Human beings’ dexterous control ability stems from the seamless collaboration between vision and touch. To enable intelligent manipulators to approach human levels in increasingly complex physical interaction tasks, manipulators with multimodal fusion of touch and vision have become an emerging research direction. However, traditional visual systems have inherent bottlenecks in contact dynamics issues such as fine force control, material identification, and slip detection, which limits their application reliability in unstructured environments. This review examines an emerging research direction: the current single-modal research on vision and touch to the multimodal collaborative mechanism of “global visual guidance and local tactile verification”, aiming to endow robots with dexterous control capabilities closer to those of humans. First, we explored the core hardware that supports this technology, ranging from high-resolution vision systems to diverse electronic skins, and the key computing models, from early convolutional neural networks to the Transformer architecture. Based on this, we propose a three-stage evolution framework, progressing through three stages: task performance enhancement, unified physical representation, and intelligent behavior guidance, in order to reveal the development context and core driving forces of this field. Finally, we discussed the challenges currently faced by fusion technologies, including cross-modal data discrepancies, the sim-to-real gap, and semantic discontinuities. Additional obstacles include system-level integration and computational scalability, and looked forward to future development opportunities.
Flexible structures exhibit unique behavior when subjected to fluid flow through deformation and interaction with the flow pattern. This two-way interaction offers significant advantages for aerodynamic and hydrodynamic propulsion and energy harvesting. Although extensive literature exists in this field, previous studies remain fragmented across different application domains and operating media that share common physics. This review, therefore, unifies these perspectives within a physics-based Fluid–Structure Interaction (FSI) framework. The framework reveals how structural deformation, oscillation, and proximity to resonance in different motion regimes govern the performance across distinct FSI phenomena. It also establishes the roles of stiffness distribution, motion amplitude, and phase synchronization in determining whether flexibility enhances stability, lift, thrust, and power extraction efficiency, or whether excessive compliance becomes detrimental to the system response. Using this framework, this study synthesizes how spanwise and chordwise flexibility influences flow separation and wake patterns, mitigates gust effects, enhances maneuverability and propulsion, and improves flow-induced energy harvesting through passive or active shape adaptation. Flexible propellers and hydrofoils offer similar benefits in hydrodynamic applications, such as noise reduction, cavitation delay, and vibration damping through the anisotropic properties of composite materials. For energy harvesting, structural compliance is exploited through various Flow-Induced Vibration (FIV) systems and piezoelectric transduction to power microdevices. A bibliometric analysis further reveals clear gaps in integrated FSI studies that explicitly link deformations to quantitative performance outcomes and underlying mechanisms. Finally, key challenges are identified, and future research directions are outlined to facilitate advancements in next-generation bioinspired and adaptive propulsion and energy-harvesting systems.
Stress shielding caused by metal implants may result in implant failure due to the mismatched mechanical properties between metal implants and bone. Meanwhile the bio-inertia nature of metal implants often leads to poor osseointegration. Herein, a triply-bioinspired strategy called ‘topology-micromorphology-component-trio’ is proposed to solve these two problems and to enable metal implants for drug delivery. First, to mimic the topology of spongy bone tissues, Ti-6Al-4V (TC4) scaffolds of Triply Periodic Minimal Surfaces (TPMS) lattices (Gyroid, Split-P and Schwarz) and strut-based lattices (Weaire Phelan and Diamond) were designed with Large (L) and Small (S) pores and manufactured by 3D printing. Second, various alkaline treatments were tested on TC4 to achieve a micromorphology with microfibers resembling blood clots. Third, an injectable hydrogel mimicking extracellular matrix components was infused into porous TC4 scaffolds. It was then photo-crosslinked to obtain TC4 scaffolds with interpenetrating Double Network (DN) hydrogel. Overall, the L-Gyroid lattice of TPMS shows superior 3D-printing manufacturability, mechanical properties, and cytocompatibility than the others. The alkaline treatment condition of 16.6 wt
Reliable physical interaction with unstructured natural environments remains a fundamental challenge for aerial robots, particularly in grasping and perching tasks. This paper presents a bio-inspired leg-claw system with active-passive coupling, enabling rapid passive perching and controllable active grasping. Inspired by the avian Tendon Locking Mechanism (TLM) and Automatic Digit Flexion Mechanism (ADFM), the proposed system integrates an independent digit self-locking mechanism based on a knot-ratchet-sprocket configuration with a leg-driven tendon actuation architecture. A rigid-soft gradient digit design is further adopted to enhance impact tolerance and surface adaptability. To evaluate system-level perching stability, a quasi-static slip model is developed to estimate the critical slip/tipping boundary under varying branch diameters. A quadrotor-mounted prototype is developed, and comprehensive experiments are conducted, including grasping adaptability, static load capacity, cyclic actuation and load-retention tests, response time, slip stability testing and calibrated trend-level model comparison, and flight demonstrations. Under the dry rigid Φ 10 mm branch load test, the proposed claw achieved an average static load capacity of 12.03 kg, corresponding to a single-side leg-claw-module-normalized load-to-weight ratio of 140.5. The cyclic actuation and load-retention test showed repeatable tendon actuation, while the fixed-load retention ratio decreased after 200 cycles owing to adhesive-interface failure between adjacent carbon-fiber plates of the ratchet-sprocket. Passive grasping and mechanical locking are completed within 48 ms. Together, these results demonstrate a compact aerial grasping and perching solution that combines rapid passive engagement, controllable release, and high load retention, while also identifying adhesive-interface failure of the current ratchet-sprocket as a structural limitation.
In order to achieve ultra-low and broadband frequency vibration suppression, a structurally compact and parameter-adaptable Multi-coupled Quadrupedal Bio-inspired Vibration Isolation platform (MQBVI) based on the mechanical features of the quadruped is proposed in this research. Due to the large working stroke and strong nonlinearity of the platform, the virtual work method and incremental harmonic balance method are utilized to systematically analyze the static and dynamic characteristics, respectively. The comparison validations of bio-inspired platform are conducted by Incremental Harmonic Balance Method (IHBM), ADAMS, numerical method and physical prototype. The investigation revealed that the restoring force and equivalent stiffness are nonlinear and asymmetrical, which lead to the MQBVI platform exhibits superior High Static Low Dynamic Stiffness (HSLDS) characteristics and strong stability. The vibration suppression performance of the MQBVI structure is investigated with installation angle, spring stiffness, connecting rod length, isolated mass and excitation amplitude, which exhibits low resonance frequency and wide effective vibration attenuation frequency band. Notably, the minimum critical values of resonance frequency and vibration suppression effect are reduced to 0.27 Hz and 0.39 Hz with α = 30°, respectively. Overall, the proposed MQBVI platform is an efficient ultra-low frequency vibration suppression structure with excellent adaptability, and the bio-inspired vibration buffering and isolation mechanism are revealed.
Surrogate-Assisted Evolutionary Algorithms (SAEAs) have emerged as a powerful paradigm for solving Expensive Multi-objective Optimization Problems (EMOPs), where fitness evaluations are computationally or financially costly. This review systematically examines recent advancements in SAEAs, focusing on their key components: surrogate model integration (e.g., Gaussian processes, neural networks, or radial basis functions), evolutionary search mechanisms, and techniques to balance exploration and exploitation under limited budgets. We categorize and analyze state-of-the-art approaches, including hierarchical, ensemble, and adaptive surrogate frameworks, as well as hybrid methods combining global and local approximations. Critical challenges such as model uncertainty management, high-dimensionality, and dynamic optimization are discussed, along with benchmarks and real-world applications in engineering design, healthcare, and energy systems. Finally, we identify open research directions, including scalable surrogates for many-objective problems, data-efficient training strategies, and the integration of SAEAs with emerging machine learning techniques. This survey provides a comprehensive reference for researchers and practitioners aiming to leverage SAEAs for complex, resource-intensive optimization tasks.
Transparent omniphobic surfaces integrating antifouling, anti-adhesion, and easy-cleaning properties are highly desirable for practical applications, yet combining strong fouling resistance with long-term durability remains challenging. Here, inspired by the slippery liquid-infused porous surfaces (SLIPS) of nepenthes pitcher plants, we report a bioinspired lubricated octa(glycidyldimethylsilylpropyl) silsesquioxane (GPOSS) coating. Our design mimics the plant’s dual-component architecture by constructing a highly cross-linked polymer network formed via nucleophilic addition between amine groups and an octa-epoxy POSS precursor-as the artificial scaffold, followed by infusion of polydimethylsiloxane (PDMS) as the mobile lubricant to create a liquid-like, low-adhesion interface. Owing to its highly cross-linked network, the coating exhibits excellent mechanical robustness, abrasion resistance (9 H pencil hardness), and chemical stability, while maintaining visible-light transmittance above 90
To clarify the superhydrophobic mechanism of Melilotus officinalis leaves and fabricate high-performance biomimetic superhydrophobic surfaces on carbon fiber reinforced polymer (CFRP), a series of experiments were carried out by integrating biomimetic structural design and systematic performance characterization. Firstly, the wettability, surface micromorphology and chemical compositions of natural Melilotus officinalis leaves were analyzed. Structural design and parameter optimization of high-strength biomimetic Melilotus officinalis leaf (BMOL) were completed via ANSYS simulation. Thermoplastic polyurethane (TPU) was adopted as the electrospinning matrix and silica (SiO₂) nanoparticles as the modifier. A superhydrophobic functional layer was constructed on CFRP substrates through the combined electrospinning and electrostatic spraying technique. Comprehensive characterizations on the microstructure, chemical composition and superhydrophobic performance of BMOL specimens were conducted, and their wetting mechanisms were systematically elaborated.Natural Melilotus officinalis leaves gain superhydrophobicity from the synergy of unique fiber-microsphere multi-scale roughness and intrinsic hydrophobic groups (benzene rings, methyl and methoxy). The BMOL coating reproduces this hierarchical texture and obtains low surface energy from C–C/C–H and Si–O bonds. Its water contact angle hits 158.65°, with great anti-adhesion and self-cleaning ability. After 30 water impact cycles, the contact angle stabilizes at 138.51°, demonstrating robust hydrophobic durability. The combined low-surface-energy TPU/SiO₂ system and electrospun-sprayed triple-scale rough structure jointly produce durable superhydrophobicity. This study offers a reliable route for superhydrophobic modification of CFRP surfaces.This study provides a novel idea for the superhydrophobic modification of CFRP surfaces.
Achieving realistic facial expressions is a central goal in humanoid robot head research. Current methods for determining facial drive points, often based on FACS and design experience, are subjective and prone to bias. This study proposes a quantitative method for selecting facial drive point positions based on optical motion capture and interpolation reconstruction, providing a set of 3D coordinates. On this basis, we further propose a rapid positioning method for robotic drive points applicable to diverse human bionic subjects. Motion data of 238 reflective markers were captured from volunteers performing six basic and one arbitrary facial expression to represent facial movement characteristics. Drive areas were defined based on facial muscle distribution and movement patterns, and the maximum displacement point in each area was calculated using TPS interpolation and displacement field reconstruction to obtain full-face drive point coordinates. For diverse human bionic subjects, proportional correspondences between their inherent facial features and the drive point parameters established in this study were calculated, and new facial drive points were positioned through scaling. To validate the approach, based on the results, a 19-degree-of-freedom humanoid head prototype was built, realizing six basic expressions with recognition rates exceeding 90
In the field of optimization, one of the fundamental challenges of existing methods lies in their inability to dynamically adapt to the diverse characteristics of objective functions. Many algorithms rely on a fixed optimization strategy; therefore, while they may perform well on certain problems, they often lose efficiency when dealing with functions exhibiting different behaviors, such as smooth or steep landscapes. To overcome this limitation, this study introduces a smart and self-adaptive approach called the Q-Learning-Based Optimization Algorithm (QLOA). The proposed QLOA incorporates a set of diverse mathematical formulations designed to enhance both the exploration and exploitation phases. A key feature of QLOA lies in the integration of the Q-learning framework, which enables the algorithm to intelligently and adaptively select the most suitable formulation based on the problem characteristics and its performance feedback, thereby establishing a dynamic balance between exploration and exploitation at each stage of the optimization process. In addition, population refinement mechanisms—such as eliminating redundant members and applying structural adjustments—prevent the algorithm from becoming trapped in local optima. To further enhance adaptability in problems with hard and soft constraints, an extended version named QLOA with Constraint Management (QLOA-CM) is developed. Experimental results on standard benchmark functions and the CEC2019 and CEC2022 test suites demonstrate that QLOA outperforms reference algorithms in both the Friedman and Wilcoxon statistical tests, confirming its efficiency, robustness, and statistically significant superiority over existing methods. Final evaluations of QLOA-CM on engineering optimization problems and real-world challenges from CEC2020, alongside QLOA’s successful application in image segmentation tasks, further validate the effectiveness and generalization capabilities of the proposed approaches in solving complex optimization problems. The source code of the proposed algorithm is publicly available at https://github.com/MSNFV/QLOA-Q-Learning-based-Optimization-Algorithm .
While piezoelectric positioning platforms show significant potential in micro/nano actuation, their widespread adoption is often limited by inherent structural complexity. To address this, we proposed a cross-scale friction-differential stepping platform driven by only two piezoelectric bimorph benders. Utilizing a specially designed T-shaped structure, the platform achieved both linear and rotational motion through an innovative friction-differential principle. Following theoretical and dynamic simulation analyses, a prototype was fabricated. Experimental results demonstrate stable performance and high repeatability. For linear motion, the resolution was 2.853 µm; at 150 V (no load), the maximum speed reached 3.306 mm/s (30 Hz), with a return deviation of 348.718 µm (1 Hz). For rotational motion, the resolution was 0.063 mrad; the peak angular velocity attained 136.551 mrad/s (15 Hz), with a return deviation of 7.598 mrad. Furthermore, the platform exhibited a maximum payload-to-weight ratio of 63.49. This design offers a simple, redundancy-free 2-DOF solution, significantly reducing structural complexity compared to traditional platforms.
To improve the radial mechanical performance and reduce the collapse risk of poly(L-lactic acid) (PLLA) vascular stents, this study proposes a novel stent design integrating the negative Poisson’s ratio (NPR) effect with a bio-inspired coconut palm (CP) cell structure. An anisotropic multi-plate crimping model incorporating the Hill yield criterion is developed to accurately capture the radial mechanical behavior of PLLA stents. The effects of different cell configurations on radial stiffness, strength, specific stiffness, and specific strength are investigated, and the deformation mechanisms of different cell structures are revealed. Results indicate that, compared with a typical PLLA stent, the NPR PLLA stent exhibits significantly enhanced radial mechanical performance. Moreover, the multi-cell structures combining CP and NPR cells further improve the radial stiffness and strength of the stent. The coupled deformation effect between the CP and NPR cells effectively suppresses buckling instability, resulting in a more stable crimping process and regular deformation behavior. Based on these findings, a novel NPR-nested CP double-cell structure is proposed with superior radial mechanical performance. Post-deployment performance is further evaluated by analyzing stent recoil and the corresponding arterial mechanical response. This work provides insights and technical guidance for the structural design and radial mechanical enhancement of PLLA vascular stents.
Aiming at the problems of existing legged robot cushioning pads, such as severe contact vibration, low energy absorption efficiency, and poor adaptability to complex terrain in dynamic impact scenarios, this study proposes a bionic cushioning pad design inspired by the jerboa toe pad with efficient cushioning capacity. The contour curves and spatial distribution characteristics of the fat compartments inside the middle toe pad of jerboas were accurately extracted, and a composite structure model of “bionic shell - cushioning filler” was constructed. Numerical simulations were performed to systematically analyze the influence of hardness ratio and structural parameters of the shell and filler on cushioning performance. Results indicate that optimal comprehensive performance is achieved when the cushioning filler is soft silicone (elastic modulus 0.75 MPa) and the bionic shell is hard silicone (elastic modulus 3 MPa): peak acceleration is reduced by 32.93
Despite the exceptional maneuverability of hover-capable tailless flapping-wing micro air vehicles, the inherent complexity of their unsteady aerodynamics poses significant challenges for developing precise dynamic models for controller design. To address this, this paper proposes ground-based damping pendulum experiments to construct a full damping dynamic model that incorporates parasitic drag damping. The parasitic drag damping is decoupled from flapping-induced damping by experimentally measuring the damping coefficients across three configurations: with wings and flapping, with wings and no flapping, and without wings. Quantitative analysis reveals that, compared with a model that considers only flapping-induced damping, the full damping dynamic model predicts the oscillation periods of longitudinal and lateral unstable modes decrease by 4.27
This study presents a numerical simulation utilizing the Levenberg–Marquardt backpropagation (LMBP) algorithm to examine the radiative flow of hybridized cobalt ferrite ( CoFe_2 O_4) and magnetite ( Fe_3 O_4) nanofluids over a permeable stretching surface, with a focus on the Forchheimer effect. Hybrid nanofluids, which consist of nanoparticles with distinct thermophysical properties, are known to exhibit enhanced heat transfer performance compared to conventional single-phase fluids. The incorporation of radiation heat transfer and the Forchheimer effect into the fluid model offers a more comprehensive representation of complex heat transport processes relevant to industrial applications. A novel hybrid nanofluid composed of cobalt ferrite and magnetite nanoparticles is introduced, demonstrating a significant improvement in the thermal characteristics of the base fluid. A similarity transformation was applied to convert the governing partial differential equations into ordinary differential equations (ODEs). The impacts of various parameters on the flow field were investigated and visualized through graphical plots. Numerical solutions obtained via the BVP4C (boundary value problem 4C) method was compared with existing literature, showing excellent agreement. The findings contribute to the optimization of thermal management systems in advanced engineering and industrial processes. Additionally, the LMBP algorithm was validated through error analysis, regression metrics, adaptive control parameter evaluations, and iterative learning curve studies, demonstrating convergence, accuracy, and efficiency. Performance verification was conducted using regression plots, mean squared error (MSE), and error histograms, ensuring the robustness of the LMBP approach in solving the problem. In addition, the numerical solution is obtained by training the network with the LMPB algorithm by developing an accurate surrogate using 70 4 · 04419 × 10^ - 9 at epoch 890 , with errors closely grouped around zero and an overall regression coefficient R ≅ 1 across the process demonstrating excellent agreement with the numerical targets.
Traditional diagnostic methods for Blood Cells (BCs) and Cervical Cells tend to erroneous analysis and inaccurate diagnoses due to the complexity involved in overlapping regions of cells. Deep Learning (DL) techniques through Convolutional Neural Networks (CNNs) have gained prominence in addressing these issues but encounter challenges as inefficient feature attention, and high computational processing power. To tackle these inefficiencies, this paper presents a novel computationally efficient lightweight architecture (EAL-Net) for BCs diagnosis, which is based on lightweight multi-attention modules and Orthogonal SoftMax Layer (OSL). OSL functionality precisely mitigates parameter co-adaptation to ensure orthogonality among weight vectors, thus refining feature learning processes. The developed EAL-Net architecture is equipped with novel Lightweight Attention Mechanism (LWAM) to selectively learn discriminative features and enabling more precise attention on diagnostic relevant regions. LWAM integrates Spatial-Attention Module (SAM) to focus on key regions, Spatial Self-Attention Module (SSAM) to capture long-range dependencies, and Category-Attention Module (CAM) to emphasize class-specific features. Collectively, this approach amplifies feature attention and strengthening spatial and channel-wise dependencies, significantly improving the performance of neural network architecture. Moreover, Genetic Algorithms (GA) efficiently search the optimal set of hyperparameters iteratively. GA is chosen for its efficacy in handling complex search spaces and non-linear optimization problems. Additionally, Grad-CAM and SHAP provide model insights by highlighting key features influencing predictions. EAL-Net outperforms pre-trained CNNs, transformer-based architectures, and existing methods across accuracy, efficiency, and memory use. With only 0.73 million parameters and 8.2 MB size, it achieves 96.11
Systemic Lupus Erythematosus (SLE) is a long-term autoimmune disease where the immune system mistakenly targets and damages healthy tissues and organs. Prevailing models delayed the diagnosis and failed to detect the disease in its early stages, which led to worse patient outcomes and less effective treatments. Therefore, the Xception Convolutional Neural Network with Black-Winged Frigate Optimization Algorithm (XCovNet_Bla-WFOA) is developed for effective SLE detection. First, the input data is collected from the database, and then data normalization is done by Z-score normalization. Besides, relevant features are selected by the proposed Black-Winged Frigate Optimization Algorithm (Bla-WFOA), which fuses the strengths of Magnificent Frigatebird Optimization (MFO) and Black-Winged Kite Algorithm (BKA). Next, data augmentation is accomplished by the Synthetic Minority Over-sampling Technique (SMOTE). Consequently, Lupus Erythematosus detection is implemented by Xception Convolutional Neural Network (XCovNet), where the newly established Bla-WFOA is exploited for optimizing the XCovNet to enhance the detection process. Finally, the Explainable Artificial Intelligence-SHapley Additive exPlanations (XAI-SHAP) are utilized to interpret the detected results. The XCovNet_Bla-WFOA model obtained an accuracy of 93.877
Passive Lower-Limb Exoskeletons (PLLEs) have emerged as a frontier in the fields of assistive locomotion and rehabilitation robotics due to their significant advantages in biomechanical compatibility, energy efficiency, and system lightweight design. This paper systematically classifies and reviews the research progress on PLLEs over recent decades, focusing on the mechanical power transmission mechanisms and system architectures for locomotion-load assistance. By utilizing a bio-inspired evaluation framework based on human lower-limb load regulation mechanisms, the study addresses the limitations of exoskeleton assistance performance. It highlights the significant impact of human–machine locomotion coupling effects on stress distribution in lower limb joints and the collaboration between humans and exoskeletons, which hinders their application in rehabilitation and prevention. The study proposes an integrated continuous metamorphic mechanism in a biomimetic design to enhance human–machine cooperation of PLLEs by achieving passive compliant dynamic responses aligned with lower limb biomechanics. Two potential research directions are then suggested: I. Bio-kinematic dimensional synthesis of PLLEs considering wearable uncertainties for workspace dexterity and accommodation pre-metamorphosis; II. Bio-dimensional synthesis based on PLLEs' rigid-flexible coupled dynamics for axial impact buffering control during metamorphic process of lower limbs.
The segmentation of brain tumors, based on hyperspectral imaging (HSI), has been attracting considerable interest for its capability of providing detailed spectral–spatial information about tissue. Current segmentation methods based on HSI have limitations such as spectral noise, illumination changes, high dimensionality of data, and temporal variation between successive surgical frames, which can impact the accuracy of segmentation and its clinical utility. In this work, we suggest a dynamic spectral calibration (DSC) integrated with diffusion-based temporal fusion (DTF) framework to overcome these limitations and achieve interpretable and low latency brain tumor segmentation in HSI. It is based on a spectral-attentive denoising stage using a deep convolutional autoencoder to remove spectral noise while maintaining the most important biochemical and morphological tissue properties. Then, a diffusion-assisted spectral–spatial tumor mapping module is used to iteratively refine the tumor boundary using a reverse diffusion method. A DSC module adaptively recalibrates the spectral responses based on a gradient guided spectral prior optimization to increase robustness in varying illumination conditions during surgery. Moreover, the Hierarchical Spatial–Temporal Integration module integrates multi-scale spatial representations and temporal feature learning via recurrent attention mechanisms and gated recurrent units to ensure temporal coherence between successive frames. The explainable artificial intelligence module is also integrated to offer interpretable visualization of spectral and spatial decision patterns, offering clinical transparency and confidence in the model predictions. Experimental results on hyperspectral brain tumor datasets show that the proposed DSC–DTF framework provides competitive segmentation results with dice similarity coefficient of 97.12
Given the poor efficacy of conventional oral medications in treating inner ear disorders due to the presence of the Blood-Labyrinth Barrier (BLB) in the inner ear, we designed an inner ear injectable Andro@GelMA hydrogel for the repair of Diabetic Hearing Loss (DHL). Natural protein-derived materials, such as collagen and gelatin, hold broad prospects in tissue engineering and regenerative medicine. Specifically, Gelatin Methacryloyl Acetate (GelMA), a photosensitive polymer obtained by modifying gelatin with methacrylic anhydride, inherits the high biocompatibility of collagen, making it an ideal drug delivery carrier. In this study, FTIR, XRD, SEM, and rheological tests demonstrated that hydrogen-bonding linkages formed between Andrographolide (Andro) and GelMA, thereby enhancing cross-linking degree and structural stability. Additionally, in vitro and in vivo tests confirmed that the Andro@GelMA hydrogel could achieve controllable sustained release by adjusting the cross-linking degree. Furthermore, in vitro cellular experiments showed strong protective effects on auditory cells, while animal experiments verified that this local delivery system significantly improved the therapeutic efficacy of DHL and promoted the regeneration of damaged inner ear hair cells compared to the control group.
