Search Results (81)

Background/Objectives: Most children and adolescents do not meet recommended physical activity (PA) levels. Artificial intelligence (AI) may enable personalized and adaptive interventions, but evidence across delivery formats remains unclear. This scoping review mapped AI-enabled PA interventions for children and adolescents aged 6–18 years. Methods: Following PRISMA-ScR and Joanna Briggs Institute guidance, six databases were searched on 24 May 2026 for English-language studies published since 2018. Eligible studies used AI in intervention delivery and empirically evaluated at least one participant-level PA-related outcome following exposure to the intervention. The review protocol was registered during data analysis. Results: Seven studies published between 2022 and 2026 were included. Primary AI techniques were computer vision (n = 3), generative AI or large language models (n = 2), reinforcement learning (n = 1), and evolutionary computation (n = 1). Interventions included immersive systems (n = 2), conversational agents (n = 2), exergames or serious games (n = 2), and a robot (n = 1). Three studies used controlled designs and four were single-arm pilot or feasibility studies. Conclusions: AI supported movement recognition, content generation, personalization, and adaptive feedback. However, the small, heterogeneous, and predominantly early-stage evidence base precludes conclusions about effectiveness. Rigorous controlled studies and transparent AI-specific reporting are needed. Full article

Robotic teleoperation is a core enabling technology spanning remote surgery, industrial inspection, and virtual-reality applications. Despite growing deployment, operator experience assessment still relies almost exclusively on post hoc subjective questionnaires, which preclude real-time monitoring and adaptive intervention. Here, the wearable IMU chain is considered not only as a command interface but also as an implicit sensing channel for operator state. We test whether end-effector kinematics generated by the IMU-to-robot mapping contain information about ten post-task workload and user-experience dimensions, comprising NASA-TLX-inspired workload scales together with usability, responsiveness, realism, intuitiveness, and perceived performance. A secondary analysis of a publicly available dataset (16 participants, 144 motion recordings, simulated UR10e arm) is conducted through a three-stage pipeline: bivariate correlation analysis (Pearson and Spearman), multivariate regression (10 model families, 16 feature-set combinations, Leave-One-Subject-Out validation), and binary classification (median-split). Statistical validity is assessed via 1000-permutation nested testing. Target-specific regression models reach $R^2\ge 0.50$ on seven out of 10 subjective dimensions, with a peak of $R^2=0.787$ for usability; permutation testing confirms significance for eight out of 10 targets. Binary classification achieves AUC $\ge 0.75$ on nine out of 10 targets, with three dimensions reaching perfect AUC. SHAP analysis identifies temporal irregularity and distributional shape descriptors as the dominant kinematic explanatory families. These results support the feasibility of kinematics-based inference of operator experience and provide an offline proof of concept toward future real-time adaptive teleoperation systems. Full article

Inspired by the energy-efficient resonance strategy of the human elbow joint during periodic arm swing, this paper investigates the energy-saving motion and performance of a robotic variable stiffness joint. A modular stiffness adjustment mechanism with continuously adjustable stiffness based on Archimedean spiral grooves is proposed. A co-simulation model using MATLAB (R2022b)/ADAMS (2020) is established, and dynamic equations are derived to reveal the correlation between resonance/anti-resonance frequencies and joint rotational stiffness. Mimicking the biological principle of stiffness-frequency matching, an energy-saving controller leveraging the resonance effect is designed, which includes a motor energy consumption model to quantify losses and an optimization strategy to match the joint rotational stiffness with the load anti-resonance frequency. Simulation results demonstrate that in variable stiffness mode, aligning the system anti-resonance frequency with the task trajectory frequency significantly reduces joint energy consumption, validating the bioinspired approach. In contrast, the high-stiffness (rigid) mode leads to a surge in system energy consumption. 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

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 continuous and reliable monitoring of soil organic carbon and soil respiration is vital for sustainable agricultural and environmental management. However, current manual methods are labor-intensive and time-consuming. This work focuses on the development of a fully automated robotic platform for in situ measurement of Soil Organic Carbon (SOC) and Soil Respiration (${\mathbf{R}}_s$). The system consists of a four-wheeled mobile platform, equipped with a robotic arm, and custom sampling and measurement tools. The platform is designed with a protected central opening that houses an on-board laboratory, enabling automated surface cleaning, soil drilling, sample collection and homogenization, SOC spectroscopy analysis, and chamber-based soil respiration measurement. The platform is equipped with a high-force mechanical insertion mechanism capable of operating a range of tools designed for soil treatment and penetration. These tools include a soil surface scraper, a soil respiration chamber, and a soil drilling unit. The mobile robotic laboratory system enables the sequential deployment of these tools in any desired order, providing flexible and efficient in-field operation. Full article

This paper presents a low-cost retrofitting pipeline for a legacy industrial robot that uses a single RGB webcam and monocular 2D keypoint tracking to estimate human-arm posture angles ${\mathit{\theta }}^{\left(\mathrm{h}\right)}$ and map them to robot-axis joint targets ${\mathbf{q}}_{\mathrm{cmd}}^{\left(\mathrm{r}\right)}$ for A1–A3 on a KUKA KR5-2 ARC HW, while keeping the wrist orientation (A4–A6) fixed. Rather than targeting full six-DoF manipulation, the main contribution is an experimental characterization of how far monocular 2D posture-to-axis mapping can be used reliably for coarse placement and safeguarded low-speed demonstrations on a legacy robot platform. Vision-side accuracy was evaluated per axis against goniometer-based reference angles ${\mathit{\theta }}_{\mathrm{ref}}^{\left(\mathrm{h}\right)}$, showing low errors for A2–A3 within the tested range and larger errors for A1 due to monocular yaw/depth ambiguity and occlusions. The study also analyzes failure modes during simultaneous multi-joint motion, where performance degrades notably, especially for A2 and A3, and reports practical mitigation directions such as improved viewpoints, multi-view/depth sensing, and stricter dropout handling. Runtime behavior is additionally characterized through a loop timing budget, with an end-to-end latency of 185.44 ms and an effective loop frequency of 5.39 Hz, which is consistent with low-speed online operation within the demonstrated scope. The system was implemented in a fenced industrial cell with restricted access and emergency stop; no collaborative operation is claimed. Full article

Accurate prediction of store trajectories at the point of release from an unmanned/manned aircraft is an essential requirement for safety and precision. Captive Trajectory System (CTS) is a well-known feature of wind-tunnel testing to simulate the dynamics of store separation. To accurately replicate real-world aerodynamic conditions based on measured forces and moments, it utilizes a six-degree-of-freedom (6-DOF) robotic arm controlled by a closed-loop control system that solves the store’s equations of motion. In this study, a wing–pylon–store configuration is used as a sample case, and published experimental trajectories are used as input. A 6-DOF robotic arm named ROBO-S is designed to follow these trajectories in a CTS setup. The kinematic analysis of ROBO-S is performed in this study. The Denavit–Hartenberg (DH) method is used for the calculation of forward kinematics, whereas geometric techniques are used for inverse kinematics calculations. A simulation of kinematic analysis is performed in MATLAB R2021a. The mechanical design of ROBO-S is carried out in PTC CREO 9.0. MATLAB simulations confirm that the robotic arm can follow the trajectory obtained from published experimental results. To demonstrate the feasibility of the design, the robotic arm is fabricated using 3D printing. The results demonstrate the potential of the developed system in accurately following trajectories for wind-tunnel testing applications. Full article

The integration of emerging technologies such as robotics and artificial intelligence (AI) has the potential to transform agricultural harvesting by improving efficiency, reducing waste, lowering labor dependency, and enhancing produce quality. This paper presents the development of an intelligent robotic berry harvesting system that combines deep learning–based perception with autonomous robotic manipulation for real-time strawberry harvesting. A computer vision pipeline based on the YOLOv11 segmentation model was developed and integrated into a Smart Mobile Manipulator (SMM) equipped with autonomous navigation, a 6-degree-of-freedom (6-DoF) xArm 6 robotic arm, and ROS middleware to enable real-time operation. Using a publicly available strawberry dataset comprising 2,800 images collected under ridge-planted cultivation conditions, the proposed YOLOv11-small segmentation model achieved 84.41% mAP@0.5, outperforming YOLOv11 object detection, Faster R-CNN, and RT-DETR in segmentation quality while maintaining real-time performance at 10 FPS on an NVIDIA Jetson Orin Nano edge GPU. A PCA-based fruit orientation and geometric analysis method achieved 86.5% localization accuracy on 200 test images. Controlled indoor harvesting experiments using synthetic strawberries demonstrated an overall harvesting success rate of 72% across 50 trials. The proposed system provides a general-purpose platform for berry harvesting in controlled environments, offering a scalable and efficient solution for autonomous harvesting. Full article

Background: Robotic-assisted thoracic surgery is increasingly recognized as an advanced minimally invasive technique for treating non-small cell lung cancer, offering technical advantages such as enhanced precision and visualization. Although numerous studies have been published worldwide, there are no comparable data from Poland. Therefore, evidence on the perioperative safety and oncologic adequacy of robotic-assisted lobectomy during early phase of program implementation within the Polish healthcare system remains limited. Methods: This retrospective, single-institution observational study included 81 consecutive patients who underwent robotic-assisted lobectomy for primary NSCLC between January 2022 and December 2024. All procedures were carried out using the da Vinci Xi system with a standardized four-arm portal approach. Clinical, perioperative, and pathologic parameters were prospectively collected and analyzed descriptively. Postoperative complications were classified according to Clavien-Dindo. Results: The median patient age was 70 years (IQR: 65–74), 52% were male, and 67% had a history of smoking. Adenocarcinoma was the predominant histologic subtype (51%). The median operative time was 176 min (IQR: 149–220). There were no conversions to thoracotomy and no 30-day mortalities. Postoperative complications occurred in 24% of cases, with prolonged air leak being most common (17%). The median hospital stay was 8 days (IQR: 6–10). R0 resection was achieved in 96% of patients, with a median of 14 lymph nodes dissected across 5 nodal stations. Conclusions: Robotic-assisted lobectomy performed during the early implementation phase of a national program demonstrated low morbidity, high rates of complete (R0) resection, and adequate lymph node yields consistent with international benchmarks. These results support the feasibility of robotic lobectomy within the Polish healthcare setting; however, the single-surgeon, single-center design limits generalizability. Further multicenter prospective studies are needed to confirm reproducibility, assess learning curves, and evaluate long-term oncologic outcomes. Full article

In recent years, autonomous liquid pouring systems have gained more relevance, with applications from daily service tasks to complex industrial operations. While seemingly simple for humans, this task poses major challenges for automated systems, as it requires precise control and adaptation to varying container geometries, liquid properties, and environmental conditions. This review examines the state-of-the-art on liquid pouring through five research questions: (1) What are the characteristics of the liquids used in the experiments? (2) What are the characteristics of the containers used in the experiments and how do they affect the performance of the pouring tasks? (3) What techniques are used to control liquid pouring (i.e., to control the robotic arm or device)? (4) What metrics are used to assess the methods for pouring liquid? (5) What devices are used to measure poured volume? This scoping review follows the Arksey and O’Malley framework, and uses the PRISMA-ScR protocol to filter the articles. A total of 285 studies published between 2018 and 2025 were screened from IEEE Xplore, SpringerLink, ScienceDirect, Web of Science, and EBSCOhost, of which 23 met the inclusion criteria. Results showed that the most widely used methods for autonomous liquid pouring were classical control methods—PID, PD (30.4% of the studies). Conversely, the least widely used methods for autonomous liquid pouring were learning, imitation learning, and probabilistic models (15% of the studies). Full article

Background: Total knee arthroplasty (TKA) is an effective procedure for symptomatic end-stage knee arthritis with good clinical and survivorship outcomes. However, up to 20% of patients report dissatisfaction following TKA. Recent studies have suggested that this may be at least partially due to suboptimal limb alignment or ligament imbalance. This study compared clinical outcomes at 1 year post-operatively (i.e., the 2011 Knee Society Score [KSS] and Forgotten Joint Score [FJS]) between two robotic-assisted personalised TKA techniques: functional alignment (FA) and an original technique combining restricted kinematic alignment (rKA) with a load sensor to achieve reliable ligament balancing (via bone re-cutting with a robotic arm). Methods: This single-centre, prospective, comparative study was performed at a robotic-assisted arthroplasty centre. The study population consisted of an FA group (43 patients) and rKA/sensor group (47 patients). Clinical outcomes were measured at 1 month post-operatively (visual analogue scale [VAS] pain score, flexion, range of motion [ROM], use of a mobility aid and stiffness) and at 1 year (2011 KSS, FJS, VAS, flexion and ROM). Results: There were no statistical significant differences in 2011 KSS or FJS at 1 year post-operatively between the two groups. Multivariate analysis showed no independent association of either technique with the 1-year follow-up KSS Objective Knee Indicators score (adjusted beta coefficient (aβ) = −2.371 [−7.380; 2.638], p = 0.357), KSS Patient Satisfaction score (aβ = −2.522 [−6.887; 1.842], p = 0.262), KSS Patient Expectations score (aβ = 0.629 [−0.928; 2.186], p = 0.431), KSS Functional Activities score (aβ = −3.399 [−10.881; 4.082], p = 0.377) or 1-year follow-up FJS (aβ = −5.168 [−19.887; 9.550], p = 0.494). Conclusions: There were no significant differences between the FA and rKA/load sensor groups in the 2011 KSS or FJS at 1 year post-operatively. To our knowledge, this is the first study to compare clinical outcomes between robotic-assisted FA TKA and rKA TKA. Clinical outcomes in the rKA/sensor group were similar to previous studies using rKA without robotic assistance or a load sensor. This was also the first report of the clinical outcomes of FA. The results need to be validated by larger scale studies to avoid potential type 2 errors. Full article

Surgical robots are increasingly utilized in medicine for their reliability and convenience. An accurate kinematic model is essential for precise robot control and enhanced surgical safety. However, the high nonlinearity and computational complexity of kinematics pose significant challenges to traditional numerical methods. This study designs a surgical robotic arm and establishes the motion mapping relationship between the joint space and the end-effector workspace. Subsequently, a hybrid kinematic estimation model based on deep pyramid convolutional neural network (DPCNN) is proposed, which integrates data sampling and an attention mechanism to improve computational accuracy. The Latin hypercube sampling technique is used to improve the uniformity of dataset sampling, and the triplet-fusion self-attention mechanism (TFSAM) is employed for multi-scale feature information. Experimental results show that the TFSAM-DPCNN model achieves coefficient of determination (R²) values exceeding 0.99 across all testing scenarios. Compared with other models, the proposed model reduced the root mean square error (RMSE) by up to 81.34%, exhibiting superior performance. Furthermore, the developed 3D simulation platform validates the effectiveness of the proposed model. This study offers a robust solution for multi-degree-of-freedom robot modeling, with potential applications across a range of robotic motion control systems. Full article

To address the problems of low success rate, excessively long obstacle avoidance paths, and a large number of invalid nodes in path planning for robotic arms in complex environments, this paper proposes an obstacle avoidance path planning method based on the Cauchy Chaotic African Vulture Optimization Algorithm (CC-AVOA). By introducing a Cauchy perturbation term, the algorithm retains a certain degree of randomness in the later stages of the search, which helps to escape local optima. Furthermore, the introduction of a logical chaotic mapping increases the diversity of the initial vulture population, thereby improving the overall search efficiency of the algorithm. This paper compares the performance of the CC-AVOA algorithm with the standard African Vulture Optimization Algorithm (AVOA), the Rapid Exploratory Random Tree (RRT) algorithm, and the A* algorithm through simulation experiments in MATLAB R2024a under two-dimensional, three-dimensional, and robotic arm space environments. The results show that the CC-AVOA algorithm can generate paths with fewer nodes and shorter paths. Finally, the CC-AVOA algorithm is validated on both the RoboGuide industrial simulation platform and a physical FANUC robotic arm. The planned trajectories can be accurately executed without collisions, further confirming the feasibility and reliability of the proposed method in real industrial scenarios. Full article

Background/Objectives: To determine the optimal site preparation and placement protocols for immediate implant positioning in robot-assisted surgeries. Methods: In vitro models of immediate and healed extraction sockets were created using 3D printing. A robotic system was used for implant site preparation and implant placement. The implant surgeries were allocated into eight experimental groups using 12 printed models in total. Each model incorporated two implant sites, an immediate site (tooth 21) and a healed site (tooth 26), resulting in 24 implants overall. With 3 implants assigned to each group, the 24 implant placements were evenly distributed across the 8 groups. For each group, the lateral force experienced during surgery was recorded by the haptic sensor on the robotic arm, and implant positional deviations were assessed by superimposing post-surgical CBCT images with the virtual implant planning. Results: Healed sites showed significantly higher accuracy than immediate sites, with reduced platform and apical deviations (p < 0.001) and markedly lower lateral force experienced by drills. In fully guided procedures, thread tapping greatly improved accuracy in immediate sites but had limited benefit in healed sites. Compared with partially guided workflows, fully guided rCAIS markedly enhanced accuracy in immediate sites (≈0.8 mm reduction in platform/apical deviation, p < 0.001), while no meaningful differences were observed in healed sites. Fully guided protocols also reduced insertion force in healed sites. Conclusions: Immediate sites showed lower implant positional accuracy and experienced higher lateral forces during surgery than healed sites. In immediate sites, thread tapping and fully guided rCAIS improved placement accuracy. Full article