Search Results (507)

Wheelchair-mounted assistive robotic manipulators can provide reach and grasp functions for power wheelchair users. This in-lab study evaluated a vision-guided shared control (VGS) system with twelve users completing two multi-step kitchen tasks: a drinking task and a popcorn making task. Using a mixed methods approach participants compared VGS and manual joystick control, providing performance metrics, qualitative insights, and lessons learned. Data collection included demographic questionnaires, the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and exit interviews. No significant SUS differences were found between control modes, but NASA-TLX scores revealed VGS control significantly reduced workload during the drinking task and the popcorn task. VGS control reduced operation time and improved task success but was not universally preferred. Six participants preferred VGS, five preferred manual, and one had no preference. In addition, participants expressed interest in robotic arms for daily tasks and described two main operation challenges: distinguishing wrist orientation from rotation modes and managing depth perception. They also shared perspectives on how a personal robotic arm could complement caregiver support in their home. Full article

Despite rapid advances in robot learning, the coordination of closed-loop manipulation in cluttered environments remains a challenging and relatively underexplored problem. We present a novel two-level hierarchical architecture for a depth vision-equipped robotic arm that integrates pushing, grasping, and high-level decision making. Central to our approach is a prioritised action–selection mechanism that facilitates efficient early-stage learning via behaviour cloning (BC), while enabling scalable exploration through reinforcement learning (RL). A high-level decision neural network (DNN) selects between grasping and pushing actions, and two low-level action neural networks (ANNs) execute the selected primitive. The DNN is trained with RL, while the ANNs follow a hybrid learning scheme combining BC and RL. Notably, we introduce an automated demonstration generator based on oriented bounding boxes, eliminating the need for manual data collection and enabling precise, reproducible BC training signals. We evaluate our method on a challenging manipulation task involving five closely packed cubic objects. Our system achieves a completion rate (CR) of 100%, an average grasping success (AGS) of 93.1% per completion, and only 7.8 average decisions taken for completion (DTC). Comparative analysis against three baselines—a grasping-only policy, a fixed grasp-then-push sequence, and a cloned demonstration policy—highlights the necessity of dynamic decision making and the efficiency of our hierarchical design. In particular, the baselines yield lower AGS (86.6%) and higher DTC (10.6 and 11.4) scores, underscoring the advantages of content-aware, closed-loop control. These results demonstrate that our architecture supports robust, adaptive manipulation and scalable learning, offering a promising direction for autonomous skill coordination in complex environments. Full article

Accurate segmentation of densely stacked and weakly textured objects remains a core challenge in robotic depalletizing for industrial applications. To address this, we propose MaskNet, an instance segmentation network tailored for RGB-D input, designed to enhance recognition performance under occlusion and low-texture conditions. Built upon a Vision Transformer backbone, MaskNet adopts a dual-branch architecture for RGB and depth modalities and integrates multi-modal features using an attention-based fusion module. Further, spatial and channel attention mechanisms are employed to refine feature representation and improve instance-level discrimination. The segmentation outputs are used in conjunction with regional depth to optimize the grasping sequence. Experimental evaluations on camshaft depalletizing tasks demonstrate that MaskNet achieves a precision of 0.980, a recall of 0.971, and an F1-score of 0.975, outperforming a YOLO11-based baseline. In an actual scenario, with a self-designed flexible magnetic gripper, the system maintains a maximum grasping error of 9.85 mm and a 98% task success rate across multiple camshaft types. These results validate the effectiveness of MaskNet in enabling fine-grained perception for robotic manipulation in cluttered, real-world scenarios. Full article

The commercialization of selective broccoli harvesters, a critical response to agricultural labor shortages, is hampered by end effectors with large operational envelopes and poor adaptability to complex field conditions. To address these limitations, this study developed and evaluated a novel end-effector with an integrated transverse cutting mechanism and a foldable grasping cavity. Unlike conventional fixed cylindrical cavities, our design utilizes actuated grasping arms and a mechanical linkage system to significantly reduce the operational footprint and enhance maneuverability. Key design parameters were optimized based on broccoli morphological data and experimental measurements of the maximum stem cutting force. Furthermore, dynamic simulations were employed to validate the operational trajectory and ensure interference-free motion. Field tests demonstrated an operational success rate of 93.33% and a cutting success rate of 92.86%. The end effector successfully operated in dense planting environments, effectively avoiding interference with adjacent broccoli heads. This research provides a robust and promising solution that advances the automation of broccoli harvesting, paving the way for the commercial adoption of robotic harvesting technologies. Full article

Remote sensing visual grounding, a pivotal technology bridging natural language and high-resolution remote sensing images, holds significant application value in disaster monitoring, urban planning, and related fields. However, it faces critical challenges due to the inherent scale heterogeneity, semantic complexity, and annotation scarcity of remote sensing data. This paper first reviews the development history of remote sensing visual grounding, providing an overview of the basic background knowledge, including fundamental concepts, datasets, and evaluation metrics. Then, it categorizes methods by whether they employ large language models as a pedestal, and provides in-depth analyses of the innovations and limitations of Transformer-based and multimodal large language model-based methods. Furthermore, focusing on remote sensing image characteristics, it discusses cutting-edge techniques such as cross-modal feature fusion, language-guided visual optimization, multi-scale, and hierarchical feature processing, open-set expansion and efficient fine-tuning. Finally, it outlines current bottlenecks and proposes valuable directions for future research. As the first comprehensive review dedicated to remote sensing visual grounding, this work is a reference resource for researchers to grasp domain-specific concepts and track the latest developments. Full article

The pick-up gripper, as a core component of automatic transplanting systems, presents challenges in reliably grasping seedlings. In this study, a large-opening flexible seedling pick-up gripper was designed based on standard trays and seedling characteristics. Structural design and force analysis of the grasping mechanism were conducted to develop a functional prototype. As this represented the first prototype of this new gripper, multi-factor orthogonal tests and performance tests under local conditions were performed to evaluate its grasping effectiveness. It was found that the end diameter of the pick-up pin and the extraction speed for lifting plug seedlings vertically had the most significant effects, followed by the penetration depth and grasping force. The optimum grasping effectiveness was achieved when the end diameter of the pick-up pin was 1.2 mm, the penetration depth in the top straight line of the pick-up pin was 40 mm, the grasping force for squeezing root lumps was 0.4 MPa, and the extraction speed for lifting plug seedlings in a vertical direction was 900 mm/s. For typical vegetable seedlings, the average success rate in transplanting was up to 95%. Under the combined actions of penetrating, squeezing, and extracting operations, plug seedlings could be efficiently picked out for efficient transplanting. Full article

The advent of mixed reality (MR) systems has revolutionized human–computer interactions by seamlessly integrating virtual elements with the real world. Devices like the HoloLens 2 (HL2) enable intuitive, hands-free interactions through advanced hand-tracking technology, making them valuable in fields such as education, healthcare, engineering, and training simulations. However, despite the growing adoption of MR, there is a noticeable lack of comprehensive comparisons between the hand-tracking accuracy of the HL2 and high-precision benchmarks like motion capture systems. Such evaluations are essential to assess the reliability of MR interactions, identify potential tracking limitations, and improve the overall precision of hand-based input in immersive applications. This study aims to assess the accuracy of HL2 in tracking hand position and measuring kinematic hand parameters, including joint angles and lateral pinch span (distance between thumb and index fingertips), using its tracking data. To achieve this, the Vicon motion capture system (VM) was used as a gold-standard reference. Three tasks were designed: (1) finger tracing of a 2D pattern in 3D space, (2) grasping various common objects, and (3) lateral pinching of objects with varying sizes. Task 1 tests fingertip tracking, Task 2 evaluates joint angle accuracy, and Task 3 examines the accuracy of pinch span measurement. In all tasks, HL2 and VM simultaneously recorded hand positions and movements. The data captured in Task 1 were analyzed to evaluate HL2’s hand-tracking capabilities against VM. Finger rotation angles from Task 2 and lateral pinch span from Task 3 were then used to assess HL2’s accuracy compared to VM. The results indicate that the HL2 exhibits millimeter-level errors compared to Vicon’s tracking system in Task 1, spanning in a range from 2 mm to 4 mm, suggesting that HL2’s hand-tracking system demonstrates good accuracy. Additionally, the reconstructed grasping positions in Task 2 from both systems show a strong correlation and an average error of 5°, while in Task 3, the accuracy of the HL2 is comparable to that of VM, improving performance as the object thickness increases. Full article

Microbiome data analysis is essential for understanding the role of microbial communities in human health. However, limited data availability often hinders research progress, and synthetic data generation could offer a promising solution to this problem. This study aims to explore the use of machine learning (ML) to enrich an unbalanced dataset consisting of microbial operational taxonomic unit (OTU) counts of 148 samples, belonging to 61 patients. In detail, 34 samples are from 16 adenomatous polyps (AP) patients, while 114 samples are from 46 colorectal cancer (CRC) patients. Synthesis of AP and CRC samples was conducted using the Synthetic Data Vault Python library, employing a Gaussian Copula synthesiser. Subsequently, the synthesised data quality was evaluated using a logistic regression model in parallel with an optimised support vector machine algorithm (polynomial kernel). The data quality is considered good when neither of the two algorithms can discriminate between real and synthetic data, showing low accuracy, F1 score, and precision values. Furthermore, additional statistical tests were employed to confirm the similarity between real and synthetic data. After data validation, layer-wise relevance propagation (LRP) was performed on a deep learning classifier to extract important OTU features from the generated dataset, to discriminate between CRC patients and those affected by AP. Exploiting the acquired features, which correspond to unique bacterial taxa, ML classifiers were trained and tested to estimate the validity of such microorganisms in recognising AP and CRC samples. The simplified version of the original OTU table opens up opportunities for further investigations, especially in the realm of extensive data synthesis. This involves a deeper exploration and augmentation of the condensed data to uncover new insights and patterns that might not be readily apparent in the original, more complex form. Digging deeper into the simplified data may help us better grasp the biological or ecological processes reflected in the OTU data. Transitioning from this exploration, the synergy of ML and synthetic data enrichment holds promise for advancing microbiome research. This approach enhances classification accuracy and reveals hidden microbial markers that could prove valuable in clinical practice as a diagnostic and prognostic tool. Full article

Rubberized concrete is a more environmentally friendly material than natural concrete as it helps to reduce rubber disposal issues and has superior impact resistance. Geopolymer concrete, on the other hand, is an economical concrete with higher mechanical properties than nominal concrete that uses fly ash and slag, among other industrial solid wastes, to lower carbon footprints. Rubberized geopolymer concrete (RuGPC) combines the advantages of both concrete types, and a thorough grasp of its dynamic compressive characteristics is necessary for its use in components linked to impact resistance. Despite the advantages of RuGPC, predicting its mechanical characteristics is sometimes difficult because of variations in binder type and combination. This research investigated the combined effect of ground granulated blast furnace slag (GGBFS) and fly ash (FA) on the workability, compressive strength, and stress–strain characteristics of RuGPC with rubber at 0%, 10%, and 20% fine aggregate replacement. Thereafter, energy absorption and ductile characteristics were evaluated through the concrete toughness and ductility index. Numerical models were proposed for the cube compressive strength, modulus of elasticity, and peak strain of RuGPC at different percentages of crumb rubber. It was found that RuGPC made with GGBFS/FA had similar stress–strain characteristics to FA- and MK-based RuGPC. At 20% of crumb rubber aggregate replacement, the workability, compressive strength, modulus of elasticity, and peak stress of RuGPC reduced by 8.33%, 34.67%, 43.42%, and 44.97%, while Poisson’s ratio, peak, and ultimate strain increased by 30.34%, 8.56%, and 55.84%, respectively. The concrete toughness and ductility index increased by 22.4% and 156.67%. The proposed model’s calculated results, with R² values of 0.9508, 0.9935, and 0.9762, show high consistency with the experimental data. RuGPC demonstrates high energy absorption capacity, making it a suitable construction material for structures requiring high-impact resistance. Full article

Thorough investigation into dune morphology is pivotal for grasping the intricacies of constructing and operating power transmission lines in desert terrains. However, there remains a notable gap in the quantitative analysis and assessment of how dune dynamics evolve under the influence of transmission infrastructure. In this study, the Real-Space Cellular Automaton Laboratory is deployed to explore how transverse dunes evolve around transmission towers under diverse wind velocities and varying dune dimensions. The results reveal that, beyond the immediate vicinity of the transmission tower, the height of the transverse dune remains largely stable across broad spatial scales, unaffected by the transmission line. As wind velocities wane, the structural integrity of the transverse dunes is compromised, leading to an expansion in the size of the trail structures. Initially, the height of the dune surges, only to decline progressively over time, with the maximum fluctuation reaching nearly $1\mathrm{m}$. The height of larger dunes escalates gradually at first, peaks, and then subsides, with the pinnacle height nearing $6.5\mathrm{m}$. As a critical metric for safety evaluation, the height of the transmission line above ground initially plummets, then gradually rebounds, and shifts backward over time after hitting its nadir. Full article

Real-time computational capability for simultaneous grasping force and displacement determination constitutes a critical enabler for stable and reliable grasping performance in dexterous robotic grasping. To accelerate the computational efficiency of LCP in the dexterous grasping problem, as well as to ensure the stability and reproducibility of the algorithm’s output, the NSNMGK method, which incorporates sequential projection iterations across all greedy-selected active constraint rows within each NSNGRK framework iteration cycle, is developed. In each NSNMGK iteration, sequential projection operations are systematically applied to all active constraint rows, satisfying the greedy criterion. This processing strategy ensures the full utilization of qualifying constraints within the greedy subset through a same generalized Jacobian evaluation per iteration cycle. The methodology effectively mitigates inherent limitations of conventional randomized row selection, including unpredictable iteration counts and computational overhead from repeated Jacobian updates, while maintaining deterministic convergence behavior. The method’s convergence theory is rigorously established, with benchmark analyses demonstrating marked improvements in computational efficiency over the NSNGRK framework. Experimental validation in dexterous robotic grasping scenarios further confirms enhanced convergence rates through reduced iteration counts and shortened computational durations relative to existing approaches. Full article

In order to improve education for sustainability (EfS) in English-speaking schools in North Cyprus, this study investigates the use of playful learning and video feedback as cutting-edge pedagogical techniques. Engaging students in transformative learning experiences is crucial in an era characterized by environmental issues and the pressing need for sustainable development. Students better understand their learning processes when paired with video feedback, which facilitates reflective practice. This study uses a qualitative case study methodology to investigate how instructors employ video feedback and playful learning activities to help students grasp sustainability ideas. Data were gathered through teacher interviews, classroom observations, and evaluations of student performance and feedback. The results demonstrate how these approaches can promote active learning, boost student enthusiasm, and enhance comprehension of sustainability-related topics. This study’s conclusion includes recommendations for educators and legislators looking to integrate creative, student-centered approaches into EfS curricula. Full article

This study proposes a GPU-accelerated Position-Based Dynamics (PBD) system for realistic and interactive cloth simulation in Extended Reality (XR) environments, and comprehensively evaluates its performance and functional capabilities on standalone XR devices, such as the Meta Quest 3. To overcome the limitations of traditional CPU-based physics simulations, we designed and optimized highly parallelized algorithms utilizing Unity’s Compute Shader framework. The proposed system achieves real-time performance by implementing efficient collision detection and response handling with complex environmental meshes (RoomMesh) and dynamic hand meshes (HandMesh), as well as capsule colliders based on hand skeleton tracking (OVRSkeleton). Performance evaluations were conducted for both single-sided and double-sided cloth configurations across multiple resolutions. At a 32 × 32 resolution, both configurations maintained stable frame rates of approximately 72 FPS. At a 64 × 64 resolution, the single-sided cloth achieved around 65 FPS, while the double-sided configuration recorded approximately 40 FPS, demonstrating scalable quality adaptation depending on application requirements. Functionally, the GPU-PBD system significantly surpasses Unity’s built-in Cloth component by supporting double-sided cloth rendering, fine-grained constraint control, complex mesh-based collision handling, and real-time interaction with both hand meshes and capsule colliders. These capabilities enable immersive and physically plausible XR experiences, including natural cloth draping, grasping, and deformation behaviors during user interactions. The technical advantages of the proposed system suggest strong applicability in various XR fields, such as virtual clothing fitting, medical training simulations, educational content, and interactive art installations. Future work will focus on extending the framework to general deformable body simulation, incorporating advanced material modeling, self-collision response, and dynamic cutting simulation, thereby enhancing both realism and scalability in XR environments. Full article

We are living in a world filled with artifacts, including daily-use and durable products. In the context of sustainable consumption and production (SCP), the term “sufficiency” is an essential keyword. The concept of sufficiency is important for grasping the overall contribution of product functions to the fulfillment of human needs in terms of social sustainability. Sufficiency is also understood to be a necessary component for reducing the environmental impact of daily-use and durable products on the natural environment. Therefore, sufficiency is regarded as a key factor in promoting environmental sustainability. Generally, a product itself is not as essential as the functions it provides to the user. However, product functions have not only positive aspects that satisfy human needs, but also negative aspects that do not. Most existing methods for assessing the satisfaction of human needs are based on direct approaches, such as life satisfaction surveys, which do not take product functions into account. In the previous study, we proposed a living-sphere approach that integrates the traditional engineering design framework with Max-Neef’s framework of needs, relating product functions to fundamental human needs. In Max-Neef’s framework, a key concept is the “satisfier,” which refers to a conceptual method of satisfying universal human needs; however, this concept varies according to regional or local circumstances, such as culture, climate, and history. This study proposes a method to evaluate net sufficiency, which is the overall impact of product functions, both positive and negative, on fulfilling fundamental human needs. Through introducing not only a satisfier that fulfills but also a barrier that obstructs fundamental human needs, it is possible to comprehensively evaluate the degree to which a product’s functions fulfill such needs. Two case studies from Osaka and Hanoi were carried out independently, showing that the proposed method enables comprehensive evaluation of the net sufficiency of meeting fundamental needs in terms of the positive and negative aspects of product functions. Full article

This study proposes a D-optimality-based viewpoint selection method to improve visual assistance for a manipulator by optimizing camera placement. The approach maximizes the information gained from visual observations, reducing uncertainty in object recognition and localization. A mathematical framework utilizing D-optimality criteria is developed to determine the most informative camera viewpoint in real time. The proposed method is integrated into a robotic system where a mobile robot adjusts its viewpoint to support the manipulator in grasping and placing tasks. Experimental evaluations demonstrate that D-optimality-based viewpoint selection improves recognition accuracy and task efficiency. The results suggest that optimal viewpoint planning can enhance perception robustness, leading to better manipulation performance. Although tested in structured environments, the approach has the potential to be extended to dynamic or unstructured settings. This research contributes to the integration of viewpoint optimization in vision-based robotic cooperation, with promising applications in industrial automation, service robotics, and human–robot collaboration. Full article