Search Results (802)

With the advancement of tactile sensors, researchers increasingly integrate tactile perception into robotics, but only for tasks such as object reconstruction, classification, recognition, and grasp state assessment. In this paper, we rethink the relationship between visual and tactile perception and propose a novel robotic grasping detection method based on visual–tactile perception. Initially, we construct a visual–tactile dataset containing the grasp stability for each potential grasping position. Next, we introduce a novel Grasp Stability Prediction Module (GSPM) to generate a grasp stability probability map, providing prior knowledge regarding grasp stability to the grasp detection network for each possible grasp position. Finally, the map is multiplied element-wise with the corresponding colored image and inputted into the grasp detection network. Experimental results demonstrate that our novel visual–tactile fusion method significantly enhances robotic grasping detection accuracy. Full article

The deployment of large remotely operated vehicles (ROVs) or autonomous underwater vehicles (AUVs) typically requires support vessels, crane systems, and specialized personnel, resulting in increased logistical complexity and operational costs. In this context, lightweight and modular underwater robots have emerged as a cost-effective alternative, capable of reaching significant depths and performing tasks traditionally associated with larger platforms. This article presents a system architecture for recovering a known object using a hybrid-controlled ROV, integrating autonomous perception, high-level interaction, and low-level control. The proposed architecture includes a perception module that estimates the object pose using a Perspective-n-Point (PnP) algorithm, combining object segmentation from a YOLOv11-seg network with 2D keypoints obtained from a YOLOv11-pose model. In addition, a Natural Language ROS Agent is incorporated to enable high-level command interaction between the operator and the robot. These modules interact with low-level controllers that regulate the vehicle degrees of freedom and with autonomous behaviors such as target approach and grasping. The proposed system is evaluated through simulation and experimental tank trials, including object recovery experiments conducted in a 12 × 8 × 5 m test tank at CIRTESU, as well as perception validation in simulated, tank, and harbor scenarios. The results demonstrate successful recovery of a black box using a BlueROV2 platform, showing that architectures of this type can effectively support operators in underwater intervention tasks, reducing operational risk, deployment complexity, and mission costs. Full article

Target-oriented grasping has become increasingly important in household and industrial environments, and deploying such systems on mobile robots is particularly challenging due to limited computational resources. To address these limitations, we present an efficient framework for real-time target-oriented grasping on resource-constrained platforms, supporting both click-based grasping for unknown objects and category-based grasping for known objects. To reduce model complexity while maintaining detection accuracy, YOLOv8 is compressed using a structured pruning method. For grasp pose generation, a pretrained GR-ConvNetv2 predicts candidate grasps, which are restricted to the target object using masks generated by MobileSAMv2. A geometry-based correction module then adjusts the position, angle, and width of the initial grasp poses to improve grasp accuracy. Finally, extensive experiments were carried out on the Cornell and Jacquard datasets, as well as in real-world single-object, cluttered, and stacked scenarios. The proposed framework achieves grasp success rates of 98.8% on the Cornell dataset and 95.8% on the Jacquard dataset, with over 90% success in real-world single-object and cluttered settings, while maintaining real-time performance of 67 ms and 75 ms per frame in the click-based and category-specified modes, respectively. These experiments demonstrate that the proposed framework achieves high grasping accuracy and robust performance, with a efficient design that enables deployment on mobile and resource-constrained robots. Full article

Recent advancements in prosthetic technology have increasingly focused on enhancing dexterity and autonomy through intelligent control systems. Vision-based approaches offer promising results for enabling prosthetic hands to interact more naturally with diverse objects in dynamic environments. Building on this foundation, the paper presents a vision-guided grasping algorithm for a prosthetic hand, integrating perception, planning, and control for dexterous manipulation. A camera mounted on the set up captures the scene, and a Bounding Volume Hierarchy (BVH)-based vision algorithm is employed to segment an object for grasping and define its bounding box. Grasp contact points are then computed by generating candidate trajectories using Rapidly-exploring Random Tree Star (RRT*) algorithm, and selecting fingertip end poses based on the minimum Euclidean distance between these trajectories and the object’s point cloud. Each finger’s grasp pose is determined independently, enabling adaptive, object-specific configurations. Damped Least Square (DLS) based Inverse kinematics solver is used to compute the corresponding joint angles, which are subsequently transmitted to the finger actuators for execution. Our intention in this work was to present a proof-of-concept pipeline demonstrating that fingertip poses derived from a simple, computationally lightweight geometric representation, specifically an AABB-based segmentation can be successfully propagated through per-finger planning and executed in real time on the Linker Hand O7 platform. The proposed method is validated in simulation, and experimental integration on a Linker Hand O7 platform. Full article

Soft robotic grippers provide compliant and adaptive manipulation, but most existing designs address actuation speed, adaptability, modularity, or sensing individually rather than in combination. This paper presents an electro-actuated customizable stacked Fin Ray gripper that integrates these capabilities within a single design. The gripper employs a compact solenoid for fast grasping, multiple vertically stacked Fin Ray segments for improved 3D conformity, and interchangeable silicone or TPU fins that can be tuned for task-specific stiffness and geometry. In addition, a light-guided, vision-based sensing approach is introduced to capture deformation without embedded sensors. Experimental studies—including free-fall object capture and optical shape sensing—demonstrate rapid solenoid-driven actuation, adaptive grasping behavior, and clear visual detectability of fin deformation. Complementary simulations using Cosserat-rod modeling and bond-graph analysis characterize the deformation mechanics and force response. Overall, the proposed gripper provides a practical soft-robotic solution that combines speed, adaptability, modular construction, and straightforward sensing for diverse object-handling scenarios. Full article

Background/Objectives: Within Paediatric Palliative Care (PPC), motherhood in the context of severe cognitive impairment is shaped by unique emotional, relational, and identity-related challenges. Traditional understandings of maternal identity are strained when verbal communication and typical developmental milestones are absent. Although caregiving in PPC has been widely studied, the subjective and symbolic dimensions of motherhood in this setting have received far less attention. This study sought to explore how mothers construct, interpret, and make sense of their maternal identity while caring for a child with severe cognitive impairment in a PPC context, and to underscore the clinical relevance of these identity-related processes. Methods: A qualitative study was conducted involving nine mothers of children receiving paediatric palliative care services at a regional centre in Italy. Participants engaged in three online focus groups, totalling 270 min. Reflexive thematic analysis was employed to interpret the transcribed data, using ATLAS.ti software, version 25.0.1 ATLAS.ti Scientific Software Development GmbH, Berlin, Germany, for support. Member reflections were incorporated to validate the findings. Results: Three interconnected themes emerged from the reflexive thematic analysis. First, mothers described the development of a fusion-like, enmeshed mother–child relationship, characterised by embodied attunement, specialised interpretive expertise, and lifelong care dependency. Second, mothers detailed the construction of their maternal role, shaped by emotional labour, identity negotiation, sacrifice, loneliness, and peer support, alongside the construction of the child’s role, in which children were perceived as unique, symbolically meaningful beings whose social presence and limited reciprocity shaped maternal identity. Third, mothers articulated a search for meaning that sustained them throughout the caregiving journey, reframing their experience within a broader existential and relational perspective. Conclusions: Maternal caregiving in PPC encompasses distinct emotional, relational, and symbolic dimensions that extend beyond conventional understandings of motherhood. Grasping these identity-related dynamics has direct clinical relevance: it enables more attuned communication, strengthens the therapeutic alliance, and supports personalised, meaning-oriented care. These insights highlight the need for tailored interventions and further qualitative research to inform health care professionals and interdisciplinary practice. Full article

To address the current reliance on manual labor in traditional logistics sorting operations, which leads to low sorting efficiency and high operational costs, this study presents the design of an unmanned logistics vehicle based on the Robot Operating System (ROS). To overcome bounding-box loss issues commonly encountered by mainstream video-stream image segmentation algorithms under complex conditions, the novel SMITE video image segmentation algorithm is employed to accurately extract key regions of mail items while eliminating interference. Extracted logistics information is mapped to corresponding grid points within a map constructed using Simultaneous Localization and Mapping (SLAM). The system performs global path planning with the A* heuristic graph search algorithm to determine the optimal route, autonomously navigates to the target location, and completes the sorting task via a robotic arm, while local path planning is managed using the Dijkstra algorithm. Experimental results demonstrate that the SMITE video image segmentation algorithm maintains stable and accurate segmentation under complex conditions, including object appearance variations, illumination changes, and viewpoint shifts. The PaddleOCR text recognition algorithm achieves an average recognition accuracy exceeding 98.5%, significantly outperforming traditional methods. Through the analysis of existing technologies and the design of a novel parcel-grasping control system, the feasibility of the proposed system is validated in real-world environments. Full article

This paper presents an immersive teleoperation framework for service robots that combines real-time 3D human pose estimation with a Virtual Reality (VR) interface to support intuitive, natural robot control. The operator is tracked using MediaPipe for 2D landmark detection and an Intel RealSense D455 RGB-D (Red-Green-Blue plus Depth) camera for depth acquisition, enabling 3D reconstruction of key joints. Joint angles are computed using efficient vector operations and mapped to the kinematic constraints of an anthropomorphic arm on the CHARMIE service robot. A VR-based telepresence interface provides stereoscopic video and head-motion-based view control to improve situational awareness during manipulation tasks. Experiments in real-world object grasping demonstrate reliable arm teleoperation and effective telepresence; however, vision-only estimation remains limited for axial rotations (e.g., elbow and wrist yaw), particularly under occlusions and unfavorable viewpoints. The proposed system provides a practical pathway toward low-cost, sensor-driven, immersive human–robot interaction for service robotics in dynamic environments. Full article

Background/Objectives: An individual with a hand disability, whether caused by an accident, disease, or congenital condition, may have significant problems with their daily activities, self-perception, and ability to work. Prosthetic hands can be used to restore essential hand functions, and features such as adaptive grasps can enhance their usability. Due to noise in the sEMG signal and hardware limitations in the system, reliable myoelectric control remains a challenge for low-cost prosthetics. ESP32 microcontrollers are used in this study to develop an SVM-based sEMG classifier that addresses these issues and improves responsiveness and accuracy. A 3D-printed mechanical structure supports the prosthesis, reducing production costs and making it more accessible. Methods: The prosthetic hand is developed using an ESP32 as the microcontroller, a Myoware Muscle Sensor to detect muscle activity, and an ESP32-based control system that integrates sEMG acquisition, SVM classification, and finger actuation with FSR feedback. A surface electromyography (sEMG) method is paired with a Support Vector Machine (SVM) algorithm to help classify signals from the sensor to improve the user’s experience and finger adaptability. Results: The SVM classifier achieved 89.10% accuracy, an F1-score of 0.89, and an AUC of 0.92, with real-time testing demonstrating that the ESP32 could reliably distinguish flexion and extension signals and actuate the servo, accordingly, producing movements consistent with the kinematic simulations. Complementing this control performance, the prosthetic hand was constructed using a coupled 4 bar linkage mechanism fabricated in PLA+, selected for its superior factor of safety compared to the other tested materials, ensuring sufficient structural reliability during operation. Conclusions: The results demonstrate that SVM-based sEMG classification can be effectively implemented on low-power microcontrollers for intuitive, low-cost prosthetic control. Further work is needed to expand beyond two-class detection and increase robustness against muscle fatigue and sensor placement variability. Full article

Autonomous experimental platforms increasingly rely on robust, vision-guided robotic manipulation to support reliable and repeatable laboratory operations. This work presents a modular motion-execution subsystem designed for integration into self-driving laboratory (SDL) workflows, focusing on the coupling of real-time visual perception with smooth and stable manipulator control. The framework enables autonomous detection, tracking, and interaction with textured objects through a hybrid scheme that couples advanced motion planning algorithms with real-time visual feedback. Kinematic analysis of the manipulator is performed using the screw theory formulations, which provide a rigorous foundation for deriving forward kinematics and the space Jacobian. These formulations are further employed to compute inverse kinematic solutions via the Damped Least Squares (DLS) method, ensuring stable and continuous joint trajectories even in the presence of redundancy and singularities. Motion trajectories toward target objects are generated using the RRT* algorithm, offering optimal path planning under dynamic constraints. Object pose estimation is achieved through a a vision workflow integrating feature-driven detection and homography-guided depth analysis, enabling adaptive tracking and dynamic grasping of textured objects. The manipulator’s performance is quantitatively evaluated using smoothness metrics, RMSE pose errors, and joint motion profiles, including velocity continuity, acceleration, jerk, and snap. Simulation results demonstrate that the proposed subsystem delivers stable, smooth, and reproducible motion execution, establishing a validated baseline for the manipulation layer of next-generation SDL architectures. Full article

The escalating volume of waste electrical and electronic equipment (WEEE) poses a significant global environmental challenge. The disassembly of printed circuit boards (PCBs), a critical step for resource recovery, remains inefficient due to limitations in the adaptability and dexterity of existing automated systems. This paper proposes an intelligent disassembly system for PCB components that integrates a multimodal large language model (MLLM) with a multi-agent framework. The MLLM serves as the system’s cognitive core, enabling high-level visual-language understanding and task planning by converting images into semantic descriptions and generating disassembly strategies. A state-of-the-art object detection algorithm (YOLOv13) is incorporated to provide fine-grained component localization. This high-level intelligence is seamlessly connected to low-level execution through a multi-agent framework that orchestrates collaborative dual robotic arms. One arm controls a heater for precise solder melting, while the other performs fine “probing-grasping” actions guided by real-time force feedback. Experiments were conducted on 30 decommissioned smart electricity meter PCBs, evaluating the system on recognition rate, capture rate, melting rate, and time consumption for seven component types. Results demonstrate that the system achieved a 100% melting rate across all components and high recognition rates (90–100%), validating its strengths in perception and thermal control. However, the capture rate varied significantly, highlighting the grasping of small, low-profile components as the primary bottleneck. This research presents a significant step towards autonomous, non-destructive e-waste recycling by effectively combining high-level cognitive intelligence with low-level robotic control, while also clearly identifying key areas for future improvement. Full article

Shape Memory Alloy (SMA) actuators offer strong potential for compact, lightweight, silent, and compliant robotic grippers; however, their practical deployment is limited by the challenge of controlling nonlinear and hysteretic thermal dynamics. This paper presents a complete Sim-to-Real control framework for precise temperature regulation of a tendon-driven SMA gripper using Deep Reinforcement Learning (DRL). A novel 12-action discrete control space is introduced, comprising 11 heating levels (0–100% PWM) and one active cooling action, enabling effective management of thermal inertia and environmental disturbances. The DRL agent is trained entirely in a calibrated thermo-mechanical simulation and deployed directly on physical hardware without real-world fine-tuning. Experimental results demonstrate accurate temperature tracking over a wide operating range (35–70 °C), achieving a mean steady-state error of approximately 0.26 °C below 50 °C and 0.41 °C at higher temperatures. Non-contact thermal imaging further confirms spatial temperature uniformity and the reliability of thermistor-based feedback. Finally, grasping experiments validate the practical effectiveness of the proposed controller, enabling reliable manipulation of delicate objects without crushing or slippage. These results demonstrate that the proposed DRL-based Sim-to-Real framework provides a robust and practical solution for high-precision SMA temperature control in soft robotic systems. Full article

Robotics is increasingly used in higher education laboratories, but most commercial robots are costly and designed for industrial use. This paper presents the design, modeling, and experimental evaluation of a low-cost four-degree-of-freedom (DOF) SCARA robot for educational and research purposes. The robot supports pick-and-place and laser engraving tasks. Direct and inverse kinematics were developed using Denavit–Hartenberg parameters, and the mechanical structure was validated through the dynamic analyses. A new machine learning (ML) framework integrating Support Vector Machine (SVM) and Random Forest (RF) models was implemented to enhance motion precision, predict task success, and compensate positioning errors in real time. Experimental tests over 360 cyles under varying speeds, payloads, and object types show that the SVM predicts grasp success with 94.4% accuracy, while the RF model estimates XY positioning error with an RMSE of 1.84 mm and cycle time error with an RMSE of 0.41 s. Moreover, a novel approach in this work that combines it with a laser engraving machine has been suggested. Repeatability experiments report 0.97 mm ISO-standard repeatability, and laser engraving trials yield mean positional errors of 0.45 mm, with maximum deviation of 0.90 mm. Compared to a baseline PID controller, the ML-enhanced strategy reduces RMS positioning error from 3.30 mm to 1.83 mm and improves repeatability by 36.5%, while slightly decreasing cycle time. These results demonstrate that the proposed SCARA robot achieves high-precision, consistent, and flexible operation suitable for both academic and light-duty practical applications. Full article

This paper investigates the effect of tactile feedback on the power efficiency and timing of controlling a remote robotic arm using a custom-built haptic glove. The glove integrates flex sensors to monitor finger movements and vibration motors to provide tactile feedback to the user. Communication with the robotic arm is established via the ESP-NOW protocol using an Arduino Nano ESP32 microcontroller (Arduino, Turin, Italy). This study examines the impact of tactile feedback on task performance by comparing precision, completion time, and power efficiency in object manipulation tasks with and without feedback. Experimental results demonstrate that tactile feedback significantly enhances the user’s control accuracy, reduces task execution time, and enables the user to control hand movement during object grasping scenarios precisely. It also highlights its importance in teleoperation systems. These findings have implications for improving human–robot interaction in remote manipulation scenarios, such as assistive robotics, remote surgery, and hazardous environment operations. Full article

Banana harvesting relies heavily on manual labor, which is labor-intensive and prone to fruit damage due to insufficient control of contact forces. This paper presents a systematic methodology for the design and optimization of adaptive flexible end-effectors for banana bunch harvesting, focusing on contact behavior and mechanical constraints. By integrating response surface methodology (RSM) with multi-objective genetic algorithm (MOGA) optimization, the relationships between finger geometry parameters and key performance metrics—contact area, contact stress, and radial stiffness—were quantified, and Pareto-optimal structural configurations were identified. Experimental and simulation results demonstrate that the optimized flexible fingers effectively improve handling performance: contact area increased by 13–28%, contact stress reduced by 45–56%, and radial stiffness enhanced by 193%, while the maximum shear stress on the fruit stalk decreased by 90%, ensuring harvesting stability during dynamic loading. The optimization effectively distributes contact pressure, minimizes fruit damage, and enhances grasping reliability. The proposed contact-behavior-constrained design framework enables passive adaptation to fruit morphology without complex sensors, offering a generalizable solution for soft robotic handling of fragile and irregular agricultural products. This work bridges the gap between bio-inspired gripper design and practical agricultural application, providing both theoretical insights and engineering guidance for automated, low-damage fruit harvesting systems. Full article