Biomimetics最新文献

This research focuses on the design of a three-finger adaptive gripper using additive manufacturing and electromechanical actuators, with the purpose of providing a low-cost, efficient, and reliable solution for easy integration with any robot arm for industrial and research purposes. During the development phase, 3D printing materials were employed in the gripper's design, with Polylactic Acid (PLA) filament used for the rigid mechanical components and Thermoplastic Polyurethane (TPU) for the flexible membranes that distribute pressure to the resistive force sensors. Stress analysis and simulations were conducted to evaluate the performance of the components under load and to gradually refine the design of the adaptive gripper. It was ensured that the mechanism could integrate effectively with the robotic arm and be precisely controlled through a PID controller. Furthermore, the availability of spare parts in the local market was considered essential to guarantee easy and cost-effective maintenance. Tests were conducted on an actual robotic arm, and the designed gripper was able to effectively grasp objects such as a soda can and a pencil. The results demonstrated that the adaptive gripper successfully achieved various types of grasping, offering a scalable and economical solution that represents a significant contribution to the field of robotic manipulation in industrial applications.

The development of biocompatible hydrogels for 3D bioprinting is essential for creating functional tissue models and advancing preclinical drug testing. This study investigates the formulation, printability, mechanical properties, and biocompatibility of a novel Alg-Gel hydrogel blend (alginate and gelatin) for use in extrusion-based 3D bioprinting. A range of hydrogel compositions were evaluated for their rheological behavior, including shear-thinning properties, storage modulus, and compressive modulus, which are crucial for maintaining structural integrity during printing and supporting cell viability. The printability assessment of the 7% alginate-8% gelatin hydrogel demonstrated that the 27T tapered needle achieved the highest normalized Printability Index (POI_normalized = 1), offering the narrowest strand width (0.56 ± 0.02 mm) and the highest printing accuracy (97.2%) at the lowest printing pressure (30 psi). In contrast, the 30R needle, with the smallest inner diameter (0.152 mm) and highest printing pressure (80 psi), resulted in the widest strand width (0.70 ± 0.01 mm) and the lowest accuracy (88.8%), resulting in a POI_normalized of 0.274. The 30T and 27R needles demonstrated moderate performance, with POI_normalized values of 0.758 and 0.558, respectively. The optimized 7% alginate and 8% gelatin blend demonstrated favorable printability, mechanical strength, and cell compatibility with MDA-MB-213 breast cancer cells, exhibiting high cell proliferation rates and minimal cytotoxicity over a 2-week culture period. This formulation offers a balanced approach, providing sufficient viscosity for precision printing while minimizing shear stress to preserve cell health. This work lays the groundwork for future advancements in bioprinted cancer models, contributing to the development of more effective tools for drug screening and personalized medicine.

Objective: To reduce hip joint muscles' activation during walking with an active hip exoskeleton. Background: Few studies examine the optimal active assistance timing of the hip exoskeleton based on muscle activation characteristics. Methods: Sixteen gender-balanced healthy adults (mean age 28.8 years) performed four tasks (each over 20 min). Tasks were different in loading and assistance. Muscle activation was collected by surface electromyography. The collected oxygen consumption evaluated the performance of the proposed active assistance strategy. Results: Experimental results verified that lower muscle activation and metabolism could be achieved when the active assistance gait phase was 9-60% of the gait cycle than that of all-gait-cycle active assist. Conclusions: Regulating the exoskeleton's active assistance timing according to muscles' activation characteristics can improve functional assistance.

To address the challenges of slow convergence speed, poor convergence precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a path planning method based on an Improved Human Evolution Optimization Algorithm (IHEOA). First, a mathematical model is used to construct a three-dimensional terrain environment, and a multi-constraint path cost model is established, framing path planning as a multidimensional function optimization problem. Second, recognizing the sensitivity of population diversity to Logistic Chaotic Mapping in a traditional Human Evolution Optimization Algorithm (HEOA), an opposition-based learning strategy is employed to uniformly initialize the population distribution, thereby enhancing the algorithm's global optimization capability. Additionally, a guidance factor strategy is introduced into the leader role during the development stage, providing clear directionality for the search process, which increases the probability of selecting optimal paths and accelerates the convergence speed. Furthermore, in the loser update strategy, an adaptive t-distribution perturbation strategy is utilized for its small mutation amplitude, which enhances the local search capability and robustness of the algorithm. Evaluations using 12 standard test functions demonstrate that these improvement strategies effectively enhance convergence precision and algorithm stability, with the IHEOA, which integrates multiple strategies, performing particularly well. Experimental comparative research on three different terrain environments and five traditional algorithms shows that the IHEOA not only exhibits excellent performance in terms of convergence speed and precision but also generates superior paths while demonstrating exceptional global optimization capability and robustness in complex environments. These results validate the significant advantages of the proposed improved algorithm in effectively addressing UAV path planning challenges.

Meningitis is the acute or chronic inflammation of the protective membranes, surrounding the brain and spinal cord, and this inflammatory process spreads throughout the subarachnoid space. The traditional drug delivery methods pose a disadvantage in limiting the capacity of crossing the blood-brain barrier (BBB) to reach the central nervous system (CNS). Hence, it is imperative to develop novel approaches that can overcome these constraints and offer efficient therapy for meningitis. Nanoparticle (NP)-based therapeutic approaches have the potential to address the limitations such as penetrating the BBB and achieving targeted drug release in specific cells and tissues. This review highlights recent advancements in nanotechnology-based approaches, such as functionalized polymeric nanoparticles, solid lipid nanoparticles (SLNs), nanostructured lipid carriers, nanoemulsions, liposomes, transferosomes, and metallic NPs for the treatment of meningitis. Recently, bionics has emerged as a next-generation technology in the development of novel ideas from biological principles, structures, and interactions for neurological and neuroinfectious diseases. Despite their potential, more studies are needed to ensure the safety and efficacy of NP-based drug delivery systems focusing on critical aspects such as toxicity, immunogenicity, and pharmacokinetics. Therefore, this review addresses current treatment strategies and innovative nanoparticle approaches, and it discusses future directions for efficient and targeted meningitis therapies.

The decomposition-based multi-objective optimization algorithm MOEA/D (multi-objective evolutionary algorithm based on decomposition) introduces the concept of neighborhood, where each sub-problem requires optimization through solutions within its neighborhood. Due to the comparison being only with solutions in the neighborhood, the obtained set of solutions is not sufficiently diverse, leading to poorer convergence properties. In order to adequately acquire a high-quality set of solutions, this algorithm requires a large number of population iterations, which in turn results in relatively low computational efficiency. To address this issue, this paper proposes an algorithm termed MOEA/D-NRD, which is based on neighborhood region domination in the MOEA/D framework. In the improved algorithm, domination relationships are determined by comparing offspring solutions against neighborhood ideal points and neighborhood worst points. By selecting appropriate solution sets within these comparison regions, the solution sets can approach the ideal points more and faster, thereby accelerating population convergence and enhancing the computational efficiency of the algorithm.

Trunk-like robots have attracted a lot of attention in the community of researchers interested in the general field of bio-inspired soft robotics, because trunk-like soft arms may offer high dexterity and adaptability very similar to elephants and potentially quite superior to traditional articulated manipulators. In view of the practical applications, the integration of a soft hydrostatic segment with a hard-articulated segment, i.e., a hybrid kinematic structure similar to the elephant's body, is probably the best design framework. It is proposed that this integration should occur at the conceptual/cognitive level before being implemented in specific soft technologies, including the related control paradigms. The proposed modeling approach is based on the passive motion paradigm (PMP), originally conceived for addressing the degrees of freedom problem of highly redundant, articulated structures. It is shown that this approach can be naturally extended from highly redundant to hyper-redundant structures, including hybrid structures that include a hard and a soft component. The PMP model is force-based, not motion-based, and it is characterized by two main computational modules: the Jacobian matrix of the hybrid kinematic chain and a compliance matrix that maps generalized force fields into coordinated gestures of the whole-body model. It is shown how the modulation of the compliance matrix can be used for the synergy formation process, which coordinates the hyper-redundant nature of the hybrid body model and, at the same time, for the preparation of the trunk tip in view of a stable physical interaction of the body with the environment, in agreement with the general impedance-control concept.

Superhydrophobic coatings are beneficial for applications like self-cleaning, anti-corrosion, and drag reduction. In this study, we investigated the impact of surface geometry on the static, dynamic, and sliding contact angles in the Cassie-Baxter state. We used fluoro-silane-treated silicon micro-post patterns fabricated via lithography as model surfaces. By varying the solid fraction (ϕ_s), edge-to-edge spacing (L), and the shape and arrangement of the micro-posts, we examined how these geometric factors influence wetting behavior. Our results show that the solid fraction is the key factor affecting both dynamic and sliding angles, while changes in shape and arrangement had minimal impact. The Cassie-Baxter model accurately predicted receding angles but struggled to predict advancing angles. These insights can guide the development of coatings with enhanced superhydrophobic properties, tailored to achieve higher contact angles and customized for different environmental conditions.

Insects enhance aerodynamic flight control using the dynamic movement of their appendages, aiding in balance, stability, and manoeuvrability. Although biologists have observed these behaviours, the phenomena have not been expressed in a unified mathematical flight dynamics framework. For instance, relevant existing models tend to disregard either the aerodynamic or the inertial effects of the appendages of insects, such as the abdomen, based on the assumption that appendage dynamic effects dominate in comparison to aerodynamic effects, or that appendages are stationary. However, appendages in insects exist in various shapes and sizes, which affect the level of both the inertial and aerodynamic contributions to the overall system. Here, the effects of the individual dynamic, inertial and aerodynamic contributions of biologically inspired appendages in fixed wing forward flight demonstrate the utility of the framework on an example system. The analysis demonstrates the effect of these aerodynamic appendages on the steady flight and manoeuvre performance of a small aircraft with an actuated aft appendage capable of movement in the longitudinal and lateral axes, analogous to an insect abdomen. We use the method to consider designs with different appendage areas. The example case showed that ignoring the aerodynamic contribution might yield useful insights depending on the size of the appendage, but including the aerodynamic effects as part of a consistent mathematical framework leads to a more comprehensive understanding of the role of appendage morphology. The method allows improved modelling for modern multivariate control system design using bioinspired appendages. Inertia-dominated appendages provided more advantages in energy-based longitudinal manoeuvres and in trimmed flight, with reduced advantage in initiating lateral manoeuvres.

In this research, inspired by the principles of biological visual attention mechanisms and swarm intelligence found in nature, we present an Enhanced Self-Correlation Attention and Multi-Branch Joint Module Network (EMNet), a novel model for few-shot image classification. Few-shot image classification aims to address the problem of image classification when data are limited. Traditional models require a large amount of labeled data for training, while few-shot learning trains models using only a small number of samples (just a few samples per class) to recognize new categories. EMNet shows its potential for bio-inspired algorithms in optimizing feature extraction and enhancing generalization capabilities. It features two key modules: Enhanced Self-Correlated Attention (ESCA) and Multi-Branch Joint Module (MBJ Module). EMNet tackles two main challenges in few-shot learning: how to make an effective important feature extraction and enhancement in images, and improving generalization to new categories. The ESCA module boosts the precision in extracting crucial local features, enhancing classification accuracy. The MBJ module focuses on shared features across images, emphasizing similarities within classes and subtle differences between them. This enhances model adaptability and generalization to new categories. Experimental results show that our model performs better than existing models in one-shot and five-shot tasks on mini-ImageNet, CUB-200, and CIFAR-FS datasets, which proves the proposed model to be an efficient end-to-end solution for few-shot image classification. In the five-way one-shot and five-way five-shot experiments on the CUB-200-2011 dataset, EMNet achieved classification accuracies that were 1.27 and 0.54 percentage points higher than those of RENet, respectively. In the five-way one-shot and five-way five-shot experiments on the miniImageNet dataset, EMNet's classification accuracies were 0.02 and 0.48 percentage points higher than those of RENet, respectively. In the five-way one-shot and five-way five-shot experiments on the CIFAR-FS dataset, EMNet's classification accuracies were 0.19 and 0.18 percentage points higher than those of RENet.