Journal of Bionic Engineering最新文献

Compared to the traditional flapping-wing structure with single motion mode, a micro servoactuator driven Flapping-Wing Air Vehicle (FWAV) breaks free from the limitations imposed by the motion parameters of the crank-connecting rod mechanism. It allows for simultaneous control of wings’ position and velocity attitude through pulse width modulation, showcasing unrivaled controllability and promising extensive applications. However, this method of motion control also brings new challenges to the design of the wings’ motion parameters. This study seeks to investigate the relationship between the motion parameters of micro servoactuator driven FWAV and its aerodynamic characteristics, then explore a servo control method that can optimize its thrust-producing performance. To achieve this, this paper involves the establishment of Amplitude Loss Model (ALM), Flapping Wing Dynamic Model (FWDM), and Power Load Model (PLM), followed by motion capture experiments, dynamic monitoring experiments, and power monitoring experiments. Experimental results show that the proposed modeling method, which fully considers the amplitude loss effect and advanced twisting effect in flapping-wing motion, can accurately calculate thrust, power, and power load, with prediction errors of less than 10%, 5% and 13%, respectively. This high-precision model can effectively optimize motion parameters, allowing for better performance of flapping-wing motion.

Wall climbing robots have a wide range of applications in the field of transportation, petrochemicals, aerial construction, and other monitoring prospects; however, for complex defects on the wall, it is easy for the robot to fall off from attachment. This paper puts forward the bionic wheel-tracked rolling adsorption wall-climbing robots. The paper designs flexible material as sealing material for the negative pressure chamber of wall-climbing robots through the imitation of the biological mechanism of the insect adhesion pads. The material has the advantages of wear resistance, strong wall adaptability, large load, simple structure, etc., and it has a highly reliable and stable adsorption ability on unstructured and complex walls. The mathematical model of adsorption of the wall-crawling robot is constructed in different wall environments, and the kinematic analysis is carried out. The influence of the leakage on the adsorption capacity due to the deformation of the flexible sealing material, defects of the wall surface and the air ducts formed under different roughnesses is analyzed. Through the fabrication and experiment of the prototype, the correctness of the theoretical analysis is verified. The measured load capacity of the robot is 2.47 times its own weight, and it has great obstacle-crossing ability.

This study investigated the validity and sensitivity of a custom-made shoelace tensile testing system. The aim was to analyze the distribution pattern of shoelace tension in different positions and under different tightness levels during running. Mechanical tests were conducted using 16 weights, and various statistical analyses, including linear regression, Bland-Altman plots, coefficient of variation, and intraclass correlation coefficient, were performed to assess the system’s validity. Fifteen male amateur runners participated in the study, and three conditions (loose, comfortable, and tight) were measured during an upright stance. The system utilized VICON motion systems, a Kistler force plate, and a Photoelectric gate speed measurement system. Results showed a linear relationship between voltage and load at the three sensors (R2 ≥ 0.9997). Bland-Altman plots demonstrated 95% prediction intervals within ± 1.96SD from zero for all sensors. The average coefficient of variation for each sensor was less than 0.38%. Intraclass correlation coefficient values were larger than 0.999 (p<0.0001) for each sensor. The peak tension of the front shoelace was greater than that of the front and middle when the shoelace was loose and tight. The rear shoelace had the highest tension force. The study also found that shoelace tension varied throughout the gait cycle during running. Overall, this research provides a novel and validated method for measuring shoelace tensile stress, which has implications for developing automatic shoelace fastening systems.

This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer (SMOGWO) as a novel methodology for addressing the complex problem of empty-heavy train allocation, with a focus on line utilization balance. By integrating surrogate models to approximate the objective functions, SMOGWO significantly improves the efficiency and accuracy of the optimization process. The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite, where SMOGWO achieves a superiority rate of 76.67% compared to other leading multi-objective algorithms. Furthermore, the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation, which validates its ability to balance line capacity, minimize transportation costs, and optimize the technical combination of heavy trains. The research highlights SMOGWO’s potential as a robust solution for optimization challenges in railway transportation, offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.

Convolutional Neural Networks (CNNs) have shown remarkable capabilities in extracting local features from images, yet they often overlook the underlying relationships between pixels. To address this limitation, previous approaches have attempted to combine CNNs with Graph Convolutional Networks (GCNs) to capture global features. However, these approaches typically neglect the topological structure information of the graph during the global feature extraction stage. This paper proposes a novel end-to-end hybrid architecture called the Multi-Graph Pooling Network (MGPN), which is designed explicitly for chest X-ray image classification. Our approach sequentially combines CNNs and GCNs, enabling the learning of both local and global features from individual images. Recognizing that different nodes contribute differently to the final graph representation, we introduce an NI-GTP module to enhance the extraction of ultimate global features. Additionally, we introduce a G-LFF module to fuse the local and global features effectively.

Bipolar plate is one of the key components of Proton Exchange Membrane Fuel Cell (PEMFC) and a reasonable flow field design for bipolar plate will improve cell performance. Herein, we have reviewed conventional and bionic flow field designs in recent literature with a focus on bionic flow fields. In particular, the bionic flow fields are summarized into two types: plant-inspired and animal-inspired. The conventional methodology for flow field design takes more time to find the optimum since it is based on experience and trial-and-error methods. In recent years, machine learning has been used to optimize flow field structures of bipolar plates owing to the advantages of excellent prediction and optimization capability. Artificial Intelligence (AI)-assisted flow field design has been summarized into two categories in this review: single-objective optimization and multi-objective optimization. Furthermore, a Threats-Opportunities-Weaknesses-Strengths (TOWS) analysis has been conducted for AI-assisted flow field design. It has been envisioned that AI can afford a powerful tool to solve the complex problem of bionic flow field design and significantly enhance the performance of PEMFC with bionic flow fields.

Flapping-Wing Air Vehicles (FWAVs) have been developed to pursue the efficient, agile, and quiet flight of flying animals. However, unlike lightweight FWAVs capable of vertical takeoff, relatively heavy FWAVs face challenges in self-takeoff, which refers to taking off without both external device and energy input. In this study, a cliff-drop method is implemented for an independent takeoff of a heavy FWAV, relying solely on gravity. In the takeoff process using the cliff-drop method, the FWAV moves on the ground to a cliff edge using a wheel-driving motor and then descends from the cliff to achieve the necessary speed for flight. To demonstrate the cliff-drop method, the KAIST Robotic Hawk (KRoHawk) with a mass of 740 g and a wingspan of 120 cm is developed. The takeoff tests demonstrate that the KRoHawk, significantly heavier than the vertical-takeoff capable FWAVs, can successfully take off using the gravity-assisted takeoff method. The scalability of cliff-drop method is analyzed through simulations. When drop constraints are absent, the wheel-driving motor mass fraction for cliff-drop method remains negligible even as the vehicle’s weight increases. When drop constraints are set to 4 m, FWAVs heavier than KRoHawk, weighing up to 4.4 kg, can perform the cliff-drop takeoffs with a wheel-driving motor mass fraction of less than 8%.

The Unit Commitment Problem (UCP) corresponds to the planning of power generation schedules. The objective of the fuel-based unit commitment problem is to determine the optimal schedule of power generators needed to meet the power demand, which also minimizes the total operating cost while adhering to different constraints such as power generation limits, unit startup, and shutdown times. In this paper, four different binary variants of the Bald Eagle Search (BES) algorithm, were introduced, which used two variants using S-shape, U-shape, and V-shape transfer functions. In addition, the best-performing variant (using an S-shape transfer function) was selected and improved further by incorporating two binary operators: swap-window and window-mutation. This variation is labeled Improved Binary Bald Eagle Search (IBBESS2). All five variants of the proposed algorithm were successfully adopted to solve the fuel-based unit commitment problem using seven test cases of 4-, 10-, 20-, 40-, 60-, 80-, and 100-unit. For comparative evaluation, 34 comparative methods from existing literature were compared, in which IBBESS2 achieved competitive scores against other optimization techniques. In other words, the proposed IBBESS2 performs better than all other competitors by achieving the best average scores in 20-, 40-, 60-, 80-, and 100-unit problems. Furthermore, IBBESS2 demonstrated quicker convergence to an optimal solution than other algorithms, especially in large-scale unit commitment problems. The Friedman statistical test further validates the results, where the proposed IBBESS2 is ranked the best. In conclusion, the proposed IBBESS2 can be considered a powerful method for solving large-scale UCP and other related problems.

Felids, during intense activities such as jumping and sprinting, adjust their posture by twisting and stretching their body to disperse limb impact and minimize injury. This self-stabilization mechanism has garnered significant attention for inspiring biometric robot design. This study investigates the flexibility and cushioning characteristics of a cat’s spine, focusing on its biomechanical properties. A high-fidelity 3D model was used to test the range of motion (ROM) under six conditions, simulate dorsiflexion to analyze stress distribution. The torsional and compressive stiffness were tested by using five cat spinal specimens. the flexibility principles of the flexible cat’s spine were explained via morphological insights. Results indicate that the cat spine has the least rotational stiffness in axial rotation, followed by extension and lateral bending, with a compressive stiffness of 53.62 ± 4.68 N/mm. Stress during dorsiflexion is evenly distributed across vertebrae. The vertebrae heights account for 90.34% of total spinal length with a mean height-to-width ratio of 1.04. Cats’ spines, with more articulations and elongated vertebrae, allow for significant twisting and bending, aiding in rapid body posture adjustments and impact mitigation. These biomechanical traits could inspire the design of robots for confined rescue operations.

Delaying the sinusoidal plunging in the middle of the up- and down stroke is studied. This form of kinematics can appear if the flapping mechanism is malfunctioning, or while large birds fly in some cases. Aim of this study is to understand the effect of pausing the airfoil during plunging in the equilibrium position. The paused plunging is modelled mathematically by means of a sinusoidal waveform raised to the third power. Evaluation of this waveform is done on two stages; the wake pattern analysis and the aerodynamic and propulsive analysis. Studying this waveform reveals a robust way to generate two triads wake pattern instead of the regular reverse von Karman vortex wake pattern. The thrust generation mechanism is presented. The performance evaluation is done based on the thrust, lift, and power coefficients and the propulsive efficiency at different point in the nondimensional amplitude– reduced frequency space. Regression modeling methods are utilized to stand on the performance of the paused waveform with respect to the regular sinusoidal waveform. These findings underscore the potential of the proposed waveform as a promising alternative for enhancing the aerodynamic performance and propulsive efficiency in the design of Micro Air Vehicles (MAVs), a rapidly evolving field.