Search Results (1,353)

The Joint Clinical Assessment (JCA) evaluates the relative effectiveness (RE) of interventions over comparators. While randomised control trials (RCTs) are considered the gold standard, single-arm trials (SATs) require an external control for accurate RE estimation. This study reviewed Health Technology Assessment (HTA) outcomes for medicinal products supported by SATs in France, Germany, Poland, and Spain, and simulated the JCA for these products based on evidence submitted in France. Among HTA evaluations published in France in 2019–2024, 16% were SAT-driven, and 5.6% of them included external controls. SAT-supported drugs had a high reimbursement approval rate (74%) and showed better HTA outcomes when controls were used. In Germany, 64% of SAT-based HTA outcomes indicated no added benefit and 30% a non-quantifiable benefit. In Poland and Spain, 63% and 72% HTA evaluations recommend reimbursement, respectively. Despite wide acceptance by Member States, experts determined that 94% of SAT-supported products would not qualify for JCA review due to insufficient evidence. Only 6% would qualify for JCA for a likely limited number of PICOs (Population–Intervention–Comparator–Outcome), but the certainty rating would be low. These findings suggest that SATs, as primary evidence, may not be suitable for JCA, potentially undermining HTA in EU Member States. Full article

Industrial robotic arms are fundamental components of modern automated production lines, executing critical tasks such as welding, painting, and assembly. Such high-precision operations often require careful manual tool positioning during the initial setup. To automate and refine this process, a highly accurate kinematic model of the robot is essential. In this paper, the authors propose a novel algorithm for kinematic parameter calibration using photogrammetry to track multiple robot links simultaneously. The proposed multi-link calibration approach provides a more precise parameter estimation and introduces the practical possibility of continuous parameter refinement while the robot executes its primary operational tasks. The superior accuracy and robustness of the proposed methodology are confirmed through comprehensive simulation experiments, and the feasibility of the approach is successfully demonstrated on a real robotic arm. Full article

This article presents the development and proof-of-concept testing of a vision-based safety monitoring tool for operators in simulated robotic cells in RoboDK. The proposed method uses a virtual camera placed above the cell and image processing techniques to analyze the relationship between the operator and the workspace swept by the robot. In an initial stage, the robot movement is recorded as a mask of the swept space, and areas irrelevant to the process can be excluded by user-defined polygons. In the monitoring stage, the operator is identified in the video stream by HSV segmentation, after which an adjustable clearance zone is generated around the detected contour. Based on the intersections between the operator, clearance, swept space mask and the mask of the current robot movement, the application provides four discrete states: SAFE, WARNING, DANGER and COLLISION. For the experimental validation in the virtual environment, the virtual contact moment is estimated separately, while the COLLISION state is defined as the intersection between the inflated operator contour and the current robot motion mask. Therefore, in this study, COLLISION does not indicate measured physical contact, but an image-based imminent-collision condition used for early warning. The test scenario was carried out in a virtual palletizing cell, which includes an articulated arm robot, conveyors, manipulated objects and a mobile dummy acting as an operator. The obtained results support the use of the method as an applicative simulation solution for the evaluation of the early detection of risk situations. The study is limited to the virtual environment and represents a basis for future research on the development of visual monitoring systems to increase safety in collaborative and industrial robotic cells. Full article

In this article, a fuzzy payload compensation algorithm is proposed. In the context of simulating a machine vision model reconstruction, the target object is regarded as a cylinder to obtain the corresponding geometric size data. The first fuzzy mass prediction system is then used to predict the mass of the target object. During operation, real-time processing and calculation of the robotic arm’s joint motor current data are performed. Based on the mathematical relationship between the identified basic parameter set from the dynamic parameters and the end-effector payload, the second fuzzy compensation system was used to calculate the root mean square error (RMSE) of the predicted versus collected current data of the 6-th joint motor, thereby predicting and compensating for the payload mass. The final prediction is generated upon completion of the operation. The overall experiment is conducted on the HSR-CR607 robot. The experimental results indicated that the proposed prediction algorithm consistently operates within the acceptable error range (15%) in most test cases. Full article

Handover control in human–robot collaboration remains a significant challenge. This paper proposes a three-step vision-based human–robot handover system (VHS). Vision inputs are used to perceive the environment and enable adaptive control of the robotic arm. Moreover, a three-step behavior cloning learning strategy is designed. Furthermore, a modified Temporal Difference (TD) loss function based on transfer models is proposed to train the algorithm to improve policy exploration and convergence. The proposed method results in substantial enhancements in comparative experimental validation in a simulation environment with a realistic dynamic hand model. Full article

This study proposes a canopy-adaptive TAD-IRRT* (target-biased sampling, artificial potential field, and dynamic step-size informed rapidly-exploring random tree star) algorithm to solve the collision-free 3D path-planning problem for a 6-DOF apple-harvesting robotic arm. To improve computational speed and search directionality, the method integrates target-biased sampling and a distance-regulated artificial potential field (APF) into the Informed-RRT* framework. Furthermore, an obstacle-distance-based dynamic step-size mechanism is introduced to optimize spatial exploration. The generated routes undergo greedy path pruning and cubic B-spline smoothing to ensure kinematic executability. The simulation results in complicated ROS-based scenarios demonstrate that the TAD-IRRT* algorithm achieves a 100% planning success rate, reducing the average computational time and joint-space path length by approximately 60.1% and 15.6%, respectively, compared to the standard Informed-RRT*. Kinematic analysis via Fourier curve fitting ($R^2=0.9849$) confirms continuous angular velocity and acceleration without high-frequency chattering. Physical prototype experiments in the dense-obstacle scenarios show that the proposed method increases the path execution success rate by 36.7% and reduces the average execution time by 41% compared to the standard Informed-RRT* algorithm. The proposed approach effectively balances high-quality path generation with low computational overhead, providing a reliable and safe solution that significantly reduces mechanical wear. Full article

The dual-arm cooperative operation mode can effectively address the problems of insufficient load capacity and limited motion flexibility of traditional single-arm robots during the installation of construction boards. However, the selection of the end-effector grasping position of dual-arm robots will significantly affect their motion performance during handling operations. To address this issue, this study proposes an end-effector grasping strategy for sheet installation in the dual-arm cooperative operation mode of a dual-arm robot, which determines the optimal grasping position to ensure the robot’s good operational performance. We developed a dual-arm robot prototype for board installation and established a kinematic model of the robot’s manipulators. Based on the dexterity index’s service sphere, we obtained the dexterity envelope surfaces of the robot end-effector at different grasping distances and analyzed the relationship between grasping distance and dexterity. The mechanical model of the robot was established, and simulations were performed for each joint. The effects of different grasping points on the torque, stiffness, and stability at the robot’s key points were investigated, and the end-effector grasping range of the robot with optimal mechanical performance was analyzed. Finally, the proposed robot grasping strategy was verified on the robot prototype. The results demonstrate that the strategy is feasible and effective, helping to improve the robot’s operational performance. Full article

This study presents an integrated framework for enhancing the safety and operational efficiency of robotic arms in laparoscopic surgery by addressing minimum distance estimation between multi-arm manipulators and the associated collision-aware warning. By combining analytical modeling, real time simulation, and machine learning, the framework offers a robust solution for ensuring safe robotic operations. An analytical model was developed to estimate the minimum distances between robotic arms based on their joint configurations, offering theoretical calculations that serve as both a validation tool and a benchmark. To complement this, a 3D simulation environment was created to model two 7 DOF Kinova robotic arms (Kinova Inc., Boisbriand, QC, Canada), generating a diverse dataset of configurations for distance estimation and collision warning. Using these insights, a deep residual neural network model was trained with joint configurations as inputs. On the held out validation set, the model achieves $R^2=0.940$, RMSE $=42.0$ mm, MAE $=28.7$ mm, and a near zero mean bias, demonstrating strong predictive accuracy and consistent generalization across the workspace. The framework is intended as an early collision warning layer, where a warning is triggered when the predicted inter-arm distance falls below a 0.2 m threshold, which corresponds to a surface to surface clearance of approximately 50 mm given the Kinova Gen3 (Kinova Inc., Boisbriand, QC, Canada) cross sectional radius. This work demonstrates the effectiveness of combining analytical modeling with machine learning to enhance the precision and reliability of multi-arm robotic systems. Full article

The present work investigates fluid–structure instabilities and flow-induced oscillations of a pitching NACA0012 airfoil through numerical simulations. The flow is modeled using the compressible Navier–Stokes equations in a non-inertial rotating reference frame, while the structural dynamics are represented by a torsional spring–mass–damper system. The analysis focuses on the effects of reduced velocity, equilibrium angle of attack, and elastic axis position on the aeroelastic behavior at low Reynolds number ($Re=1000$). Particular attention is devoted to characterizing the transition from vortex-shedding-dominated oscillations to fully developed limit-cycle oscillations and to assessing its sensitivity to aerodynamic and structural parameters. The results show a transition from steady flow to vortex shedding and, at higher reduced velocities, to limit-cycle oscillations. Increasing the equilibrium angle of attack promotes an earlier onset of instability and stronger aerodynamic forcing, while moving the elastic axis downstream has a similar destabilizing effect due to the larger aerodynamic moment arm (up to approximately 20% reduction of the critical reduced velocity). The nature of the transition is found to depend strongly on the equilibrium angle of attack, with distinct behaviors observed at low and high incidence. Frequency analysis highlights the progressive coupling between fluid and structural dynamics: vortex shedding dominates in the weakly coupled regime, whereas the structural frequency governs the response in the limit-cycle regime. The study provides a consistent description of the mechanisms driving flow-induced oscillations and of the parameters controlling aeroelastic stability. Full article

This exploratory study compares the Froude efficiency (η_F), intra-cyclic speed fluctuation (ICSF) and other performance determinants between two freestyle swimming techniques: double-arm backstroke and front crawl, and then demonstrates how Para swimmers with hypertonia differ from non-disabled swimmers when performing double-arm backstroke. Three-dimensional motion analysis was undertaken on three Para swimmers with hypertonia (sport classes 3–4) and eight non-disabled swimmers performing a simulated double-arm backstroke with lower limbs immobile. The non-disabled group also completed front crawl trials. Swimming speed, stroke frequency, stroke length and η_F were significantly greater, and ICSF significantly lower, during front crawl than during double-arm backstroke in non-disabled swimmers. Para swimmers’ double-arm backstroke speed was 45–52% that of the non-disabled group; their stroke length was 58–69% shorter and stroke frequency 26–53% higher. Non-disabled swimmers demonstrated higher peak elbow extension velocity during the push phase than Para swimmers (6.36 ± 1.26 rad∙s⁻¹ vs. 1.50–1.81 rad∙s⁻¹) and their η_F was approximately double the Para swimmers’ (0.33 ± 0.02 vs. 0.14–0.18). Para swimmers displayed poorer body alignment than the non-disabled group; ICSF did not differ between groups. Double-arm backstroke is slower and less efficient than front crawl. Hypertonia may reduce the efficiency of double-arm backstroke by diminishing propulsive movements and worsening body orientation. Full article

Resolving the kinematic redundancy of 7-DoF humanoid arms to generate natural, human-like motions remains a fundamental challenge in biomimetic robotics. This paper presents a hybrid inverse kinematics (IK) framework that learns a pose-dependent redundancy parameter and integrates it into a differential IK solver. Specifically, we employ the stereographic Shoulder–Elbow–Wrist (SEW) angle as a well-conditioned geometric parameterization. This formulation transforms the algorithmic singularity into a unidirectional half-line, which can be oriented outside the typical reachable workspace. To specify the optimal configuration within the self-motion manifold, a motion dataset was collected by teleoperating a humanoid arm via an anthropomorphic wearable exoskeleton. This approach translates operator-specific postural preferences into the robot’s joint space. A lightweight neural network was then trained to learn the mapping from end-effector poses to these operator-specific SEW angles. By incorporating the predicted SEW angle as a dynamic secondary objective in the null space of the primary tracking task, the proposed framework enables natural redundancy resolution while preserving end-effector tracking accuracy. Both simulations and real-robot experiments were conducted to validate the approach. Results show that, compared to the average performance of static fixed-parameter strategies, the proposed method improves the Joint Configuration Quality Index (CQI) by 22.5% and reduces energy costs by 11.3%. Moreover, the sub-millisecond inference latency (0.44 ms) facilitates seamless integration into real-time control pipelines. Full article

In hybrid-series-converter-based LCC-HVDC systems, controllable capacitor modules can provide additional voltage–time area during commutation, thereby improving inverter-side fault tolerance under AC faults. However, their switching behavior makes the commutation path impedance state-dependent, while most existing commutation-failure prediction methods still rely on fixed-reactance assumptions. To address this problem, this paper proposes a reactance-corrected predictive control and coordinated switching method. First, a capacitor switching coefficient is introduced to describe the insertion state of the controllable capacitor modules, and an equivalent commutation reactance of the HSC valve arm is derived. Then, the corrected reactance is incorporated into an extinction-angle margin index and an energy-margin index to quantify the influence of reactance variation on commutation capability. A segmented firing-angle controller with smooth compensation is further designed, and energy-margin feedback is coordinated with capacitor insertion control. PSCAD/EMTDC simulations verify that the proposed method reduces prediction error, provides a prediction lead time of 0.7–4.5 ms, and improves fault ride-through capability. Full article

Unmanned aerial vehicles (UAVs) are widely used for monitoring and payload transport; however, their application in autonomous physical interaction remains limited due to payload constraints, stability challenges, and the complexity of integrating manipulation systems. This study presents the design and development of a lightweight foldable robotic arm based on the ten-bar Kempe Kite Inversor II linkage for UAV aerial manipulation. The mechanism generates precise straight-line motion using a single degree of freedom. Kinematic modeling and simulation validated a maximum end-effector reach of approximately 0.42 m. Structural optimization using additive manufacturing and honeycomb cellular architectures significantly reduced system weight while maintaining mechanical reliability. A passive compliant gripper, counterbalance mechanism, onboard storage net, and landing gear were integrated to evaluate the arm in a practical harvesting scenario using cherries as the test object. The final integrated system weighs 0.351 kg during operation, remaining approximately 16% below the experimentally determined UAV payload limit of 0.4185 kg. Proof-of-concept flight demonstrations confirmed successful aerial grasping of cherries, validating the feasibility of the proposed lightweight manipulation approach for agricultural applications. Full article

To address the challenges of combinatorial explosion and expensive evaluations in truck–drone (truck–UAV) collaborative delivery under complex geographical constraints, this paper proposes a Surrogate-assisted Rezone-Enhanced Multi-objective Adaptive Evolutionary Algorithm (SRE-MAEA). As a knowledge-driven decomposition-based surrogate-assisted framework, the proposed algorithm aims to synergistically optimize a four-dimensional conflicting objective space consisting of economic cost, social satisfaction, environmental emissions, and battery resilience. To overcome the curse of dimensionality in high-dimensional and strongly constrained environments, SRE-MAEA constructs an adaptive Rezone Search architecture. By dynamically deconstructing the decision space, it transforms global search pressure into refined knowledge mining within high-potential local regions. The core mechanism incorporates an intelligent sampling strategy based on the Multi-Armed Bandit (MAB). By utilizing real-time evolutionary feedback to dynamically prioritize the Pareto contribution of each rezone, the MAB achieves pruning-level scheduling of expensive evaluation resources. Simulation results on 15 benchmark instances with clustered, random, and mixed spatial distributions demonstrate that SRE-MAEA exhibits superior convergence boundaries and distribution uniformity in terms of IGD and HV metrics, significantly outperforming state-of-the-art regression-based strategies. Furthermore, computational efficiency analysis confirms that by precisely identifying invalid search paths via the MAB mechanism, SRE-MAEA maintains a high-precision Pareto front while reducing the average CPU time by approximately 35.2–48.5%. This effectively resolves the computational bottleneck caused by complex battery resilience integral models. This research provides an efficient algorithmic paradigm for resilient logistics scheduling in extreme environments and holds significant academic value and engineering application prospects. Full article

Existing ground-slotted coplanar waveguide (CPW) spoof surface plasmon polariton (SSPP) low-pass filters (LPFs) remain constrained by the difficulty of achieving a wide stopband while maintaining a compact size, as well as by undesired radiation leakage arising from their open-aperture slot configuration. To address these issues, a grounded coplanar waveguide spoof surface plasmon polariton (GCPW-SSPP) low-pass filter based on a multi-arm star-shaped slot (MASS) loading topology is proposed. An equivalent-circuit interpretation and full-wave dispersion analysis show that the multi-arm slots introduce enhanced distributed reactive loading, thereby lowering the asymptotic frequency and enabling compact SSPP implementations. The near-field characteristics further demonstrate tighter electromagnetic confinement, as reflected by an approximately 48% reduction in the electric-field confinement width along the z-direction. To alleviate the trade-off between miniaturization and wide-stopband performance in cascaded SSPP LPFs, the single-cell S-parameters of the proposed topology are investigated. A single MASS unit exhibits a sharp cutoff and a deep transmission notch, allowing a wide stopband to be obtained with fewer cascaded cells. Radiation characteristics are subsequently quantified by a loss-decomposition method, and the MASS topology is found to suppress the radiation leakage of open-aperture ground-slotted structures, yielding a maximum radiation-loss reduction of approximately 75%. To validate the design methodology, a MASS-loaded GCPW-SSPP LPF is designed, fabricated, and measured. The measured results are in good agreement with the simulated ones, confirming the effectiveness of the proposed scheme. By simultaneously achieving a wide stopband, compact size, and suppressed radiation leakage, the proposed filter offers a promising low-interference filtering solution for highly integrated microwave and RF front-end systems. Full article