Journal of Bionic Engineering最新文献

In this study, we present the design and development evaluation of BalanSENS toward the realization of the Integrated Balance Rehabilitation (I-BaR) framework. BalanSENS is designed to encourage active participation by integrating multi-sensory information with the co-improvement of sensory and motor functions. Moreover, it can offer individual rehabilitation design with the integration of three phases. The first phase provides foot-ankle muscle activation and movement sensation development. In the second phase, sensory weighting skills and upper extremities independence can be improved by using multi-sensory input. In the last/stepping phase, walking parameters are aimed to be improved with modulated distance. The parallel manipulator is designed through simulations to determine actuation properties and analyze the load-bearing capacity and feasibility of the materials. Drawing from simulation outcomes, an operational 3 DoF platform is constructed to demonstrate their design suitability for the I-BaR framework. Furthermore, design evaluations demonstrated promising results in quantifying force and real-time data monitoring during the passive ankle preparation phase.

The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis. Computer-aided medical systems are designed to provide accurate information and reduce human errors, in which accurate and effective segmentation of medical images plays a pivotal role in improving clinical outcomes. Multilevel Threshold Image Segmentation (MTIS) is widely favored due to its stability and straightforward implementation. Especially when dealing with sophisticated anatomical structures, high-level thresholding is a crucial technique in identifying fine details. To enhance the accuracy of complex breast cancer image segmentation, this paper proposes an improved version of RIME optimizer EECRIME, denoted as the double Enhanced solution quality Crisscross RIME algorithm. The original RIME initially conducts an efficient optimization to target promising solutions. The double-enhanced solution quality (EESQ) mechanism is proposed for thorough exploitation without falling into local optimum. In contrast, the crisscross operations perform a further local exploration of the generated feasible solutions. The performance of EECRIME is verified with basic and advanced algorithms on IEEE CEC2017 benchmark functions. Furthermore, an EECRIME-based MTIS method in combination with Kapur’s entropy is applied to segment breast Infiltrating Ductal Carcinoma (IDC) histology images. The results demonstrate that the developed model significantly surpasses its competitors, establishing it as a practical approach for complex medical image processing.

Aiming to address the issues of poor optimization-seeking ability and easily falling into local optimization of the Snow Ablation Optimizer (SAO), a Physically Hybrid strategy-based Improved Snow Ablation Optimizer (PHISAO) is proposed. In this paper, a snow blowing strategy was introduced during the initialization phase of the population to improve population diversity. Secondly, the dual-population iterative strategy of SAO has been replaced by a multi-population iterative strategy, which is supplemented with a position update formula for the water evaporation phase. Additionally, Cauchy mutation perturbation has been introduced in the snow melting phase. This set of improvements better balances the exploration and exploitation phases of the algorithm, enhancing its ability to pursue excellence. Finally, a fluid activation strategy is added to activate the potential of the algorithm when its update iterations enter stagnation, helping the algorithm to escape from the local optimum. Comparison experiments between PHISAO and six metaheuristics were conducted on the CEC (Congress on Evolutionary Computation)-2017 and CEC-2022 benchmark suites. The experimental results demonstrate that the PHISAO algorithm exhibits excellent performance and robustness. In addition, the PHISAO is applied into the unmanned aerial vehicle trajectory planning problem together with particle swarm optimization, beluga whale optimization, sand cat swarm optimization, and SAO. The simulation results show that the proposed PHISAO can plan the optimal trajectory in all two different maps. The proposed PHISAO objective function values were reduced by an average of 29.49% (map 1), and 18.34% (map 2) compared to SAO.

Traditional robotic grippers encounter significant challenges when handling small objects in confined spaces, underscoring the need for innovative instruments with enhanced space efficiency and adaptability. Erodium cicutarium awns have evolved hygroresponsive helical deformation, efficiently driving seeds into soil crevices with limited space utilization. Drawing inspiration from this natural mechanism, we developed a biomimetic thin-walled actuator with water-responsive helical capabilities. It features a composite material structure comprising common engineering materials with low toxicity. Leveraging fused deposition modeling 3D printing technology and the composite impregnation process, the actuator’s manufacturing process is streamlined and cost-effective, suitable for real-world applications. Then, a mathematical model is built to delineate the relationship between the biomimetic actuator’s key structural parameters and deformation characteristics. The experimental results emphasize the actuator’s compact dimension (0.26 mm thickness) and its capability to form a helical tube under 5 mm diameter within 60 s, demonstrating outstanding space efficiency. Moreover, helical characteristics and stiffness of the biomimetic actuators are configurable through precise modifications to the composite material structure. Consequently, it is capable of effectively grasping an object smaller than 3 mm. The innovative mechanism and design principles hold promise for advancing robotic technology, particularly in fields requiring high space efficiency and adaptability, such as fine tubing decongestion, underwater sampling, and medical endoscopic surgery.

Ionic Polymer Metal Composites (IPMCs) are considered important electroactive polymers that have recently attracted the attention of the scientific community owing to their simple structure, adaptable form, high degree of flexibility, and biocompatibility during their utilization as sensing elements. Along these lines, in this work, the recent developments in performance optimization, model construction, and applications of IPMC sensors were reported. Different methods were introduced to enhance the sensitivity, preparation efficiency, and stability of the IPMC sensors including optimising the electrode and substrate membrane preparation, as well as implementing structural and shape modifications, etc. The IPMC sensing model, which serves as the theoretical foundation for the IPMC sensor, was summarized herein to offer directions for future application research activities. The applications of these sensors in a wide range of areas were also reviewed, such as wearable electronic devices, flow sensors, humidity sensors, energy harvesting devices, etc.

Feature Selection (FS) is an optimization problem that aims to downscale and improve the quality of a dataset by retaining relevant features while excluding redundant ones. It enhances the classification accuracy of a dataset and holds a crucial position in the field of data mining. Utilizing metaheuristic algorithms for selecting feature subsets contributes to optimizing the FS problem. The White Shark Optimizer (WSO), as a metaheuristic algorithm, primarily simulates the behavior of great white sharks’ sense of hearing and smelling during swimming and hunting. However, it fails to consider their other randomly occurring behaviors, for example, Tail Slapping and Clustered Together behaviors. The Tail Slapping behavior can increase population diversity and improve the global search performance of the algorithm. The Clustered Together behavior includes access to food and mating, which can change the direction of local search and enhance local utilization. It incorporates Tail Slapping and Clustered Together behavior into the original algorithm to propose an Improved White Shark Optimizer (IWSO). The two behaviors and the presented IWSO are tested separately using the CEC2017 benchmark functions, and the test results of IWSO are compared with other metaheuristic algorithms, which proves that IWSO combining the two behaviors has a stronger search capability. Feature selection can be mathematically described as a weighted combination of feature subset size and classification error rate as an optimization model, which is iteratively optimized using discretized IWSO which combines with K-Nearest Neighbor (KNN) on 16 benchmark datasets and the results are compared with 7 metaheuristics. Experimental results show that the IWSO is more capable in selecting feature subsets and improving classification accuracy.

Photovoltaic (PV) power generation is highly regarded for its capability to transform solar energy into electrical power. However, in real-world applications, PV modules are prone to issues such as increased self-heating and surface dust accumulation, which contribute to a reduction in photoelectric conversion efficiency. Furthermore, elevated temperatures can adversely affect the components’ operational longevity. To augment the efficiency and extend the lifespan of PV modules, it is crucial to implement cooling strategies and periodic surface dust removal. In this research, we introduce a composite PV module design that amalgamates a hygroscopic hydrogel with self-cleaning attributes. The design incorporates a superhydrophobic polydimethylsiloxane (PDMS) film as its exposed surface layer and employs a PAM-CaCl₂-SiC hygroscopic hydrogel for rear cooling. This arrangement is intended to facilitate efficient surface self-cleaning and passive cooling of the composite PV module. Experimental studies were conducted to evaluate the performance of this innovative composite PV module design, and the results showed that the composite PV panel had an increase of about 1.39% in power generation compared to an ordinary PV panel in the spring of Shenzhen, China.

Biomimetic micro-robot technology based on non-contact and cable-free magnetic actuation has become one of the crucial focuses of future biomedical research and micro-industrial development. Inspired by the motion characteristics of ray fish, this article proposes a micro-robot with magnetic controlled bionic ray structure. The micro-robot is made of soft elastic materials such as poly dimethyl siloxane (PDMS), Ethylene-Propylene-Diene Monomer (EPDM), and magnetic material Neodymium Iron Boron (NdFeB) nanoparticles. The external driving magnetic field is a periodic oscillating magnetic field generated by a Helmholtz coil. In order to verify the feasibility of the ray-inspired micro-robot, the motion principle was analyzed and several experiments were carried out. Experimental results demonstrated that the ray-inspired micro-robot can excellently mimic the crucial swimming characteristics of rays under the driving force of a oscillating magnetic field with an intensity of 5 mT and a frequency of 5 Hz, the swimming speed of the biomimetic micro-robot can reach nearly 2 body lengths per second. Analysis shows that the speed and stability of the micro-robot primarily depends not only on the amplitude and frequency of the vertical oscillating magnetic field, but also on the magnitude of the horizontal uniform magnetic field. This article demonstrates that the designed biomimetic micro-robot has the potential application of remotely performing specialized tasks in confined, complex environments such as microchannel-based scenarios.

Along with the flourishing of brain-computer interface technology, the brain-to-brain information transmission between different organisms has received high attention in recent years. However, specific information transmission mode and implementation technology need to be further studied. In this paper, we constructed a brain-to-brain information transmission system between pigeons based on the neural information decoding and electrical stimulation encoding technologies. Our system consists of three parts: (1) the “perception pigeon” learns to distinguish different visual stimuli with two discrepant frequencies, (2) the computer decodes the stimuli based on the neural signals recorded from the “perception pigeon” through a frequency identification algorithm (neural information decoding) and encodes them into different kinds of electrical pulses, (3) the “action pigeon” receives the Intracortical Microstimulation (ICMS) and executes corresponding key-pecking actions through discriminative learning (electrical stimulation encoding). The experimental results show that our brain-to-brain system achieves information transmission from perception to action between two pigeons with the average accuracy of about 72%. Our study verifies the feasibility of information transmission between inter-brain based on neural information decoding and ICMS encoding, providing important technical methods and experimental program references for the development of brain-to-brain communication technology.

The dynamic motion capability of humanoid robots is a key indicator for evaluating their performance. Jumping, as a typical dynamic motion, is of great significance for enhancing the robot’s flexibility and terrain adaptability in unstructured environments. However, achieving high-dynamic jumping control of humanoid robots has become a challenge due to the high degree of freedom and strongly coupled dynamic characteristics. The idea for this paper originated from the human response process to jumping commands, aiming to achieve online trajectory optimization and jumping motion control of humanoid robots. Firstly, we employ nonlinear optimization in combination with the Single Rigid Body Model (SRBM) to generate a robot’s Center of Mass (CoM) trajectory that complies with physical constraints and minimizes the angular momentum of the CoM. Then, a Model Predictive Controller (MPC) is designed to track and control the CoM trajectory, obtaining the required contact forces at the robot’s feet. Finally, a Whole-Body Controller (WBC) is used to generate full-body joint motion trajectories and driving torques, based on the prioritized sequence of tasks designed for the jumping process. The control framework proposed in this paper considers the dynamic characteristics of the robot’s jumping process, with a focus on improving the real-time performance of trajectory optimization and the robustness of controller. Simulation and experimental results demonstrate that our robot successfully executed high jump motions, long jump motions and continuous jump motions under complex working conditions.