Search Results (898)

The use of industrial and collaborative robots in tasks requiring high precision places increasing demands on the evaluation of their performance. In practice, parameters such as positioning accuracy, repeatability and stability are typically assessed according to ISO 9283, based on repeated measurements and comparison of commanded and measured positions. This paper presents a measurement and analysis system developed for this purpose. The system combines selected measurement hardware with a software solution that covers the full workflow from data acquisition to result evaluation. A Python-based backend is used to handle communication with measuring devices, data processing and storage, while a web-based interface provides access to system control, real-time monitoring, and visualization of results. The separation of these components allows the system to remain stable even if the user interface is interrupted. Measured data are evaluated using statistical methods based on repeated measurements, with results presented in both numerical and graphical form. This approach simplifies interpretation and reduces the need for additional external tools. The proposed solution provides a practical and extendable framework for evaluating robot performance in laboratory as well as industrial conditions. Based on the obtained data, the robot’s performance can be evaluated in terms of pose accuracy and pose repeatability. In addition, robot parameters can be monitored and evaluated over an extended period, which allows the proposed solution to be used in predictive maintenance. This article primarily focuses on verifying pose accuracy, since these data were required by the robot user, who specified a minimum of 30 measurement repetitions. The maximum allowable deviation was ±0.01 mm. In the case of pose repeatability and drift of pose characteristics, the calculated value obtained from the measured data must not exceed ±0.02 mm, which is the value declared by the robot manufacturer. Full article

Background/Objectives: Ankle dorsiflexion plays a fundamental role in gait stability, impact absorption, and the stance-to-swing transition, and its impairment is a major limitation in transtibial prostheses. This study proposes and evaluates a lightweight two-stage pipeline for generating ankle-dorsiflexion references using a single shank-mounted inertial measurement unit (IMU). Methods: In the first stage, a deep neural network (DNN) estimates the shank pitch waveform from raw three-axis accelerations and angular velocities. In the second stage, the estimated shank pitch is transformed into an ankle-dorsiflexion waveform using a temporal mapping model. The approach was evaluated on a multisubject subset of the NONAN GaitPrint database comprising 35 healthy young adults, 598 walking trials, and approximately 122,468 gait cycles, using a strict subject-held-out protocol. Results: A feature-based Random Forest baseline showed limited performance, whereas the waveform-based DNN achieved high accuracy for shank pitch estimation, with test R² values up to 0.97. A conventional polynomial mapping between shank pitch and dorsiflexion yielded weak performance, whereas a temporal mapping model substantially improved the estimation of ankle dorsiflexion, with test R² values up to 0.85. The resulting ankle reference was integrated into a Gazebo/Robot Operating System 2 (ROS 2) simulation of a transtibial prosthesis, where the generated trajectories were executed in a software integration test under open-loop position control, confirming stable and consistent trajectory execution. Conclusions: These results indicate that combining accurate shank pitch estimation with temporal mapping enables feasible ankle-dorsiflexion reference generation from a single sensor in able-bodied gait, offering a preliminary, simulation-based pathway for single-sensor artificial intelligence (AI) pipelines in prosthetic development. The framework supports waveform-level feasibility, not clinical readiness or functional prosthetic control. Full article

Industrial modernization is widely regarded as an important pathway toward high-quality and sustainable economic development. Using panel data from 30 provinces in mainland China from 2012 to 2023, this study examines the relationship between artificial intelligence (AI) and industrial modernization. AI is proxied by industrial robot density, while industrial modernization is evaluated using a composite index covering supportiveness, substantiveness, innovativeness, greenness, openness, and integration. Fixed-effects models are employed, alongside a series of robustness tests and instrumental variable estimation. The results indicate that AI, as captured by industrial robot density, is positively associated with industrial modernization. This relationship remains robust after adopting alternative measures, introducing lagged explanatory variables and additional controls, applying winsorization, adjusting the sample, and addressing potential endogeneity. Heterogeneity analysis shows that the association is stronger in eastern provinces and in regions with higher levels of AI infrastructure and technical talent. Mechanism analysis suggests that green technology innovation is an important channel through which AI is associated with industrial modernization. In addition, software industry development strengthens the positive association between AI and industrial modernization, highlighting the importance of complementary digital capabilities in supporting industrial transformation. These findings contribute to understanding how AI adoption, represented by industrial robot deployment, is related to industrial modernization and suggest that policies promoting AI-driven industrial transformation should be accompanied by investments in green innovation, software industry development, digital infrastructure, and technical talent cultivation. Full article

This paper presents a control framework that integrates adaptive sliding mode control (ASMC), time-delay control (TDC), and admittance filtering to achieve robust force and position tracking in robot manipulators. TDC is employed to estimate unmodeled dynamics using delayed measurements, while ASMC enhances robustness by compensating for time-delay estimation (TDE) errors and mitigating chattering effects. An adaptive law incorporating a decline-rate reduction factor is introduced to explicitly regulate the decay of the adaptive gain inside the boundary layer, thereby preserving compensation capability against time-delay estimation errors and external disturbances for a longer duration while improving position tracking performance. In addition, the admittance mechanism converts force-tracking errors into position correction signals, enabling force tracking without modifying the underlying position control structure. The stability of the closed-loop system is analyzed based on Lyapunov theory, ensuring bounded tracking performance in the presence of estimation errors and uncertainties. Simulation results demonstrate that the proposed method improves position tracking accuracy—reducing the root mean square error (RMSE) from 0.0522 mm to 0.019 mm—while maintaining reliable force tracking performance. Full article

Robotic lower-limb exoskeletons are an increasingly important tool in the rehabilitation of patients with motor impairments, and their effectiveness depends on how faithfully the device reproduces the natural gait pattern. Inertial measurement units (IMUs) are widely used to acquire body-worn kinematic data for gait monitoring, but compact, interpretable models linking IMU-derived hip- and knee-flexion features to gait phase under exoskeleton-assisted conditions are still lacking. We collected gait data from two independent experiments: Experiment 1, 20 healthy adults (10 M, 10 F; 22.2 ± 1.9 years) walking freely on level ground, stairs and a ramp with seven Noraxon IMUs; and Experiment 2, six healthy adults (4 M, 2 F; 31.0 ± 8.9 years) walking with and without the Exowalk (HR-02) over-ground exoskeleton with five IMUs. Eight bilateral hip- and knee-flexion features were extracted, and a binary logistic-regression model with stance/swing as the dependent variable was fitted on Experiment 1 and externally cross-validated on Experiment 2. The model classified gait phases with an accuracy of 90.83% (sensitivity 87.50%, specificity 92.50%, positive predictive value 85.37%) on Experiment 1. External validation retained 91.7% accuracy during free walking but dropped to 41.7% under Exowalk-assisted walking, indicating that the device alters the inertial signature of gait. The findings identify swing-phase hip flexion and the minimum swing-phase knee flexion as the kinematic descriptors most predictive of gait phase, and provide quantitative design and control targets for next-generation IMU-instrumented lower-limb rehabilitation exoskeletons. Full article

Achieving safe physical interaction on the human back is challenging due to respiratory rhythms, complex topography, and varying tissue stiffness. To enable compliant force tracking within commercial closed position-control robot architectures, this paper presents an adaptive variable damping admittance control framework driven by multi-dimensional force sensor feedback. A stiffness-free admittance model is constructed to eliminate steady-state tracking errors, integrated with a nonlinear adaptive damping law that sensitively responds to real-time force sensor measurements. This mechanism rapidly dissipates dynamic impact energy during contacts while maintaining low impedance during steady state. Validated via a high-fidelity MATLAB R2024b-CoppeliaSim co-simulation platform replicating Traditional Chinese Medicine (TCM) manipulations, the proposed sensor-driven strategy significantly improves force tracking fidelity over traditional fixed-parameter control. Quantitative results demonstrate that across all complex therapeutic waveforms, the root mean square error (RMSE) remains below 0.42 N, the mean absolute error (MAE) is within 0.32 N, and the squared correlation coefficient ($r^2$) exceeds 0.97. These findings confirm the high efficiency and clinical potential of the proposed framework. Full article

Reliable indoor localization is required for automated guided vehicles (AGVs), robot validation, and industrial digital-twin applications, but ultrasonic positioning can degrade sharply when acoustic visibility changes. This paper evaluates Marvelmind Super-Beacon localization in controlled laboratory experiments involving both AGV tracking and UR10 robot-arm positioning. The non-inverse architecture (NIA) and inverse architecture (IA) configurations are included as parallel validation scenarios to assess the robustness of the proposed mitigation framework across different Marvelmind deployment modes. The baseline analysis identifies the dominant acoustic failure modes, including multipath-induced scatter, crossover-zone handover jumps, update-rate degradation, complete non-line-of-sight (NLoS) outages, and height-dependent 3D jitter. To mitigate these effects, an embedded ultrasonic–inertial pipeline is implemented on an ESP32-S3-WROOM-1 module. The system combines UART packet validation, interrupt-driven ICM-20948 inertial acquisition at 500 Hz, sliding-window kinematic outlier rejection, and a 15-state error-state Kalman filter (ESKF). The embedded estimator logic is designed to maintain motion continuity during intermittent or corrupted acoustic positioning while reintroducing validated ultrasonic absolute corrections. Using recorded AGV and UR10 datasets, mitigation performance was quantitatively assessed through a firmware-consistent replay of the recorded measurements, using the same gating, inertial propagation, and measurement-update logic as the real-time ESP32-S3 implementation. Across ten trials per configuration, the replay-based trial-mean RMSE in the 2D AGV scenarios decreased from 101.2–104.1 mm for raw ultrasonic data to 47.2–48.7 mm after fusion, while peak failure-interval errors were reduced by 64.2–65.7%. In the 3D UR10 scenarios, replay-based trial-mean RMSE decreased from 157.6–158.4 mm to 80.2–80.5 mm, and peak height-sensitive 3D errors were reduced by 58.8–60.0%. The results demonstrate the feasibility of embedded ultrasonic–inertial robustness enhancement for localization in controlled laboratory AGV and robot-arm scenarios. While the proposed approach shows promising performance under the investigated conditions, further validation is required before extending the conclusions to larger-scale and dynamically changing industrial environments. Full closed-loop online robot localization and control based directly on the fused localization output remain subjects for future investigation. Full article

Freehand three-dimensional (3D) ultrasound enables cost-effective volumetric breast imaging, but accurate reconstruction requires reliable probe tracking during manual scanning. This study proposes a portable freehand 3D ultrasound framework using dual-rotary-encoder two-degree-of-freedom (2DoF) pose sensing to measure probe displacement and inclination during breast scanning. A slip-resistant roller mechanism and time-aware trajectory modeling were introduced to improve measurement robustness under practical scanning conditions. The framework was evaluated through robotic experiments and phantom-based volumetric reconstruction. Positional displacement experiments achieved root mean square errors (RMSEs) of 0.38 mm on dry surfaces and 0.81 mm under gel-coated conditions. Inclination sensing using the rotary encoder outperformed an inertial measurement unit (IMU), achieving an RMSE of 2.76° with improved temporal stability. Reconstruction experiments using a breast phantom with spherical inclusions demonstrated successful volumetric visualization across multiple scanning trajectories. Statistical analysis revealed significant effects of inclusion size and scanning trajectory on relative reconstruction error, as well as a significant interaction between the two factors. Larger inclusions generally exhibited lower relative errors, while the influence of scanning trajectory depended on the target size. These findings support the feasibility of the proposed reduced-dimensional mechanical pose sensing approach for reliable freehand 3D ultrasound reconstruction with reduced hardware complexity. Full article

Source-Seeking in unknown scalar fields is a fundamental problem in robotics with applications in environmental monitoring and disaster response. In this work, we present a source-seeking approach with non-reversing forward velocity regulation by fusing measurement data from multiple sensors within the Stochastic Extremum Seeking (SES) framework. Specifically, a device model with multiple sensors is first constructed, and then a velocity regulation scheme is designed by leveraging the boundedness of the hyperbolic tangent function and the non-negativity of the exponential function to guarantee strictly positive forward velocity. We then evaluate the algorithm both in simulation environments and on the real-world Two-Wheeled Differential Drive Robot platform. The experiments show that our approach not only ensures the forward velocity remains non-negative, aligning with the design expectation, but also accurately locates the source. This work provides new insights into the design of velocity regulation strategies within the SES framework. Full article

This work addresses the design and implementation of an automated system for the handling and transportation of parts, integrating speed sensors, an optimized PID controller, an HMI interface, and an industrial robotic system. The speed sensors, powered by 5 V DC, enable continuous measurement of the conveyor belt’s speed and direction of rotation, providing the feedback signal required for the control loop. The core element of the system is the implementation of a PID controller applied to a direct current motor responsible for driving the conveyor belt. This controller regulates the motor speed by analyzing the error between the reference speed and the measured speed, using proportional, integral, and derivative actions to improve system stability, reduce steady-state error, and minimize oscillations. The application of PID control makes it possible to achieve an appropriate dynamic response, ensuring accuracy and reliability in the transportation process. System monitoring and operation are carried out through a human–machine interface (HMI) developed in LOGO Web Editor, which communicates with the PLC (LOGO V8) to visualize and control the status of the conveyor belt, sensors, and control elements in real time. This interface facilitates interaction between the operator and the system, allowing both virtual and physical operation. In addition, RAPID programming is used to control the IRB 14000 industrial robot, enabling the reading of PLC signals and the execution of coordinated trajectories between both arms. The operating sequence includes picking up a part with the left arm, placing it on the conveyor belt, and, after detection by sensors and PLC control, subsequent manipulation by the right arm to a specific point. Finally, both arms return to their original position, ensuring synchronized and collision-free operation. Lastly, this work integrates scientific knowledge related to the modeling, analysis, and control of dynamic systems, particularly in the implementation of closed-loop PID control optimized using genetic algorithms. This control is applied directly to an embedded system through the use of an Arduino board as the processing and control platform. Likewise, technological knowledge associated with industrial automation, PLC programming, HMI development, and industrial robotics is incorporated. The convergence of these scientific and technological approaches results in a comprehensive and compelling project that demonstrates the practical application of theoretical concepts in a functional automated system representative of real industrial environments. Full article

Reliable outdoor localization is essential for autonomous mobile robots, where the Global Navigation Satellite System (GNSS) is widely used to provide global positioning information. However, GNSS signals are often degraded in real-world environments due to occlusions, multipath effects, and environmental interference, leading to unstable localization and degraded navigation performance. This paper proposes an adaptive multi-sensor fusion framework for robust outdoor localization and path tracking under weak GNSS conditions. The proposed system integrates GNSS, LiDAR, wheel odometry, and inertial measurement unit (IMU) measurements within an Extended Kalman Filter (EKF) framework. To address the limitations of GNSS, an adaptive weighting mechanism is introduced to dynamically adjust the influence of GNSS observations based on signal quality indicators. Furthermore, a GNSS quality-aware mode-switching strategy is developed, enabling seamless transition between GNSS-dominant localization and multi-sensor fusion-based localization. In the fusion mode, LiDAR, odometry, and IMU jointly provide robust pose estimation, while GNSS acts as a weak global constraint. The IMU further enhances heading estimation, improving orientation stability and path tracking performance. The estimated pose is then used for trajectory tracking using a path-following controller. Experimental results conducted in outdoor environments demonstrate that the proposed framework significantly improves localization robustness and path tracking performance under degraded GNSS conditions. Compared with raw GNSS localization, the proposed method reduces the mean localization error by 47.2% and decreases the root mean square localization error by 55.5%, while maintaining smoother and more continuous trajectory estimation in weak GNSS environments. Full article

Background/objectives: Prostate cancer is the most common cancer in men over 60 years of age. The development of assisted robotic surgery has improved surgical performance across several variables in dynamic ways, introducing new reconstruction techniques. The present study aims to show differences between Retzius-sparing robotic-assisted prostatectomy vs. Retzius-sparing and posterolateral fascial reconstruction in patients diagnosed with localized prostate cancer. Methods: A retrospective study was performed in a 3-year time period by a single surgeon using the Da Vinci X platform. Two groups were assessed for the study, with and without posterolateral fascial reconstruction. Demographic data were analyzed with central tendency measures, and mean differences were calculated with the Mann–Whitney test and t-test, being significant if p < 0.05. Results: A total of 199 patients were included. The posterolateral reconstruction group had 81 patients, and outcomes saw similar performances to the non-reconstruction group. Urinary continence showed a positive trend of higher percentages in the first week after surgery but had similar results after one year, with no statistically significant differences. Oncologic results and sexual dysfunction showed no statistically significant differences between groups. Conclusions: Posterolateral reconstruction combined with Retzius-sparing radical prostatectomy demonstrated improved continence and was shown to be safe, without increasing overall complications such as bleeding. Full article

Few studies have examined robot voice, appearance, and ambient sound within a single framework to understand the user experience. To address this gap, this study explores how these three factors influence the user experience, specifically focusing on the appearance-voice matching effect, user likability, and user satisfaction when robots serve as museum docents, thereby offering design-oriented strategic insights for robot deployment in museums. Audio recordings of museum docents were collected and clustered into four categories. Accordingly, four distinct voice models were synthesized, and subjective user perceptions of these models were evaluated. Subsequently, three different agents, including an iPad, a self-designed robot prototype and a Yanshee robot, were employed as experimental platforms. These agents were each equipped with different voice models to conduct the experiments. The experimental data were analyzed by repeated measures ANOVA and mixed-model analysis. The results indicate that robot appearance significantly affects the appearance-voice matching effect, while its voice moderates this relationship in a museum scenario. Robot voice primarily drives user likability and satisfaction. In contrast, robot appearance shapes the two dimensions differently. Appropriate ambient sound significantly enhances the perceived appearance-voice congruence, and significantly affects naturalness and liveliness within user likability, as well as the fun of the experience and pleasantness of use within user satisfaction. The congruence can positively predict user likability and satisfaction, but this predictive effect has boundaries. These findings can provide references from the perspective of design strategies for human–robot interaction in museums. Full article

Robotic sorting-and-filling tasks in flexible manufacturing require robots to reproduce specified in-box arrangements while adapting to variations in container poses, object availability, sensing conditions, and external interventions. This paper proposes a box-centric one-shot teaching framework for robotic packing tasks with relative pose constraints. In the teaching stage, a human operator demonstrates the desired packing layout only once. The system uses reference-prompted SAM-based contour refinement to extract box and in-box object contours, object categories, quantities, and relative position and orientation constraints. These constraints are then converted from pixel-plane measurements into box-local pose constraints, forming a reusable box-centric packing template that preserves both translational and angular layout information. During execution, the recorded template is transferred to detected box instances with different global poses, and executable pick-and-place commands are generated through a task-level perception-to-command pipeline. A mechanism for continuous assignment and state updates is further introduced to maintain residual target slots, update object-to-slot allocation, and report missing or redundant objects across execution rounds. Single-box template transfer experiments achieved mean placement errors of 7.16 mm and 7.57 mm for two recorded templates, while representative post-execution images further showed that the relative object orientations were visually preserved with respect to the taught template footprints. Multi-box experiments demonstrated that unfinished residual slots could be preserved and completed after scene updates without re-teaching. Additional validation with different container types and object shapes showed the feasibility of extending the framework beyond cube-only cases. Ablation tests under nine exposure settings further showed that SAM refinement improved template-acquisition robustness compared with the previous recognition method. These results verify that the proposed framework enables one-shot template acquisition, box-centric layout transfer, relative pose preservation, and persistent task-level execution for constrained robotic packing tasks. Full article

The adoption of neoadjuvant immune checkpoint inhibitor (ICI)-based chemoimmunotherapy has fundamentally transformed the operative landscape of resectable non-small cell lung cancer (NSCLC). Surgeons are now routinely confronted with ICI-altered tissue planes characterized by hilar fibrosis, vascular friability, and disrupted lymph node architecture. Simultaneously, robotic-assisted thoracic surgery (RATS) has consolidated its position as the dominant minimally invasive platform for pulmonary resection, accounting for the majority of lobectomies and segmentectomies performed at high-volume centers in 2023. Whether RATS confers specific technical advantages in this increasingly complex operative context remains incompletely characterized. We conducted a structured narrative review of published evidence, synthesizing data from randomized controlled trials, prospective cohorts, national registry analyses, and emerging technology reports addressing RATS in the setting of neoadjuvant ICI-based therapy for NSCLC. A systematic literature search was conducted across PubMed and EMBASE using predefined search terms. Available evidence, though largely retrospective and limited by small sample sizes, consistently demonstrates that RATS after neoadjuvant chemoimmunotherapy is technically feasible and oncologically sound, with R0 resection achievable in virtually all cases. The enhanced three-dimensional visualization, tremor filtration, and instrument degrees of freedom afforded by robotic platforms appear particularly advantageous in the setting of dense hilar adhesions and fragile pulmonary vasculature. Lymph node yield, a recognized robotic advantage, is preserved or enhanced despite post-ICI fibrosis. Pooled conversion rates to thoracotomy, derived from post hoc surgical analyses of ICI trial populations rather than trials designed to measure conversion, are higher than for upfront resection; available retrospective single-center data, including one direct RATS-versus-VATS comparison, suggest lower conversion rates with RATS in experienced hands, though this conclusion requires prospective validation. Emerging platform integrations, including combined robotic bronchoscopy and thoracoscopic surgery, single-port systems, and artificial intelligence-assisted anatomical navigation, are poised to further extend the reach of minimally invasive surgery in this challenging clinical scenario. In experienced centers, RATS appears to offer a technically favorable minimally invasive platform for pulmonary resection after neoadjuvant ICI-based therapy, with potential advantages over VATS in managing immunotherapy-altered anatomy; however, this conclusion is derived from retrospective series and should be interpreted cautiously pending prospective comparative data. Prospective multicenter trials with standardized surgical endpoints are urgently needed. Full article