Journal of Bionic Engineering最新文献

Inspired by the loofah sponge’s axially continuous porous core and peripheral hexagonal scaffold, we propose a compact bio-inspired low-frequency vibration isolator. An analytical static model reveals a Quasi-Zero-Stiffness (QZS) region produced by parallel coupling of an axial positive-stiffness spring and a nonlinear hexagonal link–spring unit. A Lagrangian dynamic model and ADAMS multibody simulations predict resonance and transmissibility and are validated by sinusoidal base-displacement tests. With a 5.775 kg payload, the prototype achieves effective isolation above ~ 5 Hz and ~ 25–30 dB attenuation at 20–22 Hz while retaining comparable load capacity to linear references of similar size. Compared with linear isolators, the designed bio-inspired low-frequency vibration isolator exhibits a lower isolation onset and a broader useful bandwidth under a compact footprint, offering tunable low-frequency isolation via geometric and stiffness parameters (α, k₁, k₃, l).

Icing is a common natural phenomenon. However, it is widespread in various fields, including aerospace, power facilities, and others, where it can lead to equipment failure, reduced energy efficiency, and even severe safety accidents. In recent years, biomimetic superhydrophobic materials inspired by the surface of lotus leaves have shown significant potential for applications in anti-icing and de-icing due to their excellent hydrophobicity and low surface energy. This paper systematically introduces the anti-icing mechanisms of superhydrophobic coatings and provides a review of recent research progress on superhydrophobic materials in the field of anti-icing. Additionally, the application of these materials in three scenarios: wind turbine blades, power transmission lines, and aerospace equipment, was analysed to assess their prospects and feasibility in engineering projects. Finally, the current status of superhydrophobic anti-icing materials and the challenges they face are summarised, highlighting existing issues and offering perspectives for future research directions.

Biological systems such as mountain goats and felines exhibit remarkable agility and adaptability when traversing complex terrains. Inspired by these capabilities, quadruped robots have been developed to mimic legged locomotion and improve mobility over uneven environments. To further enhance locomotion efficiency and terrain versatility, wheeled-legged robots integrate wheels and legs into a hybrid platform, enabling both high-speed traversal and robust ground contact in unstructured terrain. However, planning coordinated locomotion across diverse terrains remains challenging due to the nonlinear dynamics, complex terrain contact constraints, and multimodal locomotion capabilities. In this paper, we propose a real-time, integrated planning framework that jointly optimizes gait scheduling, footstep placement, and whole-body motion trajectories. Our method adopts a two-stage approach. First, a sampling-based planner generates candidate gait sequences and nominal footstep targets based on terrain features and kinematic feasibility. Second, a constrained trajectory optimizer reformulates the planning problem as a Quadratic Programming (QP) task to compute dynamically feasible base trajectories and corresponding ground reaction forces. This hybrid formulation balances planning efficiency and physical realism. The planned trajectories and contact forces are tracked using a hierarchical control architecture combining Model Predictive Control (MPC) and Whole-Body Control (WBC), enabling fast and stable execution on real hardware. Simulation and real-world experiments demonstrate that our approach enables adaptive gait transitions and improves terrain adaptability compared to traditional planners.

Achieving high drive efficiency remains a significant challenge in active knee prosthesis design. Inspired by human knee biomechanics, this study presents a novel biomimetic hydraulic drive system integrated with three human-like mechanisms: antagonistic muscle driving mechanism, dynamic simulation of muscle forces, and multi-stage collaborative energy supply. The system features a multi-stage hydraulic-rope hybrid transmission enabling adjustable damping and compliant motion control, coupled with a dual-cylinder configuration that boosts driving efficiency while delivering 29.7 Nm peak torque. A pump-valve hybrid control strategy is developed to dynamically adjust the flow and driving torque across gait phases, enhancing response speed and angular tracking accuracy. Through computational modeling, simulation, and prototype validation, we demonstrate that the proposed hydraulic drive system achieves efficient and responsive knee flexion and extension while meeting functional demands, reducing energy consumption by 20–50% compared to traditional pump-controlled systems. This study introduces a novel strategy for developing multimodal muscle-joint collaborative mechanisms, establishing a foundational framework for next-generation, high-performance bioinspired prostheses.

The pain induced by the friction between residual limb and prosthetic socket is the significant issue that influence the comfort of prostheses. In this study, to reveal and compare the pain generation and processing mechanisms in amputees and healthy individuals, the skin friction, electroencephalogram, and functional Near-Infrared Spectroscopy tests were carried out. The Hodgkin-Huxley and Gate Control Theory models were used to reveal the neural transduction and transmission of frictional pain and connect the surface friction and brain activation of pain. The skin friction and subjective evaluation tests showed that compared with healthy skin, the residual limb skin exhibited poorer elastic properties, lower friction coefficients and higher frictional pain thresholds. The results showed that the hemodynamic response of amputees was insignificant and brain regions of amputees related with γ oscillations was smaller compared with healthy individuals. The changes of skin mechanical properties and neural reorganization may responsible for the difference in frictional pain response between amputees and healthy individuals. The results also showed that the increasing intensity of frictional pain can induce the increase of T-cell potential, resulting in the increasing HbO concentration and γ oscillations. This study provides a better understanding of pain generation mechanisms from skin surface to brain response using mathematical models. It also reveals the differences in frictional pain between amputees and healthy individuals.

Mirror-assisted strategies are commonly used in the rehabilitation training of patients with hemiparesis in the upper limbs following a stroke. Traditional robotic mirror assistance focuses on achieving high-precision mirror trajectory tracking, often neglecting the issue of active movement in the affected side. This paper proposes a task performance-based adaptive impedance control, where the robot assists the affected side in an assist-as-needed manner, thereby encouraging the patient to perform active movements. To account for inter-individual variability, a method for assessing the affected side’s motor performance, based on the healthy side’s movement level, is introduced. Adaptive impedance control is then constructed based on the motor performance of the affected side, enabling the robot to provide adaptive assistance force. Eight healthy participants were recruited for experimental testing. Experimental results show that when the robot provides mirror-based assist-as-needed to the affected side, the robot’s stiffness coefficient and assistance force are positively correlated with the motor assessment coefficient of the affected side, thereby verifying the feasibility of the proposed strategy. This study offers a robotic-assisted rehabilitation strategy for stroke patients that balances active participation and individual adaptability, with the potential to enhance rehabilitation outcomes and enable precise rehabilitation interventions.

This review explores the potential of incorporating Machine Learning (ML) and Artificial Intelligence (AI) into the control systems of Lower Limb Rehabilitation Exoskeletons (LLREs), with a focus on its capacity to advance the field of rehabilitation robotics. It examines applications in optimizing personalized control supporting trajectory adaptation and key metrics in rehabilitation and discusses the challenges and limitations of traditional ML control methods supervised, unsupervised, and Reinforcement Learning (RL) in LLRE control. We analyze the specific aspects of traditional ML techniques in controlling the rehabilitation exoskeleton. Finally, the results of scientific solutions and developments in the implementation of gait parameters for personalized control that support the trajectory adaptation of the rehabilitation process are studied. Clinical applications and case studies directly describe the shortcomings and advantages of the field.

Accurate detection of driver fatigue is essential for improving road safety. This study investigates the effectiveness of using multimodal physiological signals for fatigue detection while incorporating uncertainty quantification to enhance the reliability of predictions. Physiological signals, including Electrocardiogram (ECG), Galvanic Skin Response (GSR), and Electroencephalogram (EEG), were transformed into image representations and analyzed using pretrained deep neural networks. The extracted features were classified through a feedforward neural network, and prediction reliability was assessed using uncertainty quantification techniques such as Monte Carlo Dropout (MCD), model ensembles, and combined approaches. Evaluation metrics included standard measures (sensitivity, specificity, precision, and accuracy) along with uncertainty-aware metrics such as uncertainty sensitivity and uncertainty precision. Across all evaluations, ECG-based models consistently demonstrated strong performance. The findings indicate that combining multimodal physiological signals, Transfer Learning (TL), and uncertainty quantification can significantly improve both the accuracy and trustworthiness of fatigue detection systems. This approach supports the development of more reliable driver assistance technologies aimed at preventing fatigue-related accidents.

Hydraulic artificial muscles offer higher stiffness and faster response. Unlike pneumatic artificial muscles, which rely on pressure control, hydraulic artificial muscles are more suitable for volume control. This study focuses on the modeling and nonlinear dynamic analysis of volume-controlled McKibben hydraulic artificial muscles. To address the limitations of existing models, a hierarchical semi-empirical modeling method is adopted to construct a static model with constant volume, which integrates a geometric model and a static force model. This model supports not only pressure control, but also volume control, and considers the end effects, the effective bulk modulus, and the nonlinear dependence of artificial muscles on Young’s modulus. Furthermore, a dynamic model encompassing four state parameters is established by combining the static model with fluid continuity equations, enabling the characterization of dynamic responses under volume control. Experimental methods were employed to determine the parameters in the formulas, significantly improving the accuracy of calculating the required injected volume for the given contraction ratio and load force. Nonlinear dynamics analysis reveals that HAM exhibit significant nonlinear damping, and the initial volume of connecting pipes significantly affects static pressure and working performance. Experimental validation shows that the static model achieves a maximum relative error of 2.05% in contraction ratio, while the dynamic model accurately captures the second-order oscillatory characteristics of force and pressure responses. This research provides a comprehensive modeling framework for volume-controlled HAM and deepens the understanding of their nonlinear dynamic behavior, facilitating engineering applications in robotics and rehabilitation devices.

Treating bone defects complicated by bacterial infections remains a significant clinical challenge. Drawing inspiration from the human body’s bone repair mechanisms, the use of biomimetic methods to design tissue engineering scaffolds is of great significance for bone repair. This study synthesized copper (Cu)-doped mesoporous silica nanoparticles (Cu@MSN) modified with hydroxyethyl methacrylate to obtain methacrylated Cu@MSN (Cu@MSNMA). Furtheremore, biomimetic nanocomposite hydrogels were prepared by adding Cu@MSNMA to a GelMA/gelatin solution. This hydrogel achieves multi-modal bone tissue biomimicry: (i) GelMA/gelatin mimics the matrix components in bone ECM, ensuring biocompatibility while promoting cellular behavior (such as adhesion, proliferation, and differentiation); (ii) GelMA/gelatin and the crosslinking sites introduced by Cu@MSNMA form a stable porous network structure, achieving structural and mechanical biomimicry to provide necessary support for bone defects; (iii) The elemental biomimicry of Si and Cu in Cu@MSNMA achieves efficient osteogenic induction. The effect of different proportions of Cu@MSNMA on the physical properties of the composite hydrogels was investigated to determine the optimal proportion. The results indicated that the mechanical properties of hydrogel were enhanced with the increasing Cu@MSNMA mass ratio. Notably, 5% NPs/GelMA/gelatin hydrogel exhibited excellent mechanical property compared to the GelMA/gelatin hydrogel. In vitro and vivo cellular experiments demonstrated a significant enhancement in antibacterial and osteogenic induction with Cu@MSNMA addition. In conclusion, the proposed nanocomposite hydrogel with biomimetic components and ion-regulating properties can serve as a multifunctional scaffold, offering antimicrobial properties for infected bone regeneration, and guide for future research in bone regeneration and three-dimensional printing.