Search Results (332)

Flexible pressure sensors serve as core sensing components for wearable health monitoring systems, electronic skins for soft robots, and flexible human–machine interaction devices. Benefiting from the interfacial electric double-layer polarization effect, iontronic sensing delivers far higher pressure response sensitivity than conventional parallel-plate capacitive sensors, endowing it with distinctive advantages in the detection of weak physiological signals. Nevertheless, current dense ionic thin-film dielectric layers suffer from limited deformation space under compression and poor low-pressure sensing capability. Mainstream high-precision micropillar arrays are fabricated via photolithography, 3D printing, and metal etching molds, which require costly equipment and complicated fabrication procedures, making large-area mass production unfeasible. Random frosted concave-convex microstructures feature disordered dimensions, leading to severe device hysteresis and narrow linear ranges, which fail to achieve ultrahigh sensitivity alongside a wide pressure detection range simultaneously. To address the aforementioned multiple bottlenecks, this paper proposes a low-cost resin template replication process to fabricate TPU-based ionic thin-film dielectric layers with ordered micropillar array microstructures. Combined with inkjet-printed silver conductive PI flexible electrodes, an iontronic flexible pressure sensor with a sandwich layered structure is constructed. Multi-dimensional investigations including microscopic morphology characterization, electromechanical sensing performance calibration, and human wearable application tests are systematically implemented to thoroughly elucidate the synergistic enhancement mechanism of the arrayed micropillars. Test results demonstrate that the effective pressure detection range of the sensor spans 0–1038 kPa, accommodating ultra-low pressures such as pulse signals as well as medium-to-high-pressure loads including joint bending. The sensitivity reaches 23.27 kPa⁻¹ within the low-pressure range of 0–200 kPa and remains stable at 3.52 kPa⁻¹ in the high-pressure range of 200–1038 kPa, with piecewise linear fitting correlation coefficients of 0.93 and 0.96 respectively. Both the response time and recovery time of the device are 40 ms, and the hysteresis error throughout the loading-unloading cycle is merely 2.62%. After 20,000 consecutive cyclic loading-unloading tests, the peak capacitance output only decays by 5.1%, verifying outstanding mechanical fatigue resistance and electrical stability. Validations in multi-scenario applications prove that the sensor can accurately capture human physiological and motion signals including radial artery pulses, laryngeal deformation induced by multi-syllable vocalization, and multi-angle bending of fingers and elbow joints, suitable for home-based health monitoring, quantitative rehabilitation training, flexible tactile interaction and other scenarios. The entire fabrication process eliminates high-precision micro-nano processing equipment such as photolithography systems, plasma etchers and 3D printers; only general chemical raw materials and conventional laboratory instruments are adopted. The reusable templates enable low manufacturing costs and large-area coating forming, offering a novel low-cost technical solution for the engineering implementation and industrialization of high-performance iontronic flexible pressure sensors. Full article

Aiming at the challenges of potato storage scenarios, severe target stacking and occlusion, and large variations in defect characteristics in potato storage environments, a potato defect detection method named MDS-DETR was proposed, and its deployment on a robotic sorting platform was validated. First, potato images under different illumination conditions and stacking states were collected in potato storage warehouses to construct a potato defect dataset containing defects such as black spot, sprouting, dry rot, decay, green skin, and cracking. Subsequently, according to the characteristics of object detection tasks in storage scenarios, an MCF module was designed to strengthen the extraction and integration of contextual features across different spatial scales. An AIFI-DyMona structure, namely Anchor-free Instance Feature Interaction with Dynamic Mona, was constructed to improve the stability of feature representation under different illumination conditions. In addition, the Shape-IoU regression strategy was incorporated to improve the network sensitivity to irregular defect contours and geometric characteristics. Validation experiments demonstrated that the proposed MDS-DETR framework achieved 96.3% mAP@0.5, with only 14.2 M parameters and 42.9 G FLOPs. Compared with several representative object detection algorithms, the proposed method exhibited superior recognition capability and stronger robustness under complicated storage conditions, while also showing better suppression of missed and incorrect detections. To further evaluate its practical applicability, the trained network was integrated into an intelligent potato sorting robot and tested in real warehouse scenarios. Experimental observations indicated that the robotic system consistently maintained a sorting accuracy exceeding 95%, demonstrating the effectiveness and practical deployment potential of the proposed approach for potato storage applications. This study can provide a reference for intelligent detection and automated sorting in potato storage processes. Full article

Flexible piezoresistive sensors are highly desirable for wearable health monitoring, yet balancing ultrahigh sensitivity and wide pressure detection range is a major bottleneck restricting their applications in electronic skin and soft robots. This work constructs a hierarchical piezoresistive sensor through a simple three-step fabrication: electrospinning PVDF/PAN fiber networks, polydopamine (PDA) surface modification, and in situ polypyrrole (PPy) polymerization for conductive sensing layers. As a dual-function interlayer, PDA forms hydrogen and covalent bonds with PPy to yield uniform, firm conductive coatings. The link between PPy morphology and sensing performance is clarified by regulating polymerization parameters. At a pyrrole concentration of 3 g/L, the optimized sensor achieves a high sensitivity of 220.88 kPa⁻¹ (0–10 kPa) and stable linear signals up to 1 MPa, with superior cycling durability over 5000 cycles and good biocompatibility. This scalable fabrication resolves the sensitivity–range tradeoff, promising wearable medical monitoring and human–machine interaction devices. Full article

Flexible capacitive sensors for electronic skins and soft robotic systems are expected to provide not only high-pressure sensitivity but also multifunctional sensing capabilities. However, conventional dielectric layer designs often suffer from a trade-off among multiple functionalities. To address this challenge, we developed a flexible sensor featuring a hybrid microstructured functional layer for pressure sensing, distance monitoring, and material identification. The functional layer was a polydimethylsiloxane (PDMS) film embedded with micro-sized sugar particles and patterned with microstructures on its surface. The pressure-sensing performance, such as pressing sensitivity, response time, and hysteresis, was first evaluated. The pressure sensitivity reached 3.55 × 10⁻² kPa⁻¹ at an applied force of 1 N, which is significantly greater than that of the sensor using either a flat PDMS layer or a PDMS film embedded solely with sugar particles. The hybrid microstructured sensor also exhibited superior performance in terms of hysteresis and repeatability. Moreover, the sensor was shown to measure the distance to an object with a sensitivity of 0.023 mm⁻¹. Furthermore, the robust identification of materials with different permittivities was demonstrated using the flexible sensor. Given its multifunctional, non-contact, and high-sensitivity capabilities, this flexible sensor holds significant potential for integration into advanced electronic skins, intelligent soft robotics for tactile object sorting, and human–-machine interfaces. Full article

Background/Objectives: Prostate cancer is one of the most common non-skin solid malignancies among male solid organ transplant recipients (SOTRs), in whom radical prostatectomy is technically demanding. We report the first use of indocyanine green (ICG) fluorescence for simultaneous transplanted-ureter identification and renal graft vascular mapping during robot-assisted radical prostatectomy (RARP), and the first use of the CMR Versius^® platform in a SOTR. Methods: Retrospective case series of four consecutive male SOTRs (two renal [RTRs], two hepatic) undergoing RARP. In both RTRs, a dual-route ICG protocol was used on the da Vinci Xi with Firefly^® imaging: pre-docking intraureteral ICG via a ureteral catheter for ureter identification, plus an intravenous ICG bolus for graft vascular mapping and cortical perfusion. One hepatic recipient was operated with the CMR Versius^® system using an infra-umbilical port configuration to avoid the chevron transplant scar. Results: All four procedures were completed robotically without conversion. Median operative time was 176 min and median estimated blood loss 350 mL. Surgical margins were negative (R0) in all four; final pathology was pT3aN0 in three and pT2N0 in one, although in the renal recipients nodal staging reflected a contralateral-only dissection. PSA was undetectable at three months in all patients. One hepatic recipient later developed biochemical recurrence, managed with salvage radiotherapy and androgen deprivation therapy, with subsequent undetectable PSA. One hepatic recipient had a Clavien–Dindo IIIa complication. No graft dysfunction occurred. Conclusions: ICG-guided RARP and the CMR Versius^® platform appear technically feasible in carefully selected solid organ transplant recipients treated at an experienced multidisciplinary centre, with no graft-related complications observed in this small initial series. These preliminary findings require validation in larger, multicenter studies before general safety and oncological efficacy can be established. Full article

Background/Objectives: Intermittent ureteropelvic junction obstruction (UPJO), classically termed Dietl’s crisis, may be missed when imaging is obtained outside symptomatic periods. The challenge is amplified in duplicated collecting systems, where the obstructed moiety may not be recognized on screening ultrasonography. Case Presentation: A 14-year-old boy presented with recurrent severe right flank pain triggered by heavy fluid intake, associated with nausea and vomiting and separated by symptom-free intervals. An initial renal ultrasound performed between attacks was normal. One week later, the same pain pattern recurred together with a febrile urinary tract infection. Computed tomography urography (CTU) demonstrated an incomplete right duplex collecting system with a bifid ureter, marked hydronephrosis of the lower moiety, delayed contrast excretion, and focal narrowing at the lower-moiety ureteropelvic junction adjacent to one crossing artery and one crossing vein. Diuretic ^99mTc-mercaptoacetyltriglycine (MAG3) renography documented obstructive drainage, with a post-furosemide drainage half-time (T1/2) > 20 min, differential renal function of 51% on the right, and split moiety function of 31% (upper) and 20% (lower). The patient underwent transperitoneal robot-assisted dismembered Anderson–Hynes pyeloplasty of the lower moiety with the reconstructed ureteropelvic junction repositioned anterior to the crossing vessels; a 6 Fr × 26 cm double-J stent was placed and subsequently removed at 4 weeks postoperatively. Total skin-to-skin operative time was 75 min, estimated blood loss < 100 mL, and the postoperative course was uneventful. At early 3-month follow-up, the patient remained free of Dietl-type episodes, and ultrasonography showed marked reduction in lower-moiety hydronephrosis. Selective postoperative CTU, obtained because of the unusual bifid anatomy, demonstrated patent drainage. Conclusions: A normal interval ultrasound should not exclude intermittent UPJO when the history is stereotypical, and cross-sectional plus functional imaging is decisive when duplex anatomy and crossing vessels coexist. Full article

Ultrasound is safe, portable, and relatively low cost, and robotic ultrasound research is expanding across many diagnostic applications. Within this context, abdominal aortic aneurysm (AAA) screening remains comparatively unexplored, with few systems reporting end-to-end autonomous scanning and clinician-validated evaluation in volunteers. We present a conditionally autonomous (Level-3) robotic ultrasound system in which the operator defines the region of interest and confirms the target force band, after which the robot performs surface-constrained abdominal sweeps under force control and automatically selects diagnostic frames and estimates aortic diameter without further manual interaction during scanning. The system combines RGB-depth-based patient-to-robot registration, hybrid position–force control with a low-cost force sensor, and a post-acquisition image-analysis pipeline comprising rule-based aorta localisation, a composite image quality assessment (IQA) metric, and a transfer-learned U-Net segmentation baseline. In a feasibility study on ten healthy volunteers spanning BMI 18.6–33 and diverse sex and skin-tone profiles, the robot maintained stable contact within the target force band in all sessions and produced aortic images rated diagnostically acceptable by clinicians in all participants. Automated diameter measurements showed a mean absolute difference of 1.45 mm relative to clinician reference values, with 9/10 cases within 3 mm and all within the 5 mm screening criterion. Volunteer questionnaires indicated high levels of comfort and trust in the system. These results demonstrate the feasibility of operator-supervised, force-aware robotic AAA scanning and highlight the potential of low-cost robotic ultrasound for wider automated vascular imaging. Full article

To address prevalent industrial challenges, including the high cost of fabricating microstructures via photolithography and 3D printing, impurity residues easily generated by conventional physical/chemical pore-forming techniques, and the limited sensitivity of regular capacitive sensors, this paper innovatively proposes an integrated low-temperature in situ gas foaming strategy using ammonium bicarbonate for the fabrication of porous TPU-based ionic gels. Relying on the complete gaseous decomposition property of ammonium bicarbonate upon heating, a three-dimensionally interconnected continuous porous network is spontaneously constructed inside the polymer matrix. Thermoplastic polyurethane (TPU) is selected as the continuous polymer phase, and [EMIM][TFSI] imidazolium ionic liquid is blended as the ion source to synthesize composite ionic gel substrates. A PDMS composite slurry filled with graphene is employed to prepare flexible substrates, followed by low-temperature oxygen plasma surface modification to introduce polar functional groups such as hydroxyl and carboxyl onto electrode surfaces. A standard sandwich-structured ionic pressure sensor with the configuration of “top modified electrode—porous ionic gel dielectric layer—bottom modified electrode” is finally assembled. The porous framework and modified electrodes constitute a dual synergistic enhancement system: the porous structure markedly reduces the equivalent elastic modulus of the gel and improves its compressive deformation capacity; polar-modified electrodes optimize the interfacial compatibility between electrodes and gels, shorten ion migration paths and lower interfacial contact resistance. Systematic calibration of multiple batches of parallel samples reveals that the as-fabricated sensor achieves a high sensitivity of 25.3 kPa⁻¹ across the full measuring range from 0 to 1000 kPa with a linear fitting coefficient R² = 0.992. The loading response time and unloading recovery time of the device are 60 ms and 80 ms respectively, with a performance degradation of less than 3% after 1000 consecutive loading–unloading cycles, featuring low hysteresis error and excellent signal repeatability. Multi-scenario in vivo wearable tests on human subjects verify that the device can precisely capture subtle fluctuations of radial artery pulse and periodic laryngeal deformation during swallowing, distinguish characteristic waveform patterns of various English words according to differences in vocal cord vibration, and accurately detect bending motions when attached to finger joints. The entire fabrication process adopts common chemical raw materials and standard laboratory equipment without expensive micro-nano processing facilities, featuring convenient raw material procurement and high process fault tolerance, which enables large-area coating-based mass production. This work delivers a novel technical route for the low-cost large-scale production of high-performance ionic flexible sensors and bears significant industrialization reference value for applications in wearable medical monitoring, bionic robotic electronic skin, flexible human–machine interactive touch panels and other related fields. Full article

Background/Objectives: Pediatric spinal cord injury (SCI) is a rare yet highly disabling condition associated with substantial functional and psychosocial impairments. Although robot-assisted gait training (RAGT) and transcutaneous spinal cord stimulation (tSCS) have independently demonstrated promising results, evidence regarding their combined use in pediatric SCI is limited. This study aimed primarily to assess the feasibility and safety of combining RAGT with tSCS in children with incomplete SCI, and secondarily to explore its effects on clinical outcomes. Methods: A case series study was conducted that included three pediatric participants (<18 years) with chronic incomplete SCI (AIS C–D). Participants completed five consecutive sessions of RAGT using a Lokomat device combined with tSCS applied at the lumbosacral level. Each session consisted of 30 min of gait training, including 20 min of concurrent electrical stimulation. Safety was assessed through adverse-event monitoring and pain evaluation. Clinical outcomes included gait speed (10MWT), trunk control (SATCo), lower-limb strength (LEMS), and spasticity (MAS). Results: Session adherence reached 100%. Skin erythema was the most frequently reported adverse event and showed a clear association with tSCS. All participants demonstrated improvements in gait speed, with two exceeding the minimal clinically important difference. Secondary results showed similar outcomes for spasticity, strength, and trunk control, with no clinically meaningful changes in any case. Conclusions: The combined application of RAGT and tSCS appears to be a feasible and safe intervention for children with incomplete SCI. Preliminary findings suggest potential benefits in gait speed, thereby supporting the need for further investigation with larger samples and controlled study designs. Full article

The integration of collaborative robots into industrial environments requires rigorous safety validation under the Power and Force Limiting (PFL) regime. This review article systematically maps the technological and normative development of certified Pressure and Force Measurement Devices (PFMDs) and experimental biofidelic instruments for Physical Human–Robot Interaction (pHRI) between the years 2011 and 2026. A quantitative screening of 68 studies revealed a publication peak in impact metrology in 2021. This peak occurred with a five-year latency after the release of the ISO/TS 15066 technical specification. Although global interest in collaborative robotics steadily grows, the publication trend indicates a gradual shift in scientific focus from reactive testing toward proactive prevention. A methodological deconstruction of four Research Questions (RQs) identifies persistent limitations in safety evaluation. The findings demonstrate that the internal structure of conventional sensors induces nonlinear shock filtering and parasitic oscillations (RQ1). Furthermore, the rigid fixation of test stands generates unrealistic pressure spikes. This physical limitation forces a transition to flexible and pendulum-based configurations (RQ2). Commercial flat films physically fail due to sensor saturation and introduced stiffness. Such failures accelerate the development of conformable electronic skins (e-skins) and multimodal test manikins (RQ3). To ensure interlaboratory reproducibility within the current ISO 10218-2:2025 standard, the text defines imperative metrological parameters. These parameters strictly include frequency response, calibration protocols, and volumetric mapping of inertial masses (RQ4). Furthermore, the analysed publications were systematically stratified into distinct technological categories, strictly reflecting their primary engineering domains, ranging from empirical metrological evaluation and sensor hardware design to advanced numerical modeling. Finally, the vision for future research anticipates a definitive shift toward proactive anti-collision technologies, encompassing Artificial Intelligence (AI), machine vision, and Augmented Reality/Virtual Reality/Mixed reality (AR/VR/MR). Future methodologies must also consider demographic anisotropies and the cognitive fatigue of the human operator. Full article

The contact force between the ultrasound probe and human skin directly affects image quality, patient safety, and comfort. In infant developmental dysplasia of the hip (DDH) ultrasound examinations, higher force control precision is required, as infants have thin skin and soft cartilage that are easily deformed under excessive probe pressure. This paper proposes a comprehensive force control method for DDH ultrasound robots. Firstly, an online gravity calibration approach is employed to estimate the installation tilt, sensor zero offset, and probe center of gravity, thereby improving force measurement accuracy. Then, a torque-based pose control algorithm is adopted to achieve conformal probe–skin contact. Finally, a variable admittance control strategy based on fuzzy neural network (FNN) is proposed, which adaptively regulates the damping coefficient based on the force error and its rate, enabling stable force control without explicit soft-tissue modeling. Experiments on an infant phantom and human skin show that the proposed method achieves force fluctuation amplitudes of 0.0984 ± 0.0012 N and 0.0976 ± 0.0014 N, respectively, with absolute steady-state force errors below 0.01 N. Compared with conventional admittance control, it significantly reduces force oscillations and improves tracking accuracy. In infant experiments, the method enables smooth convergence to the desired force and maintains relatively stable probe–skin interaction, which contributes to consistent ultrasound image acquisition and reduces tissue deformation. These results suggest that the proposed method can provide a feasible force control basis for stable and gentle robotic DDH ultrasound scanning. Full article

This review summarizes the research on the end effectors of agricultural harvesting robots (2010–2025) and extracts two core design principles. First of all, the selection of end effectors must follow the biological characteristics of fruits: rigid grippers are suitable for hard skinned and regular fruits; soft grippers can reduce the damage of fragile crops to a certain extent; suction cups are suitable for smooth, barrier free surfaces; the envelope type is suitable for soft and lossless picking scenes; the combined suction and grip design is more suitable for unstructured environments. Secondly, the separation mode should match the characteristics of the stem: motion separation (torsion/pull) is suitable for weak stems, while cutting is mainly used for hard stems. Unlike previous literature, this review provides a field deployability checklist (including dust/water proofing, cleanliness, maintenance, aging prevention, and aspiration prevention) to narrow the results of the laboratory and the real field environment. The three future directions of multimodal perception, variable stiffness driving and reinforcement learning are logically related to the analysis in this paper: multimodal perception optimizes the perception limit, variable stiffness solves the rigid–flexible trade-off, and reinforcement learning provides adaptive strategies for different crops. This framework can match the end effector design with the crop-specific field conditions. Full article

In recent years, the deployment of robots in human-centric environments has necessitated the development of artificial skins that integrate safety and durability. Traditional damage detection often relies on raw signal thresholds, lacking the functional integration of touch, pain, and damage found in biological systems. This study proposes a bio-inspired artificial skin model that separately evaluates these three states through a spatio-temporal anomaly detection framework. We developed an unsupervised model combining a Convolutional Autoencoder (CAE) and Convolutional LSTM (ConvLSTM) to learn the latent representations of tactile maps from intact skin. By quantifying spatial reconstruction and temporal prediction errors, the system generates individual scores for touch, pain, and damage. Pain is defined as an abstract signal of instantaneous abnormality, while damage is identified as a persistent structural deviation. We implemented a dynamic thresholding mechanism mimicking biological sensitization and recovery, with damage detection gated by a pain-flag constraint to minimize false positives. Experimental results across various conditions—including incisions (3–6 cm) and abrasions (10–30 times)—demonstrate that the model can distinguish between momentary noxious stimuli and sustained structural degradation. Quantitative evaluation shows that the proposed model achieves an Area Under the Curve (AUC) of 0.653, outperforming a threshold-based baseline and maintaining zero false positives under strong, non-damaging contact. Specifically, the system successfully mimics biological aftereffects and the pain-gating mechanism, where damage is only assessed in the presence of a pain-related trigger. This research provides a scalable, software-driven foundation for robot self-protection that overcomes the implementation constraints of hardware-dependent neuromorphic systems. Full article

Hybrid stepper (HB) motors are widely used in precision actuation systems such as cryogenic refrigerator robotic arms. Under cryogenic working conditions, the core loss characteristics of magnetic materials change significantly, while conventional core loss models calibrated at room temperature can hardly provide reliable prediction accuracy. In this work, the electromagnetic properties of 35SW1900 non-oriented silicon steel were measured from 25 °C − 100 °C using a BROCKHAUS Epstein frame system. Variations in permeability, core loss and coercivity with magnetic flux density, temperature and frequency were obtained. An improved core loss model was developed by introducing a flux-dependent exponent and dual temperature correction coefficients for hysteresis and eddy current losses. Experiments place the prediction error of the proposed model within 4% under cryogenic conditions. Compared with the classical Bertotti model, the proposed model effectively reduces high-frequency deviation caused by the temperature-dependent material properties and skin effect. The core loss of silicon steel increases by 15–30% at −100 °C compared with room temperature, which is mainly attributed to the decrease in resistivity and the strengthening of domain wall pinning. This paper provides an accurate loss prediction method and design references for HB motors applied in ultralow temperature working conditions. Full article

Humanoid robots operating in proximity to humans require the ability to perceive and interpret emotional cues conveyed through touch to achieve safe, natural, and socially intelligent interaction. Conventional tactile sensing systems primarily focus on force or pressure detection and cannot infer affective intent, while frame-based deep learning models often suffer from high latency and energy consumption when deployed on embedded platforms. To address these limitations, this paper presents a neuromorphic AI-based multimodal electronic skin (e-skin) framework for emotion-sensitive touch perception in humanoid robots. The proposed system integrates pressure, temperature, and electrostatic sensing with a bio-inspired signal conditioning pipeline and a Spiking Neural Network (SNN) for event-driven, low-power processing. A custom multimodal tactile dataset was collected using the proposed e-skin prototype to model four emotional touch interactions: stress, neutral, comfort, and affection. Experimental results demonstrate that the proposed approach achieves a high emotion classification accuracy of up to 92%, with an average accuracy of 88.75% across all classes. The neuromorphic SNN significantly reduces inference latency to approximately 8 ms, compared to 38 ms for a conventional CNN-based model, while maintaining energy-efficient operation suitable for edge deployment. The results validate the effectiveness of combining multimodal tactile sensing with neuromorphic processing to enable real-time, emotion-aware human–robot interaction. Full article