions. Sub-millimeter positioning accuracy and repeatability are achieved, and linear motion exhibits lower repeatability deviations than axial motion. The proposed approach provides a cost-effective and flexible solution for external robot characterization, particularly suited for self-built and resource-constrained systems. Full article