Journal of Bionic Engineering最新文献

This research study aims to enhance the optimization performance of a newly emerged Aquila Optimization algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random numbers. This work employs 25 different chaotic maps under the framework of Aquila Optimizer. It considers the ten best chaotic variants for performance evaluation on multidimensional test functions composed of unimodal and multimodal problems, which have yet to be studied in past literature works. It was found that Ikeda chaotic map enhanced Aquila Optimization algorithm yields the best predictions and becomes the leading method in most of the cases. To test the effectivity of this chaotic variant on real-world optimization problems, it is employed on two constrained engineering design problems, and its effectiveness has been verified. Finally, phase equilibrium and semi-empirical parameter estimation problems have been solved by the proposed method, and respective solutions have been compared with those obtained from state-of-art optimizers. It is observed that CH01 can successfully cope with the restrictive nonlinearities and nonconvexities of parameter estimation and phase equilibrium problems, showing the capabilities of yielding minimum prediction error values of no more than 0.05 compared to the remaining algorithms utilized in the performance benchmarking process.

Cutting tools are known as the “productivity” of the manufacturing industry, which affects the production efficiency and quality of the workpiece, and has become the focus of research and attention in academia and industry. However, traditional cutting tools often suffer from adhesion or wear during the cutting process, which considerably reduces the cutting efficiency and service life of the tools, and makes it difficult to meet current production requirements. To solve the above problems, scholars have introduced bionics into the tool’s design, applying the microscopic structure of the biological surface to the tool surface to alleviate the tool’s failure. This paper mainly summarizes the research progress of bionic textured cutting tools. Firstly, categorize whether the bionic texture design is inspired by a single organism or multiple organisms. Secondly, it is discussed that the non-smooth surface of the biological surface has five characteristics: hydrophilic lubricity, wear resistance, drag reduction and hydrophobicity, anti-adhesion, and arrangement, and the non-smooth structure of these different characteristics are applied to the surface of the tool is designed with bionic texture. Furtherly, the cutting performance of bionic textured cutting tools is discussed. The anti-friction and wear-resisting mechanism of bionic textured cutting tools is analyzed. Finally, some pending problems and perspectives have been proposed to provide new inspirations for the design of bionic textured cutting tools.

Compliance motion and footstep adjustment are active balance control strategies from learning human subconscious behaviors. The force estimation without direct end-actuator force measurement and the optimal footsteps based on complex analytical calculation are still challenging tasks for elementary and kid-size position-controlled robots. In this paper, an online compliant controller with Gravity Projection Observer (GPO), which can express the external force condition of perturbations by the estimated Projection of Gravity (PoG) with estimation covariance, is proposed for the realization of disturbance absorption, with which the robustness of the humanoid contact with environments can be maintained. The fuzzy footstep planner based on capturability analysis is proposed, and the Model Predictive Control (MPC) is applied to generate the desired steps. The fuzzification rules are well-designed and give the corresponding control output responding to complex and changeable external disturbances. To validate the presented methods, a series of experiments on a real humanoid robot are conducted. The results verify the effectiveness of the proposed balance control framework.

In this study, an integrative bioinspired coating system for antifouling and corrosion resistance was investigated, in which self-healing nanocapsules (tung oil calcium alginate, TO@CA), doped polyaniline and nano-titanium dioxide nanocomposites (SPAn–TiO₂) and a biostructure metal surface were combined. The antifouling property of the bioinspired coating resulted from the synergistic antifouling effect of nano-TiO₂ and acid-doped polyaniline in SPAn–TiO₂. The protonated nitrogen with a positive charge in SPAn–TiO₂ and the intrinsic bactericidal property of nano-TiO₂ could damage negatively charged single-celled chlorella, endowing the composite coating with good antifouling performance (less algae attached on the surfaces after a 90-day antifouling test and a conductivity test). The composite bioinspired coating had excellent corrosion resistance, which was due to the good synergistic anticorrosion barrier effect of SPAn–TiO₂ with TO@CA nanocapsules and the repairing ability of microcracks of TO@CA nanocapsules during the corrosion process. The bioinspired coating with 2 wt% SPAn–TiO₂ and 2 wt% TO@CA nanocapsules exhibited a better adhesion, corrosion resistance and antifouling performance than the other coatings did.

This paper is based on a previously developed bio-inspired Flapping Wing Aerial Vehicle (FWAV), RoboFalcon, which can fly with a morphing-coupled flapping pattern. In this paper, a simple flapping stroke control system based on Hall effect sensors is designed and applied, which is capable of assigning different up- and down-stroke speeds for the RoboFalcon platform to achieve an adjustable downstroke ratio. The aerodynamic and power characteristics of the morphing-coupled flapping pattern and the conventional flapping pattern with varying downstroke ratios are measured through a wind tunnel experiment, and the corresponding aerodynamic models are developed and analyzed by the nonlinear least squares method. The relatively low power consumption of the slow-downstroke mode of this vehicle is verified through outdoor flight tests. The results of wind tunnel experiments and flight tests indicate that increased downstroke duration can improve aerodynamic and power performance for the RoboFalcon platform.

Aiming at the environment such as ravines and obstacles that may be encountered in the actual movement, this paper proposes a method for optimizing the bounding and jumping motion based on the ground touching force trajectory and the air motion trajectory of the quadruped robot. The method of optimizing the ground reaction force according to the speed of the demand and the height of the jump, and adjusting the stance and swing time according to the relationship of dynamics and momentum conservation. At the same time, under the constraints of dynamics and energy consumption of the robot system, considering the jumping distance and height, a method for optimizing the air trajectory of bounding and jumping is proposed. State switching and landing stability control are also added. Finally, the experimental results show that the quadruped robot has strong bounding and jumping ability, and has achieved stable bounding movement and forward jump movement of 0.8 m.

Over the past years, many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems. This study presents a new optimization method based on an unusual geological phenomenon in nature, named Geyser inspired Algorithm (GEA). The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process. The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005, CEC 2014, CEC 2017, and real-parameter benchmark functions. Moreover, GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness. In addition, to demonstrate the applicability and robustness of GEA, a comprehensive investigation is performed for a fair comparison with other standard optimization methods. The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms, including ABC, BBO, PSO, and RCGA. Note that the source code of the GEA is publicly available at https://www.optim-app.com/projects/gea.

Feature Subset Selection (FSS) is an NP-hard problem to remove redundant and irrelevant features particularly from medical data, and it can be effectively addressed by metaheuristic algorithms. However, existing binary versions of metaheuristic algorithms have issues with convergence and lack an effective binarization method, resulting in suboptimal solutions that hinder diagnosis and prediction accuracy. This paper aims to propose an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm (IBQANA) for FSS in medical data preprocessing to address the suboptimal solutions arising from binary versions of metaheuristic algorithms. The proposed IBQANA’s contributions include the Hybrid Binary Operator (HBO) and the Distance-based Binary Search Strategy (DBSS). HBO is designed to convert continuous values into binary solutions, even for values outside the [0, 1] range, ensuring accurate binary mapping. On the other hand, DBSS is a two-phase search strategy that enhances the performance of inferior search agents and accelerates convergence. By combining exploration and exploitation phases based on an adaptive probability function, DBSS effectively avoids local optima. The effectiveness of applying HBO is compared with five transfer function families and thresholding on 12 medical datasets, with feature numbers ranging from 8 to 10,509. IBQANA's effectiveness is evaluated regarding the accuracy, fitness, and selected features and compared with seven binary metaheuristic algorithms. Furthermore, IBQANA is utilized to detect COVID-19. The results reveal that the proposed IBQANA outperforms all comparative algorithms on COVID-19 and 11 other medical datasets. The proposed method presents a promising solution to the FSS problem in medical data preprocessing.

The rotor is the most important component of rotating machinery, and the vibration produced by its mass unbalance has a serious influence on the secure and steady operation of the machine, so an effective online suppression technology is urgently needed. A new hydraulic unbalanced bionic self-recovery system is introduced, imitating the way of manually repairing faulty equipment. To accomplish the effect of actuator mass redistribution, the technology employs pressurized air to drive the quantitative transfer of liquid in the reservoir cavity at opposite positions. It can complete the online adjustment of the equipment’s balancing state and suppress the unbalanced vibration of equipment in real time, which gives the equipment the ability to maintain an autonomous health state and improve equipment performance. The composition and working principle of the system are introduced in detail, and the key performance parameters, such as the minimum running speed and the balancing liquid transfer speed, are analyzed theoretically. The fluid–solid coupling model of the actuator was established, and the two-phase flow from inside the hydraulic unbalanced bionic self-recovery actuator was simulated under multiple working conditions and the performance parameters were quantitatively analyzed. A balancing simulation test bed was built, and its effectiveness was verified by performance parameter tests and unbalanced bionic self-recovery experiments. The experimental results show that the mass distribution adjustment of the balancing disk can be achieved using different viscosity balancing liquid, and the response of liquid viscosity 10 (cS{text{t}}) is faster than that of liquid viscosity 100 (cS{text{t}}) in the process of balancing liquid transfer, and the time is reduced by more than 75%; the system can reduce the simulated rotor amplitude from 18.3 μm to 10.6 μm online in real time, which provides technical support for the subsequent development of a new generation of bionic intelligent equipment.