Search Results (117)

The inlet pressure steel pipe is an important part of the hydropower unit, and its inspection tasks mainly include cleaning with high-pressure water, surface anti-corrosion layer detection and internal flaw detection. In order to accomplish the above tasks effectively, a multifunctional, non-contact magnetic, tracked climbing robot is presented in this paper. Focusing on the pressure steel pipe inspection tasks, the design of the climbing robot system is given, including the mechanism and control system. By analyzing the slippage and overturning situations, the magnetic attraction constraints for reliable adhesion are obtained, which are used as the basis for designing magnetic adhesion modules. To enable climbing robots to meet the requirement of following the welding seam during the inspections, the improved Deeplabv3+ semantic segmentation method is proposed for welding seam recognition. Experiment results show that the climbing robot can achieve reliable adsorption and flexible movement on the internal face of inlet pressure steel pipe, and the climbing robot can meet the requirements of safety and efficiency for pressure steel pipe inspection processes. Full article

This study presents a novel strategy for designing photocurable resins tailored for the additive manufacturing of smart thermoset materials. A quaternary formulation was developed by integrating bis(2-methacryloyl)oxyethyl disulfide (DADS) with an epoxy/thiol-ene system (ETES) composed of diglycidyl ether of bisphenol A (EP), pentaerythritol tetrakis(3-mercaptopropionate) (PTMP), and 4,4′-methylenebis(N,N-diallylaniline) (ACA4). This unique combination enables the simultaneous activation of four polymerization mechanisms: radical photopolymerization, thiol-ene coupling, thiol-Michael addition, and anionic ring-opening, within a single resin matrix. A key innovation lies in the exothermic nature of DADS photopolymerization, which initiates and sustains ETES curing at room temperature, enabling 3D printing without thermal assistance. This represents a significant advancement over conventional systems that require elevated temperatures or post-curing steps. The resulting hybrid poly(acrylate–co-ether–co-thioether) network exhibits enhanced mechanical integrity, shape memory behavior, and intrinsic self-healing capabilities. Dynamic Mechanical Analysis revealed a shape fixity and recovery of 93%, while self-healing tests demonstrated a 94% recovery of viscoelastic properties, as evidenced by near-overlapping storage modulus curves compared to a reference sample. This integrated approach broadens the design space for multifunctional photopolymers and establishes a versatile platform for advanced applications in soft robotics, biomedical devices, and sustainable manufacturing. Full article

The convergence of carbon nanomaterials and functional polymers has led to the emergence of smart carbon–polymer nanocomposites (CPNCs), which possess exceptional potential for next-generation sensing and actuation systems. These hybrid materials exhibit unique combinations of electrical, thermal, and mechanical properties, along with tunable responsiveness to external stimuli such as strain, pressure, temperature, light, and chemical environments. This review provides a comprehensive overview of recent advances in the design and synthesis of CPNCs, focusing on their application in multifunctional sensors and actuator technologies. Key carbon nanomaterials including graphene, carbon nanotubes (CNTs), and MXenes were examined in the context of their integration into polymer matrices to enhance performance parameters such as sensitivity, flexibility, response time, and durability. The review also highlights novel fabrication techniques, such as 3D printing, self-assembly, and in situ polymerization, that are driving innovation in device architectures. Applications in wearable electronics, soft robotics, biomedical diagnostics, and environmental monitoring are discussed to illustrate the transformative impact of CPNCs. Finally, this review addresses current challenges and outlines future research directions toward scalable manufacturing, environmental stability, and multifunctional integration for the real-world deployment of smart sensing and actuation systems. Full article

Liquid crystalline block copolymers (LCBCPs) have emerged as an adaptable hybrid class at the intersection of self-assembling block copolymers and liquid crystalline ordering, producing multi-tiered architectures that can be finely programmed for multifunctional performance. This review surveys recent advances in their structure–property relationships and highlights applications spanning nanotechnology, biomedical systems, flexible photonics, stimuli-responsive, energy storage, and soft robotics. Particular emphasis is placed on how molecular design enables precise tuning of structural, optical, mechanical, and stimuli-responsive functions, positioning LCBCPs as strong candidates for next-generation functional materials. We also discuss current challenges, including scalability, phase control, and advanced characterization, and outline promising research directions to accelerate their translation from laboratory concepts to real-world technologies. Full article

A novel stretchable flexible electrode capable of simultaneously detecting isotropic three-directional strain and normal pressure has been developed. Inspired by the recursive symmetry of the Peano-Gosper fractal, the electrode integrates liquid metal (EGaIn) microchannels within a PDMS matrix to achieve uniform strain distribution and mechanically robust conductive pathways under large deformation. Within a strain range of 0–60%, the electrode exhibits highly consistent three-directional responses, with resistance variation across axes kept below 4% and a gauge factor (GF) standard deviation of only 0.0252. The device demonstrates low hysteresis (minimum DH = 0.94%), good cyclic durability, and reliable electromechanical stability. For normal pressure sensing (0–20 kPa), it provides a linear response (R² ≈ 0.99) with a moderate sensitivity of 0.198 kPa⁻¹. Wearable tests on the wrist, finger, and fingertip confirm the electrode’s reliable operation in multidimensional mechanical monitoring. This integrated fractal–liquid metal design offers a promising route for multifunctional sensing in applications such as soft robotics, human–machine interaction, and wearable electronics. Full article

Improving the decoding accuracy of biological signals has been a research focus for decades to advance health, automation, and robotic industries. However, challenges like inter-subject variability, data scarcity, and multifunctional variability cause low decoding accuracy, thus hindering the practical deployment of biological signal paradigms. This paper proposes a multifunctional biological signals network (Multi-BioSig-Net) that addresses the aforementioned issues by devising a novel blind few-shot learning (FSL) technique to quickly adapt to multiple target domains without needing a pre-trained model. Specifically, our proposed multimodal similarity extractor (MMSE) and self-multiple domain adaptation (SMDA) modules address data scarcity and inter-subject variability issues by exploiting and enhancing the similarity between multimodal samples and quickly adapting the target domains by adaptively adjusting the parameters’ weights and position, respectively. For multifunctional learning, we proposed inter-function discriminator (IFD) that discriminates the classes by extracting inter-class common features and then subtracts them from both classes to avoid false prediction of the proposed model due to overfitting on the common features. Furthermore, we proposed a holistic-local fusion (HLF) module that exploits contextual-detailed features to adapt the scale-varying features across multiple functions. In addition, fractal dimension estimation (FDE) was employed for the classification of left-hand motor imagery (LMI) and right-hand motor imagery (RMI), confirming that proposed method can effectively extract the discriminative features for this task. The effectiveness of our proposed algorithm was assessed quantitatively and statistically against competent state-of-the-art (SOTA) algorithms utilizing three public datasets, demonstrating that our proposed algorithm outperformed SOTA algorithms. Full article

Agriculture is facing increasing challenges due to labor shortages, rising productivity demands, and the need to operate in unstructured environments. Robotics, particularly soft robotics, offers promising solutions for automating delicate tasks such as fruit harvesting. While numerous soft grippers have been proposed, most focus on grasping and lack the capability to detach fruits with rigid peduncles, which require cutting. This paper presents a novel modular hexagonal soft gripper that integrates soft pneumatic actuators, embedded mechano-optical force sensors for real-time contact monitoring, and a self-centering iris-type cutting mechanism. The entire system is 3D-printed, enabling low-cost fabrication and rapid customization. Experimental validation demonstrates successful harvesting of bell peppers and identifies cutting limitations in tougher crops such as aubergine, primarily due to material constraints in the actuation system. This dual-capability design contributes to the development of multifunctional robotic harvesters capable of adapting to a wide range of fruit types with minimal requirements for perception and mechanical reconfiguration. Full article

Rapid developments in intelligent interfaces across service, healthcare, and industry have led to unprecedented demands for advanced tactile perception systems. Traditional tactile sensors often struggle with adaptability on curved surfaces and lack sufficient feedback for delicate interactions. Flexible and wearable tactile sensors are emerging as a revolutionary solution, driven by innovations in flexible electronics and micro-engineered materials. This paper reviews recent advancements in flexible tactile sensors, focusing on their mechanisms, multifunctional performance and applications in health monitoring, human–machine interactions, and robotics. The first section outlines the primary transduction mechanisms of piezoresistive (resistance changes), capacitive (capacitance changes), piezoelectric (piezoelectric effect), and triboelectric (contact electrification) sensors while examining material selection strategies for performance optimization. Next, we explore the structural design of multifunctional flexible tactile sensors and highlight potential applications in motion detection and wearable systems. Finally, a detailed discussion covers specific applications of these sensors in health monitoring, human–machine interactions, and robotics. This review examines their promising prospects across various fields, including medical care, virtual reality, precision agriculture, and ocean monitoring. Full article

Currently, soft robots face challenges such as low motion efficiency, susceptibility to damage in traditional silicone materials, and difficulty in achieving reproducible manufacturing. To address these issues, we integrate flexible film materials with modular design principles and apply them to soft robotics. Based on the concept of modularity, this study simplifies and decomposes the robot’s motion into three fundamental modules: a thin-film elongation actuator module, a thin-film deflection actuator module, and a connection module. Inspired by the Miura-fold origami technique and traditional lantern contraction, the elongation actuator is designed to produce axial extension of varying lengths under different air pressures. The deflection actuator is modeled after the head expansion mechanism of the pelican eel, enabling deflection movement. The connection module integrates the elongation and deflection modules into a unified structure. The research results show that the elongation actuator achieves an extension length of 118 mm under 50 kPa and can pull a 500 g load during horizontal contraction. The two-layer deflection actuator achieves a deflection angle of 56° at 40 kPa, while the three-layer version reaches 98°. For further demonstration, we subsequently conducted peristaltic soft robot experiments and obstacle avoidance experiments. This study holds significant potential for the development of next-generation multifunctional soft robots. Full article

Flexible sensors have emerged as essential components in next-generation technologies such as wearable electronics, smart healthcare, soft robotics, and human–machine interfaces, owing to their outstanding mechanical flexibility and multifunctional sensing capabilities. Despite significant advancements, challenges such as the trade-off between sensitivity and detection range, and poor signal stability under cyclic deformation remain unresolved. To overcome the aforementioned limitations, this work introduces a high-performance soft sensor featuring a dual-layered electrode system, comprising silver nanoparticles (AgNPs) and a composite of multi-walled carbon nanotubes (MWCNTs) with carbon black (CB), coupled with a laser-engraved crack-gradient microstructure. This structural strategy facilitates progressive crack formation under applied strain, thereby achieving enhanced sensitivity (1.56 kPa⁻¹), broad operational bandwidth (50–600 Hz), fine frequency resolution (0.5 Hz), and a rapid signal response. The synergistic structure also improves signal repeatability, durability, and noise immunity. The sensor demonstrates strong applicability in health monitoring, motion tracking, and intelligent interfaces, offering a promising pathway for reliable, multifunctional sensing in wearable health monitoring, motion tracking, and soft robotic systems. Full article

A mobile manipulation robot combines the navigation capability of unmanned ground vehicles and manipulation advantage of robotic arms. However, the development of a mobile manipulation robot is challenging due to the integration requirement of numerous heterogeneous subsystems. In this paper, we propose a multifunctional mobile manipulation robot by integrating perception, mapping, navigation, object detection, and grasping functions into a seamless workflow to conduct search-and-fetch tasks. To realize navigation and collision avoidance in complex environments, a new hierarchical motion planning strategy is proposed by fusing global and local planners. Control Lyapunov Function (CLF) and Control Barrier Function (CBF) are employed to realize path tracking and to guarantee safety during navigation. The convolutional neural network and the gripper’s kinematic constraints are adopted to construct a learning-optimization hybrid grasping algorithm to generate precise grasping poses. The efficiency of the developed mobile manipulation robot is demonstrated by performing indoor fetching experiments, showcasing its promising capabilities in real-world applications. Full article

Stretchable display technology has rapidly evolved, enabling a new generation of flexible electronics with applications ranging from wearable healthcare and smart textiles to implantable biomedical devices and soft robotics. This review systematically presents recent advances in stretchable displays, focusing on intrinsic stretchable materials, wavy surface engineering, and hybrid integration strategies. The paper highlights critical breakthroughs in device architectures, energy-autonomous systems, durable encapsulation techniques, and the integration of artificial intelligence, which collectively address challenges in mechanical reliability, optical performance, and operational sustainability. Particular emphasis is placed on the development of high-resolution displays that maintain brightness and color fidelity under mechanical strain, and energy harvesting systems that facilitate self-powered operation. Durable encapsulation methods ensuring long-term stability against environmental factors such as moisture and oxygen are also examined. The fusion of stretchable electronics with AI offers transformative opportunities for intelligent sensing and adaptive human–machine interfaces. Despite significant progress, issues related to large-scale manufacturing, device miniaturization, and the trade-offs between stretchability and device performance remain. This review concludes by discussing future research directions aimed at overcoming these challenges and advancing multifunctional, robust, and scalable stretchable display systems poised to revolutionize flexible electronics applications. Full article

Wearable sensors have rapidly advanced, enabling applications such as human activity monitoring, electronic skin, and biomimetic robotics. To meet the growing demands of these applications, multifunctional sensing has become essential for wearable devices. However, most existing studies predominantly focus on enhancing single-function sensing capabilities. This study introduces a multifunctional sensor that combines high stretchability for strain and pressure detection with ultraviolet (UV) sensing capability. To achieve simultaneous detection of strain, pressure, and UV light, a multi-sensing approach was employed: a capacitive method for strain and pressure detections and a resistive method utilizing a pn-heterojunction diode for UV detection. In the capacitive method, polyaniline (PANI) served as parallel-plate electrodes, while silicon-based elastomer acted as the dielectric layer. This configuration enabled up to 100% elongation and enhanced operational stability through encapsulation. The sensor demonstrated a strong linear relationship between capacitance value changes reasonably based on the area of PANI, and showed a good linearity with an R-squared value of 0.9918. It also detected pressure across a wide range, from low (0.4 kPa) to high (9.4 kPa). Furthermore, for wearable applications, the sensor reliably captured capacitance variations during finger bending at different angles. For UV detection, a pn-heterojunction diode composed of p-type silicon and n-type zinc oxide nanorods exhibited a rapid response time of 6.1 s and an on/off ratio of 13.8 at −10 V. Durability under 100% tensile strain was confirmed through Von Mises stress calculations using finite element modeling. Overall, this multifunctional sensor offers significant potential for a variety of applications, including human motion detection, wearable technology, and robotics. Full article

Sustainability and resource optimization have spurred interest in giving a second life to used equipment, often discarded after limited use. Within this framework, we conducted a multidisciplinary, final-year engineering project to explore the reverse engineering and repurposing of commercial hoverboards for an auto-stabilizing, modular robotic platform, with emphasis on medical applications such as transporting medication. The innovation lies in recycling hoverboards to develop a teleoperated, stabilized base that can accommodate additional modules—for instance, a multifunctional arm or a transport shelf—akin to existing commercial robots. Our methodology involves disassembling and reprogramming the hoverboard’s motor controllers and sensors to maintain horizontal stability. Control is realized through the sensor fusion of accelerometer and gyroscope data, processed by a Kalman filter and implemented in a Proportional-Integral-Derivative (PID) loop. A user-friendly Human-Machine Interface (HMI), hosted on an ESP32 microcontroller, enables remote operation and monitoring. Experimental results show that the platform autonomously balances, carries payloads, and achieves high energy efficiency, highlighting its potential as a sustainable and versatile solution in modular robotic applications. Full article

Glove-based human–machine interfaces (HMIs) offer a natural, intuitive way to capture finger motions for gesture recognition, virtual interaction, and robotic control. However, many existing systems suffer from complex fabrication, limited sensitivity, and reliance on external power. Here, we present a flexible, self-powered glove HMI based on a minimalist triboelectric nanogenerator (TENG) sensor composed of a conductive fabric electrode and textured Ecoflex layer. Surface micro-structuring via 3D-printed molds enhances triboelectric performance without added complexity, achieving a peak power density of 75.02 μW/cm² and stable operation over 13,000 cycles. The glove system enables real-time LED brightness control via finger-bending kinematics and supports intelligent recognition applications. A convolutional neural network (CNN) achieves 99.2% accuracy in user identification and 97.0% in object classification. By combining energy autonomy, mechanical simplicity, and machine learning capabilities, this work advances scalable, multi-functional HMIs for applications in assistive robotics, augmented reality (AR)/(virtual reality) VR environments, and secure interactive systems. Full article