Search Results (2,892)

Robust manipulation of small, densely stacked objects remains a challenging problem due to severe occlusions and geometric ambiguities, particularly under single-view sensing conditions. When observed using a single RGB-D sensor, adjacent surfaces of featureless cuboid objects, such as Jenga blocks, often merge in depth measurements, making reliable instance separation and pose estimation difficult. This paper presents a strategy-driven perception and manipulation framework for the robotic rearrangement of randomly stacked Jenga blocks under single RGB-D sensor constraints. The proposed approach employs a heightmap-based perception pipeline that integrates color filtering with geometric reasoning to segment individual blocks and estimate manipulation-compatible poses. Beyond perception, the proposed system determines robot actions through a structured manipulation policy consisting of region-wise search for directly executable grasps, grasp candidate evaluation based on accessibility and collision risk, selective local regrasping for workspace reconfiguration, and placement mode selection between direct insertion and sliding-assisted placement. In this framework, controlled grasp-and-release actions are applied only when no directly executable candidate is found within the currently scanned region and a suitable recovery target can be identified, thereby transforming cluttered local arrangements into more executable states without requiring additional sensing modalities. Experimental results, conducted under competition-equivalent conditions, demonstrate a high task success rate of 99.02%, confirming the robustness and reliability of the proposed framework. The results show that strategy-driven manipulation can effectively compensate for perception limitations in single RGB-D sensor environments, enabling stable and efficient pick-and-place operations in dense clutter. 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

The majority of currently available hand kinematic databases have been gathered using expensive marker-based systems or are restricted to a particular gesture-recognition task, failing to capture the dynamic nature of joints when the hand is engaged with an object. To address this gap, we introduce the RGB-based Hand Joint Kinematics (RGB-HJK) dataset, a publicly available collection of continuous, frame-level 3D joint angle trajectories, recorded while ten healthy adults (six male, four female; age $25.8\pm 3.2$ years; BMI $22.8\pm 2.0$ kg/m²) performed five standardized object interaction grasps: Power Grasp (cylindrical bottle), Tripod Grasp (pen), Static Power Hold (smartphone), Precision Pinch (thin paper), and Lateral Pinch (book). Data were collected using a standard RGB camera and the MediaPipe Hands markerless pipeline at $26.95\pm 0.29$ Hz, a rate that was stable across all subjects. Each participant completed five trials for each grasp type. After filtering using active hold, 28,111 validated frames remained, with a 100% detection rate for all 250 trials. Intra-subject repeatability was good (mean SD $\le 7.9°$ across all joint grasp combinations) and inter-subject variability was within the range expected based on normal anatomical diversity. Importantly, kinematic validation of the Index Proximal Interphalangeal (PIP) joint (61.8° ± 18.4°) showed values consistent with ranges reported in previous studies using instrumented gloves and depth sensors. Principal Component Analysis (PCA) confirmed clear linear separability among the five grasp configurations. Unlike existing datasets, the RGB-HJK method does not compromise the natural sense of touch and is free of hardware occlusions, thereby providing an easily accessible ecological baseline. Full article

We, the reviewers, explore Alex Baratta’s The Societal Codification of Korean English, highlighting Korean English (KE), since expanding circle English varieties are often overlooked despite their significant global role. Baratta argues that codification should be reconceptualized as a societal process driven by users themselves, where socially-used innovations become legitimate conventions, rather than having to be officially recognized as per tradition. Building on and moving beyond other works, he insists the field cannot wait for formal codification, even while acknowledging that some may find his framing of KE unconvincing or premature. We summarize such arguments around the legitimization of KE, offer insights into what Baratta’s work effectively addresses and leaves less explored. We then offer a conceptual matrix and metaphor to depict the complexity of KE and its codification. Finally, we introduce a new term, “K-English(es)”. The review aims to help readers better grasp the nuanced dynamics of KE that Baratta and the field engage with and to situate readers’ own interests (e.g., as English language teachers or Hallyu fans) around KE, while supporting the expanding scholarship on the topic. 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

This review provides a comprehensive synthesis of robotic fruit harvesting systems, with a particular focus on the system-level integration of perception, manipulation, and fruit detachment within autonomous harvesting environments. Recent advances in machine vision, deep learning, sensor fusion, robotic end-effectors, grasping strategies, and motion planning are critically analyzed alongside cutting, pulling, and vibration-based detachment mechanisms under unstructured orchard conditions. Beyond component-level analysis, this review emphasizes the critical role of perception–action coupling and highlights key system integration challenges, including localization errors, perception-to-action latency, and environmental variability, which continue to limit reliable field deployment. In addition, orchard and pre-harvest-related factors such as canopy structure, fruit distribution, and detachment force variability are examined in relation to their direct impact on system performance, robustness, and harvesting efficiency. Furthermore, the review extends toward system-level considerations by incorporating performance evaluation metrics, economic feasibility, and scalability constraints, which are essential for transitioning robotic harvesting systems from experimental prototypes to commercially viable solutions, including practical field deployment in distributed and multi-robot harvesting systems. Emerging technologies, including artificial intelligence, advanced sensing, digital agriculture, and energy-aware system design, are discussed as key enablers for achieving adaptive, data-driven, and scalable autonomous harvesting. The novelty of this work lies in proposing an integrated framework that explicitly links perception, manipulation, and detachment with orchard-level constraints and deployment requirements, thereby bridging the gap between algorithmic advancements and real-world implementation of autonomous fruit harvesting systems. Full article

Recent progress in prosthetic manipulation highlights the need for perception-driven control strategies that can adapt to diverse objects and user intent. This work presents a modular vision-guided grasping pipeline that integrates VLM-based object identification, orientation-aligned geometric modeling, and per-finger grasp planning for dexterous prosthetic hands. A Vision–Language Model (VLM) identifies the target object and activates the grasping pipeline only when recognition is confident, supporting intent-aware operation. From the segmented point cloud, an object-aligned bounding box (OBB) is constructed to provide a compact, orientation-aware representation of the object’s global extents, enabling more accurate distance and collision queries than axis-aligned boxes. Using this representation, the system evaluates candidate fingertip trajectories and selects contact poses for each finger independently, followed by Damped Least Squares inverse kinematics for joint-level execution. Preliminary experiments on a limited set of representative objects using the Linker Hand O7 demonstrate that the proposed pipeline achieves consistent grasp execution and exhibits promising real-time performance within controlled scenarios. In simulation, the proposed pipeline achieved a maximum segmentation accuracy of 93.4%, while hardware experiments on the Linker Hand O7 achieved 93.2% segmentation accuracy, confirming stable grasp execution across representative objects. While the evaluation is not yet comprehensive, the results indicate that combining semantic object identification with lightweight geometric reasoning can support efficient and adaptable grasp generation suitable for future prosthetic applications. Full article

Inspired by the human hand, this work presents a hybrid rigid–soft gripper that achieves passive adaptability through a self-resetting granular jamming pouch integrated onto a 3-DOF cable-driven rigid skeleton. Seven fingertip configurations (rigid tip, different jamming particles, and TPU-only) were evaluated across five object geometries. The jamming pouch configurations showed a clear advantage over rigid fingertips and a modest improvement over TPU-only fingertips when grasping flat or smoothly curved surfaces, while demonstrating substantially superior performance for objects with sharp protrusions, lips, undercuts, or deformable edges, where enhanced conformability and geometric interlocking markedly improved payload capacity and lateral offset tolerance. The passive self-reset mechanism remained reliable over 1000 cycles. These results demonstrate that the hybrid design effectively combines the advantages of rigid and soft grippers, achieving superior overall grasping performance while balancing adaptability and payload without pneumatic actuation, with strong potential for applications in logistics, food handling, and mobile robotics. Full article

Category-level generalization for robotic garment manipulation, such as bimanual smoothing, remains a significant hurdle due to high dimensionality, complex dynamics, and intra-category variations. Current approaches often struggle, either overfitting with concurrently learned visual features for a specific instance or, despite Category-level perceptual generalization, failing to predict the value of synergistic bimanual actions. We propose the Feature-Conditioned Bimanual Value Network (FCBV-Net), operating on 3D point clouds to specifically enhance intra-category policy generalization—generalizing across unseen variations within a single topological class, as distinct from cross-category transfer—for garment smoothing. FCBV-Net conditions bimanual action value prediction on pre-trained, frozen dense geometric features, ensuring robustness to intra-category garment variations. Trainable downstream components then learn a task-specific policy using these static features. In simulated PyFlex environments using the CLOTH3D dataset, FCBV-Net demonstrated superior intra-category generalization. It exhibited only an 11.5% efficiency drop (Steps80) on unseen garments compared to 96.2% for a 2D image-based baseline, and achieved 89% final coverage, outperforming an 83% coverage from a 3D correspondence-based baseline that uses identical per-point geometric features but a fixed primitive. These results highlight that the decoupling of geometric understanding from bimanual action value learning enables better intra-category generalization. Full article

Agriculture is currently transitioning toward higher intelligence and facility-based production, where harvesting robots play a crucial role in enhancing efficiency and ensuring standardized output. Addressing the challenges of inaccurate picking pose estimation and limited reachability in greenhouse environments, this paper proposes a reachable grasping pose estimation method based on Particle Swarm Optimization (PSO). First, initial poses are calculated via instance segmentation and keypoint extraction. Subsequently, a fitness function is constructed based on inverse kinematics, and the PSO algorithm is employed to iteratively search for optimal reachable poses. To further tackle planning difficulties in confined spaces, a two-stage path planning method based on cost maps is introduced. A series of performance metrics were designed to validate the proposed pose estimation and path planning methods through simulation experiments. In real-world field tests, the system achieved a harvesting success rate of 85%, significantly outperforming existing methods. The results demonstrate that the proposed approach substantially enhances the operational feasibility and success rate of tomato harvesting robots. Full article

Background/Objectives: Understanding people’s experiences is essential for improving healthcare systems (HCSs) and health outcomes, but assessing these experiences requires accurate, culturally and contextually appropriate measures. The People’s Voice Survey (PVS) is an instrument developed to assess current and future user experience across all levels of HCS globally. This research presents a cognitive interview (CI) approach as one measure for adapting PVS questions for adult and adolescent populations in three European countries. Methods: Twenty-four semi-structured CIs were conducted in Latvia, fifteen in Switzerland, and four in Germany. Study participants were recruited via convenience sampling. Interviews were conducted across virtual, telephone, and face-to-face settings. The “text summary” approach and Tourangeau’s four-step question-and-answer model served as the theoretical framework for the study. Results: The majority of the core PVS questions tested functioned well. The most common issues identified were related to comprehension and the decision process in all country cases. Analysis showed that almost all PVS core questions were also suitable for adolescents, with the exception of some questions related to evaluation and knowledge of HCS functioning, due to limited knowledge and/or experience with HCSs. Key adjustments included clarifying the question formulation, adding explanations to unclear and unknown terms and concepts, adding the following questions and missing answer categories. Conclusions: The paper shows how CIs are used to identify issues at all response stages when answering PVS core questions, thereby enabling the measure to be improved and supporting the use of CIs to enhance HCS assessment instruments. Full article

Recent advances in land system research depend heavily on efficient access to large-scale, multi-source remote sensing spatiotemporal databases. Although Text-to-SQL provides natural language interfaces, the scale and spatial complexity of remote sensing schemas generate significant noise for large language models, increasing inference costs and latency. This study presents graph-enhanced retrieval for accurate schema pruning (GRASP), a graph-based framework for schema pruning in remote sensing information systems. GRASP frames schema pruning as a semantic retrieval task and constructs a heterogeneous graph that represents both question semantics and database structure. By integrating a relation-aware transformer, a relational graph attention network, and pre-trained BERT representations, GRASP enhances schema understanding and supports joint table-column prediction through entity-level cross-attention. A dual-task objective combining contrastive learning with dynamic-threshold prediction mitigates class imbalance, while database value sampling and demonstration retrieval optimize inference performance. Experiments show that GRASP substantially improves schema pruning in spatiotemporal query scenarios: a 7B open-source LLM with GRASP surpasses an unaugmented 32B model on Spider; meanwhile, the framework also yields promising results on SpatialSQL, achieving a favorable balance among accuracy, cost, and deployment flexibility. GRASP provides a practical pathway for interdisciplinary researchers to query remote sensing databases in natural language, aiding spatiotemporal analysis. Full article

To address the challenges of severe background interference and unstable grasping of irregular maintenance tools in complex railway ballast environments, this paper proposes a robotic system that integrates enhanced visual perception with bio-inspired soft grasping. The core components of the system include a lightweight detection network (RA-YOLO), asymmetric “Fin Ray” soft fingers, and a visual servoing control framework. By embedding the CBAM attention mechanism and incorporating Mosaic data augmentation, RA-YOLO achieves robust feature extraction under complex backgrounds. The fingertip topology is optimized using the Yeoh constitutive model and finite element analysis, thereby improving stiffness under heavy loads and overall adaptability. Experimental results demonstrate that proposed RA-YOLO achieved a mAP@0.5 of 93.6% on the standard test set with an inference speed of 105 FPS. The visual-servo localization experiment an average Euclidean positioning error of 1.03 mm, with the maximum component-wise absolute error remaining below 2.5 mm. In system-level grasping experiments involving five categories of irregular tools, the integrated system achieved an overall grasping success rate of 91.8%, indicating its potential for automated tool recovery in unstructured railway maintenance environments. Full article

This study aims to address the challenge of accurately localizing picking points on mature daylily buds under complex field conditions with diverse lighting, arbitrary angles, and different bud stages. The difficulty is exacerbated by the morphological similarity of perianth tubes across stages, given that the perianth tube is the grasping region for harvesting. A multi-stage pipeline is proposed that integrates a Convolutional Block Attention Module (CBAM)-enhanced Faster R-CNN detector to simultaneously identify mature buds and perianth tubes, followed by a post-detection judgment module to select only the tubes associated with mature buds. An RGB-D camera is used to transform the 2D picking point from the center of the perianth tube bounding box to 3D coordinates in the camera coordinate system. Experimental results show that the proposed method improved Average Precision (AP) of perianth tube associated with mature bud by 29.72% over initial Faster R-CNN, and achieved a 2D localization accuracy of 98.70% and 3D localization accuracy of 90.79% under an allowable tolerance of ±5 mm, and thus an overall picking point localization accuracy of 71.88%. This study provides a theoretical basis and data support for visual localization of a robotic daylily harvester. Full article

Deep phosphate mining operations face complex, dynamic working conditions characterized by the superimposed disturbances of high temperature, high stress, and high strain. The occurrence of rockburst disasters demonstrates a clear pattern of dynamic evolution. Traditional rockburst risk assessment methods mostly adopt a static analysis approach, making it difficult to accurately grasp the dynamic characteristics of the entire process of a rockburst from inception and development to occurrence, and also making it hard to meet the practical work requirements of deep phosphate mining safety management. To address this engineering problem, this study constructs a superimposed analysis model for the risk of underground rockburst accidents in deep phosphate drilling based on a dynamic fault tree, and strives to tackle the complex dynamic issues in rockburst risk analysis and prediction. This model retains the technical advantages of traditional fault tree logical reasoning, integrates the time-series analysis function of dynamic fault trees, and organizes and describes various risk factors of deep phosphate rockbursts, as well as the concurrent, selective, and time-overlapping correlations among each factor. Finally, by introducing dynamic logic gates such as priority gates and standby gates, combined with the quantitative representation of rockburst risk stacking effects, it achieves dynamic risk assessment and accurate prediction of rockburst disasters. The model construction strictly follows the core processes of top event definition, hierarchical decomposition of risk factors, and dynamic logic structure construction, and organically integrates risk stacking theory with the dynamic fault tree method, forming an emergency rockburst risk prediction system that can provide technical support for reducing the probability of deep phosphate rockburst accidents. The rock fracture risk superposition model developed in this study aims to provide a tool for risk identification and spatial superposition analysis in deep phosphate mining, minimizing disturbances to the mine’s ecological environment, and offering theoretical support and technical methods for safe and green mining, sustainable development, and high-quality exploitation of deep phosphate resources. Full article