Search Results (213)

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

In this paper, an engineering model (EM) of a multi-joint space gripper for on-orbit servicing (OOS) is proposed. OOS missions demand robotic systems capable of reliable physical interactions under dynamic uncertainties and harsh space environments. While prior space-qualified grippers have demonstrated dexterous manipulation through anthropomorphic, high-DoF configurations, this work adopts a design direction widely established in industrial applications: a three-finger, lower-DoF configuration that balances grasp versatility, structural simplicity, and system integration for OOS missions. The developed gripper features a tendon-driven mechanism with a structural design optimized for space-environment compatibility and mechanical compliance. The kinematic characteristics of the mechanism are analyzed, while workspace and manipulability analyses are conducted to evaluate its operational limits. To verify the functional feasibility of the proposed design, representative grasping experiments were performed using a fabricated EM. The mechanical reliability and grasping performance were evaluated through a series of empirical experiments. The results indicate that the proposed design achieves a practical balance among grasp versatility, structural simplicity, and system integration for OOS missions, with a shielding-oriented structural configuration adopted as a design baseline. Its functional feasibility is supported by kinematic analysis, repeatability verification, and grasping experiments. This study provides a basis for the design and evaluation of three-finger robotic grippers in future OOS missions. Full article

Conventional industrial manipulators are often costly and come with steep learning curves, which limits their scalability in hands-on robotics education. This paper presents a compact and modular vision-guided sorting platform based on a 4-DOF SCARA robot, designed for rapid assembly, reconfiguration, and beginner-friendly deployment in laboratory courses. A collaborative visual perception strategy is proposed, which introduces a lightweight YOLOv8 algorithm for robust material category recognition, while HSV-based color segmentation and Hough circle localization are utilized to extract sub-pixel centroid features. The pixel measurements are mapped to the robot base frame through an integrated nine-point hand–eye calibration model, and joint commands are generated via a joint-space quintic polynomial interpolation algorithm to ensure continuity and avoid kinematic singularities. The overall system adopts a hierarchical architecture in which the vision host communicates target commands to a motion controller via TCP/IP, while joint actuators are driven through a CAN bus. Feasibility is first verified in a Webots digital prototype with synchronized conveyor and manipulator control, and is then validated on a physical platform equipped with a compliant TPU-based soft gripper to improve grasp tolerance under localization noise. Experiments demonstrate that the system achieves an average recognition accuracy of 98.1% and a mean positioning error of 0.189 mm. The proposed platform provides an extensible testbed for teaching kinematics, perception-to-control integration, and modular robotic system development. Full article

Vision–Language–Action (VLA) models unify visual perception, natural-language understanding, and action generation within a single foundation model, allowing a robot to follow instructions such as “fold the towel” or “fly to the red building” directly from camera images. Because VLAs inherit world knowledge from internet-scale pre-training, they have become the dominant framework for learning-based manipulation, with bimanual coordination serving as the most demanding testbed: two arms with 7+ degrees of freedom each must move in concert to fold, assemble, and reorient objects. Unmanned aerial robotics faces a structurally similar challenge: a drone must coordinate thrust, attitude, and increasingly gripper commands from visual observations under strict latency and payload constraints. This review covers 183 contributions spanning 2017–2026 and organized along seven dimensions: VLA architectures, training recipes, action representations, bimanual coordination (2022–2026), unmanned aerial vehicle (UAV) navigation and control (2017–2026), language grounding, and cross-cutting concerns including memory and world models. We show that the coordination strategies, training recipes, and action representations developed for bimanual VLAs transfer to unmanned aerial systems and identify fourteen research directions across both domains. Full article

To address the challenge that existing soft grippers have difficulty achieving fine manipulation comparable to the human finger’s “circular twisting” motion, this paper proposes a four-chamber spatial bending soft actuator based on the principle of virtual work. The actuator incorporates an internal cross-shaped restricting layer that divides its cross-section into four independent pneumatic chambers. Through independent regulation of the pressure in each chamber, continuous and controllable bending in arbitrary spatial directions is achieved, replicating the bending and abduction/adduction degrees of freedom (DoFs) of a human finger and their composite motions on a single actuator. Based on the Yeoh hyperelastic constitutive model and the principle of virtual work, a static deformation model of the actuator is established. By introducing an engineering assumption of “deformation vector superposition” and correction coefficients fitted from experimental data, high-precision prediction from multi-chamber pressure input to spatial bending output is realized. Furthermore, a three-finger soft gripper is constructed based on this actuator, successfully demonstrating fingertip pinching and enveloping grasping. Through open-loop programmed control, the fine “circular twisting” manipulation is demonstrated (exemplified by light bulb installation). This study provides an effective structural design and modeling method for soft actuators to achieve decoupled multi-DoF motion control, showcasing their application potential in adaptability and dexterous manipulation. Full article

Reliable potato quality monitoring during postharvest handling requires compact sensing systems that can acquire chemically relevant information while operating on irregular tuber surfaces. In this study, a Flexible Spectral Sensing Gripper (FSSG) was developed by integrating a low-cost 12-channel visible/near-infrared (Vis/NIR) spectral sensor array, electronic components, and an ESP32-S microcontroller onto a flexible printed circuit (FPC) substrate encapsulated with PDMS. By embedding the sensing units into the grasping interface, the FSSG enables conformal, multi-point spectral acquisition during potato handling, reducing optical-coupling uncertainty associated with unstable contact. Spectral reflectance data were collected from potato tubers, and dry matter content (DMC) and starch content (SC) were determined by standard chemical analysis as reference values. Multiple linear regression (MLR) and partial least squares regression (PLSR) models were compared under Norm, SNV, MSC, SNV-Norm, and MSC-Norm preprocessing conditions, and support vector machine (SVM) classification was used to distinguish healthy and artificially induced deteriorated samples. Normalization combined with MLR provided the best performance among the evaluated regression approaches, achieving cross-validation coefficients of determination ($R_{CV}^2$) of 0.847 and 0.817 and RPD values of 2.557 and 2.345 for DMC and SC, respectively. The SVM model achieved 98.67% accuracy for healthy versus artificially induced deteriorated potato samples. Overall, the FSSG demonstrates the value of combining gripper-integrated spectral sensing with interpretable chemometric modeling for potato quality screening. The FSSG enables real-time non-destructive quality prediction and disease-detected classification of potatoes, improves sorting accuracy and production efficiency, and provides general sensing solutions for controlled-environment agriculture, cold-chain logistics, and value-added processing of agricultural products. Full article

Soft modular grippers play a significant role in multiple fields due to their excellent adaptability and flexibility. This paper proposes a modular soft modular gripper driven by pneumatically actuated multi-chambers. The designed soft modular gripper features three operational modes, with its modular fingers employing independently controlled dual chambers. The distal and proximal dual-chamber structure enhances the fingertip force of the modular fingers. Based on classical laminated plate theory and incorporating the large deformation characteristics of soft materials, a relationship between the bending centerline of the fingers and the driving pressure is established, providing a theoretical foundation for grasping tasks executed by the soft modular gripper. The Denavit-Hartenberg (D-H) parameter method is utilized to develop the coordinate system of the soft modular gripper, thereby defining its operational workspace. Visual sensing technology is introduced, incorporating improvements to the YOLOv8-based object recognition and localization framework, which enhances recognition accuracy for target objects and ensures grasping stability. Full article

Accurately estimating the internal structure of deformable objects from sparse measurements remains a significant challenge in robotics. This work proposes a three-stage identification framework for this problem. First, a classification strategy determines a minimal informative set of indentation locations using a generalized error computed from pre-simulated FEM force reactions of baseline cavity models and flat-punch indentation estimation. Using this set, the estimation stage detects the cavity type and provides a preliminary estimate of its geometric parameters based solely on measured indentation responses. The correction stage then refines these parameters by replaying measured indentation depths in FEM simulations and deriving geometry corrections from the discrepancy between simulated and homogeneous force responses. Robust loss functions at both stages limit the influence of measurements where local contact conditions deviate from the assumed model, improving reliability across all tested cases. Indentation depth was obtained through gripper proprioception, with an RGB-D camera limited to global pose alignment. Experiments on soft cubes with spherical, cuboid, and pyramidal cavities demonstrate that, within known cavity families and fixed material parameters, the minimal indentation set reliably distinguishes cavity types and the pipeline reconstructs dimensions within error bounds. Extending the framework to non-centered structures and unknown materials remains future work. Full article

Early-stage frost damage in citrus fruits is difficult to detect because external symptoms are often weak or absent, hindering intelligent robotic sorting in postharvest scenarios. To address this challenge, this study proposes a robotic multimodal tactile sensing approach inspired by human mechanoreception for frost-damage detection during grasping. A robotic gripper equipped with a $6\times 6$ pressure matrix sensor and a piezoelectric vibration sensor was used to capture complementary tactile cues during standardized fruit handling, enabling the perception of subtle mechanical changes associated with early frost injury. Using 240 Citrus reticulata ‘Hong Mei Ren’ fruits under controlled experimental conditions, a Transformer-based multimodal fusion network was developed to jointly model pressure and vibration sequences for binary classification of normal and frost-damaged fruits. Across repeated stratified random-split experiments, the proposed method achieved a mean classification accuracy of 93.1%. Comparative experiments showed that the fusion model outperformed representative sequence-learning baselines, and ablation analysis confirmed that pressure–vibration fusion was more effective than either single modality alone. Attention-based temporal attribution further revealed that the most informative cues were concentrated in the initial contact and early loading stages, indicating the importance of early transient mechanical responses for frost-damage discrimination. Overall, the proposed approach demonstrates the feasibility of grasp-based robotic frost-damage detection under controlled experimental conditions. Full article

6D object pose estimation is an essential component for robotic grasping. Most existing deep learning-based approaches focus on instance-level pose estimation, which requires prior object models and consequently limits their applicability on unseen objects in real-world scenarios. In contrast, category-level 6D pose estimation adopts Normalized Object Coordinate Space (NOCS) maps to represent intra-class object geometry, enabling pose prediction without relying on predefined object models and thus improving generalization to unseen instances. However, the original NOCS-based category-level framework typically trains NOCS prediction and object classification in a joint manner, which introduces NOCS regression error among inter-class instances with similar appearances, thereby degrading pose estimation accuracy. To address this issue, we integrate the YOLOv8 object detection with SegFormer and propose a novel Category-Level SegFormer for 6D Object Pose Estimation (CLSF-6DPE). By decoupling object classification from NOCS regression through independent learning branches, the proposed framework significantly improves pose estimation performance. Furthermore, we validate the practical feasibility of CLSF-6DPE by integrating it with a robotic gripper via the Robot Operating System (ROS) in a Real-World grasping setup. Experimental results on the CAMERA and Real-World datasets demonstrate that the proposed method achieves mAP scores of 93.8% and 81.1%, respectively. Overall, the proposed method provides a modular and effective solution for category-level pose estimation in real-world robotic grasping applications. Full article

The installation of floating offshore wind turbines presents significant operational challenges due to coupled vessel platform dynamics and sensitivity to environmental conditions. This study proposes a response-based methodology for defining allowable operational limits and assessing operability for floating wind turbine generator (WTG) installation using the Nordic Wind concept. The approach integrates hydrodynamic modelling, time-domain simulations, and probabilistic weather-window analysis to evaluate installation feasibility under realistic offshore conditions. The methodology explicitly accounts for coupled vessel spar interactions, heading-dependent system response, and response-based failure criteria, including relative motion, gripper forces, and impact velocity. Allowable sea-state limits are derived for key installation phases and applied to multiple case studies representing different geographical locations and project scales. The results show that installation operability is governed primarily by system response rather than environmental parameters alone. Peak wave period and wave heading are identified as dominant factors, with longer wave periods leading to reduced operability due to response amplification. Across all case studies, the mating phase is found to be the most restrictive operation, controlling overall installation feasibility. Head sea conditions generally provide improved operability, while following seas lead to increased relative motion and reduced performance. The comparative analysis further demonstrates that environmental severity and project scale jointly influence installation duration. Milder environments result in higher operability, whereas harsher conditions, particularly those characterised by long-period swell, significantly reduce feasible weather windows. Larger installation campaigns increase cumulative exposure to weather downtime, even under favourable conditions. The proposed framework extends existing operability assessment methods by incorporating coupled multi-body dynamics and response-based criteria specific to floating wind installations. The results provide a quantitative basis for defining operational limits and support improved planning and decision making for offshore wind turbine installation. Full article

Robotic grippers play a key role in industrial automation and precision manipulation, where structural stiffness critically influences performance, load capacity, and accuracy. This study investigates how variations in geometric dimensions affect the stiffness and stresses of the gripper, thereby supporting more informed design decisions. A three-dimensional baseline model of a parallel-jaw robotic gripper was developed and systematically scaled along the three principal axes to evaluate the independent effects of geometric variation. Numerical simulations were conducted using ANSYS Workbench 2025 R1 to evaluate the stiffness and stress responses resulting from geometric scaling. The results provide insight into how scaling strategies influence mechanical behavior, offering a foundation for the optimization of gripper geometry in future designs. Full article

Three-dimensional printed polymers produced using Fused Deposition Modelling (FDM) exhibit directional microstructures resulting from filament paths, layer interfaces, and cellular infill, leading to mechanical and tribological responses distinct from those of homogeneous bulk materials. This study presents a comparative tribomechanical evaluation of polypropylene (PP) bulk inserts and 3D-printed polyethylene terephthalate glycol (PETG) inserts with a 30% hexagonal infill, relevant for robotic gripping applications. Progressive scratch tests were performed under loads from 5 to 100 N (150 N for PP), and profilometry was applied to quantify groove morphology, ridge formation, and displaced-volume ratios. An elasto-plastic conical indentation model was used to derive indentation pressures and elastic–plastic transition radii from groove geometry. The PETG inserts exhibited heterogeneous groove depth, intermittent ridge tearing, and friction fluctuations associated with the internal infill structure, consistent with previous findings on anisotropy and architecture-dependent behaviour in additively manufactured polymers. In contrast, bulk PP demonstrated smoother friction profiles and more stable plastic flow under increasing loads. Two functional indices—specific frictional work and ridge-to-trace volumetric ratio—are introduced to support material selection for robotic gripping systems. The results show that local contact mechanics in 3D-printed inserts are governed by print-induced structural features and can be effectively evaluated through a scratch-based elasto-plastic analysis. The methods and results presented in this work support the rational selection and design of polymer inserts for robotic gripper fingertips. The proposed scratch-based elasto-plastic evaluation framework enables manufacturers and automation engineers to compare 3D-printed and conventional materials based on friction stability, wear response, and deformation resistance. This approach can be directly applied to optimise gripping performance in industrial handling, packaging, and collaborative robotics. Full article

The research explored the automation production lines for the bottling of particulate materials in the pharmaceutical industries, covering the integrated processes of loading bottles, filling with particles, sealing, screwing on caps, quality inspection, and storage. The hardware system of the project consists of programmable logic controllers(PLCs), edge servers, motion control equipment, industrial cameras, and mechanical grippers for handling and storage. The aim of this research is to assist the manufacturing industry in transitioning from traditional production models to digital and intelligent production methods. From the perspective of core components, it analyzed and expounded the key technologies for building a digital production line; at the same time, from the perspective of data collection and processing, it clarified the role and advantages of the cloud platform. The product packaging process simulation covers loading bottles, filling with particle materials, sealing, screwing on caps, quality inspection, and storage. The production line issues production instructions and scheduling plans through the human-machine interaction interface and the cloud platform. Full article

Soft actuators provide a wide range of motion capabilities, allowing for the advancement of novel mobile robots. However, soft actuators that possess the capability required to achieve three-dimensional movement are limited. In addition, the presence of air supply tubes poses a challenge to utilizing pneumatic actuators as mobile robot components. This study presents a long inflatable actuator with a novel structure in which air supply tubes are arranged within its body. This structure enables the extension of the inflatable tube with minimal deformation. The proposed actuator comprises an inflatable tube and a spool mechanism. The length of the actuator is controlled by a motor. The performance of the actuator was evaluated experimentally, validating its alignment with our proposed models. The results showed that the proposed actuator exerted extension and contraction forces of 28 N and 87 N, respectively. Furthermore, the proposed actuator can be equipped with a gripper at its tip, enhancing its functionality. In a demonstration, this gripper-equipped actuator successfully extended to grasp a bar at a height of 1.3 m and contracted while lifting a 1.0 kg base. This demonstration indicated that the proposed actuator could provide the required arm motions of a bi-arm climbing robot. Full article