Journal of Bionic Engineering最新文献

The shortage of transplantable skin is the leading cause of death in patients with extensive skin defect. Addressing this challenge urgently requires the development of skin substitutes capable of wound repair and facilitating skin regeneration. In this study, a biomimetic bilayer skin tissue model consisting of collagen, gelatin/sodium alginate, fibroblasts, human umbilical vein endothelial cells, keratinocytes, melanocytes, and verteporfin was devised. Then, the skin model was fabricated using precise extrusion/inkjet bioprinters, and it reconstruction capabilities were evaluated through skin defect repair experiments. The printed skin tissue reduced the inflammatory response of the wound by 38% and inhibited the expression of TGF-β and YAP, and promoted the transformation of macrophages from M1 phenotype to M2 phenotype, thus promoting the reasonable reconstruction of fibronectin and collagen on the wound, finally promoting the wound healing, and reducing the wound contraction and scar formation. In addition, the proliferation and differentiation of human umbilical vein endothelial cells, keratinocytes, and melanocytes in printed skin increased the number of regenerated blood vessels by 123%, while promoting the reconstruction of multilayer epidermal structure and skin color. The outcomes of this investigation present a promising skin model and therapeutic strategy for skin injury, offering a potential avenue for the reconstruction of skin structure and function.

Nature-inspired optimization algorithms refer to techniques that simulate the behavior and ecosystem of living organisms or natural phenomena. One such technique is the “Photosynthesis Spectrum Algorithm,” which was developed by mimicking the process by which photons behave as a population in plants. This optimization technique has three stages that mimic the structure of leaves and the fluorescence phenomenon. Each stage updates the fitness of the solution by using a mathematical equation to direct the photon to the reaction center. Three stages of testing have been conducted to test the efficacy of this approach. In the first stage, functions from the CEC 2019 and CEC 2021 competitions are used to evaluate the performance and convergence of the proposed method. The statistical results from non-parametric Friedman and Kendall’s W tests show that the proposed method is superior to other methods in terms of obtaining the best average of solutions and achieving stability in finding solutions. In other sections, the experiment is designed for data clustering. The proposed method is compared with recent data clustering and classification metaheuristic algorithms, indicating that this method can achieve significant performance for clustering in less than 10 s of CPU time and with an accuracy of over 90%.

To solve the shortcomings of Particle Swarm Optimization (PSO) algorithm, local optimization and slow convergence, an Opposition-based Learning Adaptive Chaotic PSO (LCPSO) algorithm was presented. The chaotic elite opposition-based learning process was applied to initialize the entire population, which enhanced the quality of the initial individuals and the population diversity, made the initial individuals distribute in the better quality areas, and accelerated the search efficiency of the algorithm. The inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm, and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local optimum. The LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics, and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability, search accuracy and convergence speed. In addition, the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.

Cable-driven soft robots exhibit complex deformations, making state estimation challenging. Hence, this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting coefficients. These coefficients combine measurements from proprioceptive sensors, such as resistive flex sensors, to determine the bending angle. Additionally, the fusion strategy adopted provides robust state estimates, overcoming mismatches between the flex sensors and soft robot dimensions. Furthermore, a nonlinear differentiator is introduced to filter the differentiated sensor signals to address noise and irrational values generated by the Analog-to-Digital Converter. A rational polynomial equation is also introduced to compensate for temperature drift exhibited by the resistive flex sensors, which affect the accuracy of state estimation and control. The processed multi-sensor data is then utilized in an improved PD controller for closed-loop control of the soft robot. The controller incorporates the nonlinear differentiator and drift compensation, enhancing tracking performance. Experimental results validate the effectiveness of the integrated approach, demonstrating improved tracking accuracy and robustness compared to traditional PD controllers.

Real-time gait switching of quadruped robot with speed change is a difficult problem in the field of robot research. It is a novel solution to apply reinforcement learning method to the quadruped robot problem. In this paper, a quadruped robot simulation platform is built based on Robot Operating System (ROS). openai-gym is used as the RL framework, and Proximal Policy Optimization (PPO) algorithm is used for quadruped robot gait switching. The training task is to train different gait parameters according to different speed input, including gait type, gait cycle, gait offset, and gait interval. Then, the trained gait parameters are used as the input of the Model Predictive Control (MPC) controller, and the joint forces/torques are calculated by the MPC controller.The calculated joint forces are transmitted to the joint motor of the quadruped robot to control the joint rotation, and the gait switching of the quadruped robot under different speeds is realized. Thus, it can more realistically imitate the gait transformation of animals, walking at very low speed, trotting at medium speed and galloping at high speed. In this paper, a variety of factors affecting the gait training of quadruped robot are integrated, and many aspects of reward constraints are used, including velocity reward, time reward,energy reward and balance reward. Different weights are given to each reward, and the instant reward at each step of system training is obtained by multiplying each reward with its own weight, which ensures the reliability of training results. At the same time, multiple groups of comparative analysis simulation experiments are carried out. The results show that the priority of balance reward, velocity reward, energy reward and time reward decreases successively and the weight of each reward does not exceed 0.5.When the policy network and the value network are designed, a three-layer neural network is used, the number of neurons in each layer is 64 and the discount factor is 0.99, the training effect is better.

This paper presents a novel method for optimizing the contact force of a hexapod robot to enhance its dynamic motion stability when one of its legs fails. The proposed approach aims to improve the Force Angle Stability Margin (FASM) and adapt the foot trajectory through contact force optimization to achieve safe and stable motion on various terrains. The foot force optimization approach is designed to optimize the FASM, a factor rarely considered in existing contact force optimization methods. By formulating the problem of enhancing the motion stability of the hexapod robot as a Quadratic Programming (QP) optimization problem, this approach can effectively address this issue. Simulations of a hexapod robot using a fault-tolerant gait, along with real field experiments, were conducted to validate the effectiveness and feasibility of the contact force optimization approach. The results demonstrate that this approach can be used to design a motion controller for a hexapod robot with a significantly improved motion stability. In summary, the proposed contact force optimization method offers a promising solution for enhancing the motion stability of hexapod robots with single leg failures and has the potential to significantly advance the development of fault-tolerant hexapod robots for various applications.

Flapping-wing rotor (FWR) is an innovative bio-inspired micro aerial vehicle capable of vertical take-off and landing. This unique design combines active flapping motion and passive wing rotation around a vertical central shaft to enhance aerodynamic performance. The research on FWR, though relatively new, has contributed to 6% of core journal publications in the micro aerial vehicle field over the past two decades. This paper presents the first comprehensive review of FWR, analysing the current state of the art, key advances, challenges, and future research directions. The review highlights FWR’s distinctive kinematics and aerodynamic superiority compared to traditional flapping wings, fixed wings, and rotary wings, discussing recent breakthroughs in efficient, passive wing pitching and asymmetric stroke amplitude for lift enhancement. Recent experiments and remote-controlled take-off and hovering tests of single and dual-motor FWR models have showcased their effectiveness. The review compares FWR flight performance with well-developed insect-like flapping-wing micro aerial vehicles as the technology readiness level progresses from laboratory to outdoor flight testing, advancing from the initial flight of a 2.6 g prototype to the current free flight of a 60-gram model. The review also presents ongoing research in bionic flexible wing structures, flight stability and control, and transitioning between hovering and cruise flight modes for an FWR, setting the stage for potential applications.

In recent years, developed Intrusion Detection Systems (IDSs) perform a vital function in improving security and anomaly detection. The effectiveness of deep learning-based methods has been proven in extracting better features and more accurate classification than other methods. In this paper, a feature extraction with convolutional neural network on Internet of Things (IoT) called FECNNIoT is designed and implemented to better detect anomalies on the IoT. Also, a binary multi-objective enhance of the Gorilla troops optimizer called BMEGTO is developed for effective feature selection. Finally, the combination of FECNNIoT and BMEGTO and KNN algorithm-based classification technique has led to the presentation of a hybrid method called CNN-BMEGTO-KNN. In the next step, the proposed model is implemented on two benchmark data sets, NSL-KDD and TON-IoT and tested regarding the accuracy, precision, recall, and F1-score criteria. The proposed CNN-BMEGTO-KNN model has reached 99.99% and 99.86% accuracy on TON-IoT and NSL-KDD datasets, respectively. In addition, the proposed BMEGTO method can identify about 27% and 25% of the effective features of the NSL-KDD and TON-IoT datasets, respectively.

It has been demonstrated that the flexibility of the structure can enhance the kinematic performance of the underwater bionic robotic fish. Furthermore, the thrust of the underwater robotic fish can be further enhanced by changing the stiffness of the tail when the motion frequency of the propulsion system increases. This paper proposes a novel actuator, the pneumatic variable stiffness imitation dolphin tail actuator (PVSA), which combines soft robotics with the structural characteristics and movement mode of a biological dolphin. The PVSA comprises a pneumatic bi-directional bending soft actuator and a pull-wire-driven variable stiffness mechanism. The soft actuator is capable of mimicking the dorsoventral movement of dolphins by changing the pressure difference between the cavities, thereby achieving bending deformation. The variable stiffness mechanism is based on the stiffness mechanism of particle interference and the structural characteristics of vertebrate endoskeleton, with the objective of achieving variable stiffness. The parameters of the PVSA are optimised using numerical simulations and experimental studies, and then designed underwater experiments are conducted to investigate the effects of amplitude, stiffness and frequency on the propulsive performance of the PVSA. The results demonstrate that the PVSA is capable of enhancing thrust by adjusting its own stiffness and movement frequency. The development of the PVSA provides a reference for the research of related underwater bionic propulsion technology.

Combined Heat and Power Economic Dispatch (CHPED) is an important problem in the energy field, and it is beneficial for improving the utilization efficiency of power and heat energies. This paper proposes a Modified Genetic Algorithm (MGA) to determine the power and heat outputs of three kinds of units for CHPED. First, MGA replaces the simulated binary crossover by a new one based on the uniform and guassian distributions, and its convergence can be enhanced. Second, MGA modifies the mutation operator by introducing a disturbance coefficient based on guassian distribution, which can decrease the risk of being trapped into local optima. Eight instances with or without prohibited operating zones are used to investigate the efficiencies of MGA and other four genetic algorithms for CHPED. In comparison with the other algorithms, MGA has reduced generation costs by at least 562.73$, 1068.7$, 522.68$ and 1016.24$, respectively, for instances 3, 4, 7 and 8, and it has reduced generation costs by at most 848.22$, 3642.85$, 897.63$ and 3812.65$, respectively, for instances 3, 4, 7 and 8. Therefore, MGA has desirable convergence and stability for CHPED in comparison with the other four genetic algorithms.