Journal of Bionic Engineering最新文献

The goal of this paper is to develop a unified online motion generation scheme for quadruped lateral-sequence walk and trot gaits based on a linear model predictive control formulation. Specifically, the dynamics of the linear pendulum model is formulated over a predictive horizon by dimensional analysis. Through gait pattern conversion, the lateral-sequence walk and trot gaits of the quadruped can be regarded as unified biped gaits, allowing the dynamics of the linear inverted pendulum model to serve quadruped motion generation. In addition, a simple linearization of the center of pressure constraints for these quadruped gaits is developed for linear model predictive control problem. Furthermore, the motion generation problem can be solved online by quadratic programming with foothold adaptation. It is demonstrated that the proposed unified scheme can generate stable locomotion online for quadruped lateral-sequence walk and trot gaits, both in simulation and on hardware. The results show significant performance improvements compared to previous work. Moreover, the results also suggest the linearly simplified scheme has the ability to robustness against unexpected disturbances.

Almost all living organisms exhibit autonomic oscillatory activities, which are primarily generated by the rhythmic activities of their neural systems. Several nonlinear oscillator models have been proposed to elucidate these neural behaviors and subsequently applied to the domain of robot control. However, the oscillation patterns generated by these models are often unpredictable and need to be obtained through parameter search. This study introduces a mathematical model that can be used to analyze multiple neurons connected through fast inhibitory synapses. The characteristic of this oscillator is that its stationary point is stable, but the location of the stationary point changes with the system state. Only through reasonable topology and threshold parameter selection can the oscillation be sustained. This study analyzed the conditions for stable oscillation in two-neuron networks and three-neuron networks, and obtained the basic rules of the phase relationship of the oscillator network established by this model. In addition, this study also introduces synchronization mechanisms into the model to enable it to be synchronized with the sensing pulse. Finally, this study used these theories to establish a robot single leg joint angle generation system. The experimental results showed that the simulated robot could achieve synchronization with human motion, and had better control effects compared to traditional oscillators.

In the natural world, leaf-cutting ants cause vibrations through their mutual scraping of file-scraper organs. In this study, we designed a Biomimetic Ultrasonic Exciter (BUE) that imitates leaf-cutting ants. The operating characteristics of the BUE were studied through experimental testing and finite element simulations. The results showed that the BUE could generate stable ultrasonic vibrations, and that the excitation frequency only needed to be half the Output Frequency (OF). This frequency-doubling phenomenon was conducive to achieving BUE miniaturization. To further explore the phenomenon of frequency-doubling vibration output, this study designed scrapers of five different sizes, conducted excitation and first-order natural frequency measurement tests, and the corresponding finite element simulations. It was found that each scraper could operate in frequency-doubling mode, but the operating frequency and natural mode frequencies did not correspond with one another. To further explicate experimental and simulation results, a two-degrees-of-freedom vibration model was developed. It was evident that the contact relationship between the dentate disc and scraper introduced strong nonlinear factors into the system, accounting for the frequency-doubling phenomenon and the difference between the BUE’s operating and mode frequencies. The BUE could be expected to facilitate the production of high-power micro-ultrasonic generators and has potential application value in the fields of mechanical processing, industrial production, and medical health.

Most flapping-wing aircraft wings use a single degree of freedom to generate lift and thrust by flapping up and down, while relying on the tail control surfaces to manage attitude. However, these aircraft have certain limitations, such as poor accuracy in attitude control and inadequate roll control capabilities. This paper presents a design for an active torsional mechanism at the wing's trailing edge, which enables differential variations in the pitch angle of the left and right wings during flapping. This simple mechanical form significantly enhances the aircraft's roll control capacity. The experimental verification of this mechanism was conducted in a wind tunnel using the RoboEagle flapping-wing aerial vehicle that we developed. The study investigated the effects of the control strategy on lift, thrust, and roll moment during flapping flight. Additionally, the impact of roll control on roll moment was examined under various wind speeds, flapping frequencies, angles of attack, and wing flexibility. Furthermore, several rolling maneuver flight tests were performed to evaluate the agility of RoboEagle, utilizing both the elevon control strategy and the new roll control strategy. The results demonstrated that the new roll control strategy effectively enhances the roll control capability, thereby improving the attitude control capabilities of the flapping-wing aircraft in complex wind field environments. This conclusion is supported by a comparison of the control time, maximum roll angle, average roll angular velocity, and other relevant parameters between the two control strategies under identical roll control input.

Medical image segmentation is a powerful and evolving technology in medical diagnosis. In fact, it has been identified as a very effective tool to support and accompany doctors in their fight against the spread of the coronavirus (COVID-19). Various techniques have been utilized for COVID-19 image segmentation, including Multilevel Thresholding (MLT)-based meta-heuristics, which are considered crucial in addressing this issue. However, despite their importance, meta-heuristics have significant limitations. Specifically, the imbalance between exploration and exploitation, as well as premature convergence, can cause the optimization process to become stuck in local optima, resulting in unsatisfactory segmentation results. In this paper, an enhanced War Strategy Chimp Optimization Algorithm (WSChOA) is proposed to address MLT problems. Two strategies are incorporated into the traditional Chimp Optimization Algorithm. Golden update mechanism that provides diversity in the population. Additionally, the attack and defense strategies are incorporated to improve the search space leading to avoiding local optima. The experimental results were conducted by comparing WSChoA with recent and well-known algorithms using various evaluation metrics such as Feature Similarity Index (FSIM), Structural Similarity Index (SSIM), Peak signal-to-Noise Ratio (PSNR), Standard deviation (STD), Freidman Test (FT), and Wilcoxon Sign Rank Test (WSRT). The results obtained by WSChoA surpassed those of other optimization techniques in terms of robustness and accuracy, indicating that it is a powerful tool for image segmentation.

This paper aims to address the problem of multi-UAV cooperative search for multiple targets in a mountainous environment, considering the constraints of UAV dynamics and prior environmental information. Firstly, using the target probability distribution map, two strategies of information fusion and information diffusion are employed to solve the problem of environmental information inconsistency caused by different UAVs searching different areas, thereby improving the coordination of UAV groups. Secondly, the task region is decomposed into several high-value sub-regions by using data clustering method. Based on this, a hierarchical search strategy is proposed, which allows precise or rough search in different probability areas by adjusting the altitude of the aircraft, thereby improving the search efficiency. Third, the Elite Dung Beetle Optimization Algorithm (EDBOA) is proposed based on bionics by accurately simulating the social behavior of dung beetles to plan paths that satisfy the UAV dynamics constraints and adapt to the mountainous terrain, where the mountain is considered as an obstacle to be avoided. Finally, the objective function for path optimization is formulated by considering factors such as coverage within the task region, smoothness of the search path, and path length. The effectiveness and superiority of the proposed schemes are verified by the simulation.

Unilateral motor impairment can disrupt the coordination between the joints, impeding the patient’s normal gait. To assist such patients to walk normally and naturally, an adaptive control algorithm based on inter-joint coordination was proposed in this work for lower-limb exoskeletons. The control strategy can generate the reference trajectory of the affected leg in real time based on a motion coordination model between the joints, and adopt an adaptive controller with virtual windows to track the reference trajectory. Long Short-Term Memory (LSTM) network was also adopted to establish the coordination model between the joints of both lower limbs, which was optimized by preprocessing angle information and adding gait phase information. In the adaptive controller, the virtual windows were symmetrically distributed around the reference trajectory, and its width was adjusted according to the gait phase of the auxiliary leg. In addition, the impedance parameters of the controller were updated online to match the motion capacity of the affected leg based on the spatiotemporal symmetry factors between the bilateral gaits. The LSTM coordination model demonstrated good accuracy and generality in the gait database of seven individuals, with an average root mean square error of 3.5(^circ) and 4.1(^circ) for the hip and knee joint angle estimation, respectively. To further evaluate the control algorithm, four healthy subjects walked wearing the exoskeleton while additional weights were added around the ankle joint to simulate an asymmetric gait. From the experimental results, it was shown that the algorithm improved the gait symmetry of the subjects to a normal level while exhibiting great adaptability to different subjects.

Modular continuum robots possess significant versatility across various scenarios; however, conventional assembling methods typically rely on linear connection between modules. This limitation can impede the robotic interaction capabilities, especially in specific engineering applications. Herein, inspired by the assembling pattern between the femur and tibia in a human knee, we proposed a multidirectional assembling strategy. This strategy encompasses linear, oblique, and orthogonal connections, allowing a two-module continuum robot to undergo in-situ reconfiguration into three distinct initial configurations. To anticipate the final configuration resulting from diverse assembling patterns, we employed the positional formulation finite element framework to establish a mechanical model, and the theoretical results reveal that our customizable strategy can offer an effective route for robotic interactions. We showcased diverse assembling patterns for coping with interaction requirements. The experimental results indicate that our modular continuum robot not only reconfigures its initial profile in situ but also enables on-demand regulation of the final configuration. These capabilities provide a foundation for the future development of modular continuum robots, enabling them to be adaptable to diverse environments, particularly in unstructured surroundings.

Feature selection (FS) plays a crucial role in pre-processing machine learning datasets, as it eliminates redundant features to improve classification accuracy and reduce computational costs. This paper presents an enhanced approach to FS for software fault prediction, specifically by enhancing the binary dwarf mongoose optimization (BDMO) algorithm with a crossover mechanism and a modified positioning updating formula. The proposed approach, termed iBDMOcr, aims to fortify exploration capability, promote population diversity, and lastly improve the wrapper-based FS process for software fault prediction tasks. iBDMOcr gained superb performance compared to other well-esteemed optimization methods across 17 benchmark datasets. It ranked first in 11 out of 17 datasets in terms of average classification accuracy. Moreover, iBDMOcr outperformed other methods in terms of average fitness values and number of selected features across all datasets. The findings demonstrate the effectiveness of iBDMOcr in addressing FS problems in software fault prediction, leading to more accurate and efficient models.

Corneal diseases, the second leading cause of global vision loss affecting over 10.5 million people, underscores the unmet demand for corneal tissue replacements. Given the scarcity of fresh donor corneas and the associated risks of immune rejection, corneal tissue engineering becomes imperative. Developing nanofibrous scaffolds that mimic the natural corneal structure is crucial for creating transparent and mechanically robust corneal equivalents in tissue engineering. Herein, Aloe Vera Extract (AVE)/Polycaprolactone (PCL) nanofibrous scaffolds were primed using electrospinning. The electrospun AVE/PCL fibers exhibit a smooth, bead-free morphology with a mean diameter of approximately 340 ± 95 nm and appropriate light transparency. Mechanical measurements reveal Young’s modulus and ultimate tensile strength values of around 3.34 MPa and 4.58 MPa, respectively, within the range of stromal tissue. In addition, cell viability of AVE/PCL fibers was measured against Human Stromal Keratocyte Cells (HSKCs), and improved cell viability was observed. The cell-fiber interactions were investigated using scanning electron microscopy. In conclusion, the incorporation of Aloe Vera Extract enhances the mechanical, optical, hydrophilic, and biological properties of PCL fibers, positioning PCL/AVE fiber scaffolds as promising candidates for corneal stromal regeneration.