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Biomimetics, Volume 10, Issue 7 (July 2025) – 67 articles

Cover Story (view full-size image): The paper presents the design and optimization of a spider-inspired soft actuator intended for extraterrestrial exploration. Mimicking spider limb movement, the actuator uses lightweight, flexible polymers and pneumatic chambers for adaptable, robust motion in rough planetary terrains. Optimization informed by experimental data improved limb dexterity and performance for dynamic motion. A simple demonstration robot was built, demonstrating efficient untethered soft robotic jumping and movement. View this paper
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23 pages, 3554 KiB  
Article
Multi-Sensor Fusion Framework for Reliable Localization and Trajectory Tracking of Mobile Robot by Integrating UWB, Odometry, and AHRS
by Quoc-Khai Tran and Young-Jae Ryoo
Biomimetics 2025, 10(7), 478; https://doi.org/10.3390/biomimetics10070478 - 21 Jul 2025
Viewed by 636
Abstract
This paper presents a multi-sensor fusion framework for the accurate indoor localization and trajectory tracking of a differential-drive mobile robot. The proposed system integrates Ultra-Wideband (UWB) trilateration, wheel odometry, and Attitude and Heading Reference System (AHRS) data using a Kalman filter. This fusion [...] Read more.
This paper presents a multi-sensor fusion framework for the accurate indoor localization and trajectory tracking of a differential-drive mobile robot. The proposed system integrates Ultra-Wideband (UWB) trilateration, wheel odometry, and Attitude and Heading Reference System (AHRS) data using a Kalman filter. This fusion approach reduces the impact of noisy and inaccurate UWB measurements while correcting odometry drift. The system combines raw UWB distance measurements with wheel encoder readings and heading information from an AHRS to improve robustness and positioning accuracy. Experimental validation was conducted through repeated closed-loop trajectory trials. The results demonstrate that the proposed method significantly outperforms UWB-only localization, yielding reduced noise, enhanced consistency, and lower Dynamic Time Warping (DTW) distances across repetitions. The findings confirm the system’s effectiveness and suitability for real-time mobile robot navigation in indoor environments. Full article
(This article belongs to the Special Issue Advanced Intelligent Systems and Biomimetics)
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38 pages, 9771 KiB  
Article
Global Research Trends in Biomimetic Lattice Structures for Energy Absorption and Deformation: A Bibliometric Analysis (2020–2025)
by Sunny Narayan, Brahim Menacer, Muhammad Usman Kaisan, Joseph Samuel, Moaz Al-Lehaibi, Faisal O. Mahroogi and Víctor Tuninetti
Biomimetics 2025, 10(7), 477; https://doi.org/10.3390/biomimetics10070477 - 19 Jul 2025
Viewed by 1104
Abstract
Biomimetic lattice structures, inspired by natural architectures such as bone, coral, mollusk shells, and Euplectella aspergillum, have gained increasing attention for their exceptional strength-to-weight ratios, energy absorption, and deformation control. These properties make them ideal for advanced engineering applications in aerospace, biomedical devices, [...] Read more.
Biomimetic lattice structures, inspired by natural architectures such as bone, coral, mollusk shells, and Euplectella aspergillum, have gained increasing attention for their exceptional strength-to-weight ratios, energy absorption, and deformation control. These properties make them ideal for advanced engineering applications in aerospace, biomedical devices, and structural impact protection. This study presents a comprehensive bibliometric analysis of global research on biomimetic lattice structures published between 2020 and 2025, aiming to identify thematic trends, collaboration patterns, and underexplored areas. A curated dataset of 3685 publications was extracted from databases like PubMed, Dimensions, Scopus, IEEE, Google Scholar, and Science Direct and merged together. After the removal of duplication and cleaning, about 2226 full research articles selected for the bibliometric analysis excluding review works, conference papers, book chapters, and notes using Cite space, VOS viewer version 1.6.20, and Bibliometrix R packages (4.5. 64-bit) for mapping co-authorship networks, institutional affiliations, keyword co-occurrence, and citation relationships. A significant increase in the number of publications was found over the past year, reflecting growing interest in this area. The results identify China as the most prolific contributor, with substantial institutional support and active collaboration networks, especially with European research groups. Key research focuses include additive manufacturing, finite element modeling, machine learning-based design optimization, and the performance evaluation of bioinspired geometries. Notably, the integration of artificial intelligence into structural modeling is accelerating a shift toward data-driven design frameworks. However, gaps remain in geometric modeling standardization, fatigue behavior analysis, and the real-world validation of lattice structures under complex loading conditions. This study provides a strategic overview of current research directions and offers guidance for future interdisciplinary exploration. The insights are intended to support researchers and practitioners in advancing next-generation biomimetic materials with superior mechanical performance and application-specific adaptability. Full article
(This article belongs to the Special Issue Nature-Inspired Science and Engineering for Sustainable Future)
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45 pages, 11380 KiB  
Article
Application of Multi-Strategy Controlled Rime Algorithm in Path Planning for Delivery Robots
by Haokai Lv, Qian Qian, Jiawen Pan, Miao Song, Yong Feng and Yingna Li
Biomimetics 2025, 10(7), 476; https://doi.org/10.3390/biomimetics10070476 - 19 Jul 2025
Viewed by 540
Abstract
As a core component of automated logistics systems, delivery robots hold significant application value in the field of unmanned delivery. This research addresses the robot path planning problem, aiming to enhance delivery efficiency and reduce operational costs through systematic improvements to the RIME [...] Read more.
As a core component of automated logistics systems, delivery robots hold significant application value in the field of unmanned delivery. This research addresses the robot path planning problem, aiming to enhance delivery efficiency and reduce operational costs through systematic improvements to the RIME optimization algorithm. Through in-depth analysis, we identified several major drawbacks in the standard RIME algorithm for path planning: insufficient global exploration capability in the initial stages, a lack of diversity in the hard RIME search mechanism, and oscillatory phenomena in soft RIME step size adjustment. These issues often lead to undesirable phenomena in path planning, such as local optima traps, path redundancy, or unsmooth trajectories. To address these limitations, this study proposes the Multi-Strategy Controlled Rime Algorithm (MSRIME), whose innovation primarily manifests in three aspects: first, it constructs a multi-strategy collaborative optimization framework, utilizing an infinite folding Fuch chaotic map for intelligent population initialization to significantly enhance the diversity of solutions; second, it designs a cooperative mechanism between a controlled elite strategy and an adaptive search strategy that, through a dynamic control factor, autonomously adjusts the strategy activation probability and adaptation rate, expanding the search space while ensuring algorithmic convergence efficiency; and finally, it introduces a cosine annealing strategy to improve the step size adjustment mechanism, reducing parameter sensitivity and effectively preventing path distortions caused by abrupt step size changes. During the algorithm validation phase, comparative tests were conducted between two groups of algorithms, demonstrating their significant advantages in optimization capability, convergence speed, and stability. Further experimental analysis confirmed that the algorithm’s multi-strategy framework effectively suppresses the impact of coordinate and dimensional differences on path quality during iteration, making it more suitable for delivery robot path planning scenarios. Ultimately, path planning experimental results across various Building Coverage Rate (BCR) maps and diverse application scenarios show that MSRIME exhibits superior performance in key indicators such as path length, running time, and smoothness, providing novel technical insights and practical solutions for the interdisciplinary research between intelligent logistics and computer science. Full article
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18 pages, 4607 KiB  
Article
Multi-Objective Machine Learning Optimization of Cylindrical TPMS Lattices for Bone Implants
by Mansoureh Rezapourian, Ali Cheloee Darabi, Mohammadreza Khoshbin and Irina Hussainova
Biomimetics 2025, 10(7), 475; https://doi.org/10.3390/biomimetics10070475 - 18 Jul 2025
Viewed by 720
Abstract
This study presents a multi-objective optimization framework for designing cylindrical triply periodic minimal surface (TPMS) lattices tailored for bone implant applications. Using an artificial neural network (ANN) as a surrogate model trained on simulated data, four key properties—ultimate stress (U), energy absorption (EA), [...] Read more.
This study presents a multi-objective optimization framework for designing cylindrical triply periodic minimal surface (TPMS) lattices tailored for bone implant applications. Using an artificial neural network (ANN) as a surrogate model trained on simulated data, four key properties—ultimate stress (U), energy absorption (EA), surface area-to-volume ratio (SA/VR), and relative density (RD)—were predicted from seven lattice design parameters. To address anatomical variability, a novel implant size-based categorization (small, medium, and large) was introduced, and separate optimization runs were conducted for each group. The optimization was performed via the NSGA-II algorithm to maximize mechanical performance (U and EA) and surface efficiency (SA/VR), while filtering for biologically relevant RD values (20–40%). Separate optimization runs were conducted for small, medium, and large implant size groups. A total of 105 Pareto-optimal designs were identified, with 75 designs retained after RD filtering. SHapley Additive exPlanations (SHAP) analysis revealed the dominant influence of thickness and unit cell size on target properties. Kernel density and boxplot comparisons confirmed distinct performance trends across size groups. The framework effectively balances competing design goals and enables the selection of size-specific lattices. The proposed approach provides a reproducible pathway for optimizing bioarchitectures, with the potential to accelerate the development of lattice-based implants in personalized medicine. Full article
(This article belongs to the Special Issue Biomimicry and Functional Materials: 5th Edition)
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17 pages, 3302 KiB  
Article
Effects of Ovariectomy and Low-Calcium Diet on Six Different Sites of the Rat Skeleton
by Xanthippi Dereka, Rodopi Emfietzoglou and Pavlos Lelovas
Biomimetics 2025, 10(7), 474; https://doi.org/10.3390/biomimetics10070474 - 18 Jul 2025
Viewed by 425
Abstract
The aim of this study was to evaluate structural and micro-architectural changes in the mandible, parietal bone, femur, and tibia in OVX rats at different time periods after ovariectomy. Forty-two 11-month-old female Wistar rats were used. Six rats without surgery were euthanized to [...] Read more.
The aim of this study was to evaluate structural and micro-architectural changes in the mandible, parietal bone, femur, and tibia in OVX rats at different time periods after ovariectomy. Forty-two 11-month-old female Wistar rats were used. Six rats without surgery were euthanized to serve as a baseline. Eighteen rats were ovariectomized and fed with a calcium-deficient diet, and eighteen animals were used as controls (Ctrls) and fed with a standard diet. Six OVX rats and six Ctrls were euthanized at 3, 6, and 9 months. Qualitative histology and dual-energy X-ray absorptiometry (DXA) were performed. Histological evaluation of bones harvested from the OVX groups revealed trabecular bone reduction, while no significant differences in the cortical bone of OVX and Ctrls were observed. DXA measurements of (1) femoral diaphysis showed a significant decrease in the OVX group compared to the Ctrl groups at 3 (p = 0.041), 6 (p < 0.001), and 9 months (p < 0.001); (2) the proximal tibia showed a significant decrease in the OVX group compared to the Ctrl groups (p < 0.001); (3) parietal bone showed a significant difference between OVX and Ctrls at 6 months (p = 0.012); and (4) the mandible showed no significant differences between the OVX and Ctrl groups. OVX aged rats might present reductions in the density of the femoral diaphysis, proximal tibia, parietal bone, and mandible at different time points. These findings contribute to the field of biomimetics by providing more details for the understanding of age- and hormone-related bone changes in the osteoporotic-like rat model. Such data are critical for the development of biomimetic materials and structures that attempt to simulate natural bone adaptation and deterioration, especially in the context of postmenopausal or osteoporotic conditions. Full article
(This article belongs to the Section Biomimetics of Materials and Structures)
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17 pages, 6781 KiB  
Article
Fish Scale-Inspired Flow Control for Corner Vortex Suppression in Compressor Cascades
by Jin-Long Shen, Ho-Chun Yang and Szu-I Yeh
Biomimetics 2025, 10(7), 473; https://doi.org/10.3390/biomimetics10070473 - 18 Jul 2025
Viewed by 402
Abstract
Corner separation at the junction of blade surfaces and end walls remains a significant challenge in compressor cascade performance. This study proposes a passive flow control strategy inspired by the geometric arrangement of biological fish scales to address this issue. A fish scale-like [...] Read more.
Corner separation at the junction of blade surfaces and end walls remains a significant challenge in compressor cascade performance. This study proposes a passive flow control strategy inspired by the geometric arrangement of biological fish scales to address this issue. A fish scale-like surface structure was applied to the suction side of a cascade blade to reduce viscous drag and modulate secondary flow behavior. Wind tunnel experiments and numerical simulations were conducted to evaluate its aerodynamic effects. The results show that the fish scale-inspired configuration induced climbing vortices that energized low-momentum fluid near the end wall, effectively suppressing both passage and corner vortices. This led to a reduction in spanwise flow penetration and a decrease in total pressure loss of up to 5.69%. The enhanced control of secondary flows also contributed to improved flow uniformity in the end-wall region. These findings highlight the potential of biologically inspired surface designs for corner vortex suppression and aerodynamic efficiency improvement in turbomachinery systems. Full article
(This article belongs to the Special Issue Bio-Inspired Propulsion and Fluid Mechanics)
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26 pages, 5344 KiB  
Article
Sliding Mode Repetitive Control Based on the Unknown Dynamics Estimator of a Two-Stage Supply Pressure Hydraulic Hexapod Robot
by Ziqi Liu, Bo Jin, Junkui Dong, Qingyun Yao, Yinglian Jin, Tao Liu and Binrui Wang
Biomimetics 2025, 10(7), 472; https://doi.org/10.3390/biomimetics10070472 - 18 Jul 2025
Viewed by 299
Abstract
Hydraulic actuated legged robots display bright prospects and significant research value in areas such as unmanned area surveying, disaster rescue, military fields, and other scenarios owing to their excellent bionic characteristics, particularly their heavy payload capabilities and high power density. To realize the [...] Read more.
Hydraulic actuated legged robots display bright prospects and significant research value in areas such as unmanned area surveying, disaster rescue, military fields, and other scenarios owing to their excellent bionic characteristics, particularly their heavy payload capabilities and high power density. To realize the all-terrain adaptation locomotion of the hydraulic hexapod robot (HHR) with a heavy payload, one alternative control framework is position–posture control based on joint position control. As the foundation for the steady locomotion of HHRs, it is imperative to realize high-precision joint position control to improve the robustness under external disturbances during the walking process and to complete the attitude control task. To address the above issues, this paper proposes a sliding mode repetitive control based on the unknown dynamics estimator (SMRC + UDE) for the knee and hip joints of the HHR with a two-stage supply pressure hydraulic system (TSS). The effectiveness of the SMRC + UDE method is verified using a simulation environment and the ZJUHEX01 prototype experimental platform, and it is compared with the results for PID and adaptive robust sliding mode control (ARSMC). The results show that SMRC + UDE may be more suitable for our HHR, considering both the control performance and efficiency factors. Full article
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49 pages, 7424 KiB  
Article
ACIVY: An Enhanced IVY Optimization Algorithm with Adaptive Cross Strategies for Complex Engineering Design and UAV Navigation
by Heming Jia, Mahmoud Abdel-salam and Gang Hu
Biomimetics 2025, 10(7), 471; https://doi.org/10.3390/biomimetics10070471 - 17 Jul 2025
Cited by 1 | Viewed by 420
Abstract
The Adaptive Cross Ivy (ACIVY) algorithm is a novel bio-inspired metaheuristic that emulates ivy plant growth behaviors for complex optimization problems. While the original Ivy Optimization Algorithm (IVYA) demonstrates a competitive performance, it suffers from limited inter-individual information exchange, inadequate directional guidance for [...] Read more.
The Adaptive Cross Ivy (ACIVY) algorithm is a novel bio-inspired metaheuristic that emulates ivy plant growth behaviors for complex optimization problems. While the original Ivy Optimization Algorithm (IVYA) demonstrates a competitive performance, it suffers from limited inter-individual information exchange, inadequate directional guidance for local optima escape, and abrupt exploration–exploitation transitions. To address these limitations, ACIVY integrates three strategic enhancements: the crisscross strategy, enabling horizontal and vertical crossover operations for improved population diversity; the LightTrack strategy, incorporating positional memory and repulsion mechanisms for effective local optima escape; and the Top-Guided Adaptive Mutation strategy, implementing ranking-based mutation with dynamic selection pools for smooth exploration–exploitation balance. Comprehensive evaluations on the CEC2017 and CEC2022 benchmark suites demonstrate ACIVY’s superior performance against state-of-the-art algorithms across unimodal, multimodal, hybrid, and composite functions. ACIVY achieved outstanding average rankings of 1.25 (CEC2022) and 1.41 (CEC2017 50D), with statistical significance confirmed through Wilcoxon tests. Practical applications in engineering design optimization and UAV path planning further validate ACIVY’s robust performance, consistently delivering optimal solutions across diverse real-world scenarios. The algorithm’s exceptional convergence precision, solution reliability, and computational efficiency establish it as a powerful tool for challenging optimization problems requiring both accuracy and consistency. Full article
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33 pages, 5578 KiB  
Review
Underwater Drag Reduction Applications and Fabrication of Bio-Inspired Surfaces: A Review
by Zaixiang Zheng, Xin Gu, Shengnan Yang, Yue Wang, Ying Zhang, Qingzhen Han and Pan Cao
Biomimetics 2025, 10(7), 470; https://doi.org/10.3390/biomimetics10070470 - 17 Jul 2025
Viewed by 776
Abstract
As an emerging energy-saving approach, bio-inspired drag reduction technology has become a key research direction for reducing energy consumption and greenhouse gas emissions. This study introduces the latest research progress on bio-inspired microstructured surfaces in the field of underwater drag reduction, focusing on [...] Read more.
As an emerging energy-saving approach, bio-inspired drag reduction technology has become a key research direction for reducing energy consumption and greenhouse gas emissions. This study introduces the latest research progress on bio-inspired microstructured surfaces in the field of underwater drag reduction, focusing on analyzing the drag reduction mechanism, preparation process, and application effect of the three major technological paths; namely, bio-inspired non-smooth surfaces, bio-inspired superhydrophobic surfaces, and bio-inspired modified coatings. Bio-inspired non-smooth surfaces can significantly reduce the wall shear stress by regulating the flow characteristics of the turbulent boundary layer through microstructure design. Bio-inspired superhydrophobic surfaces form stable gas–liquid interfaces through the construction of micro-nanostructures and reduce frictional resistance by utilizing the slip boundary effect. Bio-inspired modified coatings, on the other hand, realize the synergistic function of drag reduction and antifouling through targeted chemical modification of materials and design of micro-nanostructures. Although these technologies have made significant progress in drag reduction performance, their engineering applications still face bottlenecks such as manufacturing process complexity, gas layer stability, and durability. Future research should focus on the analysis of drag reduction mechanisms and optimization of material properties under multi-physical field coupling conditions, the development of efficient and low-cost manufacturing processes, and the enhancement of surface stability and adaptability through dynamic self-healing coatings and smart response materials. It is hoped that the latest research status of bio-inspired drag reduction technology reviewed in this study provides a theoretical basis and technical reference for the sustainable development and energy-saving design of ships and underwater vehicles. Full article
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21 pages, 7868 KiB  
Article
Robust Visuomotor Control for Humanoid Loco-Manipulation Using Hybrid Reinforcement Learning
by Chenzheng Wang, Qiang Huang, Xuechao Chen, Zeyu Zhang and Jing Shi
Biomimetics 2025, 10(7), 469; https://doi.org/10.3390/biomimetics10070469 - 17 Jul 2025
Viewed by 800
Abstract
Loco-manipulation tasks using humanoid robots have great practical value in various scenarios. While reinforcement learning (RL) has become a powerful tool for versatile and robust whole-body humanoid control, visuomotor control in loco-manipulation tasks with RL remains a great challenge due to their high [...] Read more.
Loco-manipulation tasks using humanoid robots have great practical value in various scenarios. While reinforcement learning (RL) has become a powerful tool for versatile and robust whole-body humanoid control, visuomotor control in loco-manipulation tasks with RL remains a great challenge due to their high dimensionality and long-horizon exploration issues. In this paper, we propose a loco-manipulation control framework for humanoid robots that utilizes model-free RL upon model-based control in the robot’s tasks space. It implements a visuomotor policy with depth-image input, and uses mid-way initialization and prioritized experience sampling to accelerate policy convergence. The proposed method is validated on typical loco-manipulation tasks of load carrying and door opening resulting in an overall success rate of 83%, where our framework automatically adjusts the robot motion in reaction to changes in the environment. Full article
(This article belongs to the Special Issue Bio-Inspired and Biomimetic Intelligence in Robotics: 2nd Edition)
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15 pages, 5441 KiB  
Article
Task-Related EEG as a Biomarker for Preclinical Alzheimer’s Disease: An Explainable Deep Learning Approach
by Ziyang Li, Hong Wang and Lei Li
Biomimetics 2025, 10(7), 468; https://doi.org/10.3390/biomimetics10070468 - 16 Jul 2025
Viewed by 621
Abstract
The early detection of Alzheimer’s disease (AD) in cognitively healthy individuals remains a major preclinical challenge. EEG is a promising tool that has shown effectiveness in detecting AD risk. Task-related EEG has been rarely used in Alzheimer’s disease research, as most studies have [...] Read more.
The early detection of Alzheimer’s disease (AD) in cognitively healthy individuals remains a major preclinical challenge. EEG is a promising tool that has shown effectiveness in detecting AD risk. Task-related EEG has been rarely used in Alzheimer’s disease research, as most studies have focused on resting-state EEG. An interpretable deep learning framework—Interpretable Convolutional Neural Network (InterpretableCNN)—was utilized to identify AD-related EEG features. EEG data were recorded during three cognitive task conditions, and samples were labeled based on APOE genotype and polygenic risk scores. A 100-fold leave-p%-subjects-out cross-validation (LPSO-CV) was used to evaluate model performance and generalizability. The model achieved an ROC AUC of 60.84% across the tasks and subjects, with a Kappa value of 0.22, indicating fair agreement. Interpretation revealed a consistent focus on theta and alpha activity in the parietal and temporal regions—areas commonly associated with AD pathology. Task-related EEG combined with interpretable deep learning can reveal early AD risk signatures in healthy individuals. InterpretableCNN enhances transparency in feature identification, offering a valuable tool for preclinical screening. Full article
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43 pages, 7260 KiB  
Article
A Solution Method for Non-Linear Underdetermined Equation Systems in Grounding Grid Corrosion Diagnosis Based on an Enhanced Hippopotamus Optimization Algorithm
by Jinhe Chen, Jianyu Qi, Yiyang Ao, Keying Wang and Xin Song
Biomimetics 2025, 10(7), 467; https://doi.org/10.3390/biomimetics10070467 - 16 Jul 2025
Viewed by 576
Abstract
As power grids scale and aging assets edge toward obsolescence, grounding grid corrosion has become a critical vulnerability. Conventional diagnosis must fit high-dimensional electrical data to a physical model, typically yielding a nonlinear under-determined system fraught with computational burden and uncertainty. We propose [...] Read more.
As power grids scale and aging assets edge toward obsolescence, grounding grid corrosion has become a critical vulnerability. Conventional diagnosis must fit high-dimensional electrical data to a physical model, typically yielding a nonlinear under-determined system fraught with computational burden and uncertainty. We propose the Enhanced Biomimetic Hippopotamus Optimization (EBOHO) algorithm, which distills the river-dwelling hippo’s ecological wisdom into three synergistic strategies: a beta-function herd seeding that replicates the genetic diversity of juvenile hippos diffusing through wetlands, an elite–mean cooperative foraging rule that echoes the way dominant bulls steer the herd toward nutrient-rich pastures, and a lens imaging opposition maneuver inspired by moonlit water reflections that spawn mirror candidates to avert premature convergence. Benchmarks on the CEC 2017 suite and four classical design problems show EBOHO’s superior global search, robustness, and convergence speed over numerous state-of-the-art meta-heuristics, including prior hippo variants. An industrial case study on grounding grid corrosion further confirms that EBOHO swiftly resolves the under-determined equations and pinpoints corrosion sites with high precision, underscoring its promise as a nature-inspired diagnostic engine for aging power system infrastructure. Full article
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27 pages, 4077 KiB  
Review
Biomimetic Robotics and Sensing for Healthcare Applications and Rehabilitation: A Systematic Review
by H. M. K. K. M. B. Herath, Nuwan Madusanka, S. L. P. Yasakethu, Chaminda Hewage and Byeong-Il Lee
Biomimetics 2025, 10(7), 466; https://doi.org/10.3390/biomimetics10070466 - 16 Jul 2025
Viewed by 991
Abstract
Biomimetic robotics and sensor technologies are reshaping the landscape of healthcare and rehabilitation. Despite significant progress across various domains, many areas within healthcare still demand further bio-inspired innovations. To advance this field effectively, it is essential to synthesize existing research, identify persistent knowledge [...] Read more.
Biomimetic robotics and sensor technologies are reshaping the landscape of healthcare and rehabilitation. Despite significant progress across various domains, many areas within healthcare still demand further bio-inspired innovations. To advance this field effectively, it is essential to synthesize existing research, identify persistent knowledge gaps, and establish clear frameworks to guide future developments. This systematic review addresses these needs by analyzing 89 peer-reviewed sources retrieved from the Scopus database, focusing on the application of biomimetic robotics and sensing technologies in healthcare and rehabilitation contexts. The findings indicate a predominant focus on enhancing human mobility and support, with rehabilitative and assistive technologies comprising 61.8% of the reviewed literature. Additionally, 12.36% of the studies incorporate intelligent control systems and Artificial Intelligence (AI), reflecting a growing trend toward adaptive and autonomous solutions. Further technological advancements are demonstrated by research in bioengineering applications (13.48%) and innovations in soft robotics with smart actuation mechanisms (11.24%). The development of medical robots (7.87%) and wearable robotics, including exosuits (10.11%), underscores specific progress in clinical and patient-centered care. Moreover, the emergence of transdisciplinary approaches, present in 6.74% of the studies, highlights the increasing convergence of diverse fields in tackling complex healthcare challenges. By consolidating current research efforts, this review aims to provide a comprehensive overview of the state of the art, serving as a foundation for future investigations aimed at improving healthcare outcomes and enhancing quality of life. Full article
(This article belongs to the Special Issue Bio-Inspired and Biomimetic Intelligence in Robotics: 2nd Edition)
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22 pages, 5400 KiB  
Article
Polyaniline/Ti3C2 MXene Composites with Artificial 3D Biomimetic Surface Structure of Natural Macaw Feather Applied for Anticorrosion Coatings
by Chen-Cheng Chien, Yu-Hsuan Liu, Kun-Hao Luo, Ting-Yun Liu, Yi-Ting Kao, Shih-Harn Yang and Jui-Ming Yeh
Biomimetics 2025, 10(7), 465; https://doi.org/10.3390/biomimetics10070465 - 15 Jul 2025
Viewed by 430
Abstract
In this paper, a series of polyaniline (PANI)/Ti3C2 MXene composites (PMCs) with a biomimetic structure were prepared and employed as an anticorrosion coating application. First, the PANI was synthesized by oxidative polymerization with ammonium persulfate as the oxidant. Then, 2D [...] Read more.
In this paper, a series of polyaniline (PANI)/Ti3C2 MXene composites (PMCs) with a biomimetic structure were prepared and employed as an anticorrosion coating application. First, the PANI was synthesized by oxidative polymerization with ammonium persulfate as the oxidant. Then, 2D Ti3C2 MXene nanosheets were prepared by treating the Ti3AlC2 using the optimized minimally intensive layer delamination (MILD) method, followed by characterization via XRD and SEM. Subsequently, the PMC was prepared by the oxidative polymerization of aniline monomers in the presence of Ti3C2 MXene nanosheets, followed by characterization via FTIR, XRD, SEM, TEM, CV, and UV–Visible. Eventually, the PMC coatings with the artificial biomimetic surface structure of a macaw feather were prepared by the nano-casting technique. The corrosion resistance of the PMC coatings, evaluated via Tafel polarization and Nyquist impedance measurements, shows that increasing the MXene loading up to 5 wt % shifts the corrosion potential (Ecorr) on steel from −588 mV to −356 mV vs. SCE, reduces the corrosion current density (Icorr) from 1.09 µA/cm2 to 0.035 µA/cm2, and raises the impedance modulus at 0.01 Hz from 67 kΩ to 3794 kΩ. When structured with the hierarchical feather topography, the PMC coating (Bio-PA-MX-5) further advances the Ecorr to +103.6 mV, lowers the Icorr to 7.22 × 10−4 µA/cm2, and boosts the impedance to 96,875 kΩ. Compared to neat coatings without biomimetic structuring, those with engineered biomimetic surfaces showed significantly improved corrosion protection performance. These enhancements arise from three synergistic mechanisms: (i) polyaniline’s redox catalysis accelerates the formation of a dense passive oxide layer; (ii) MXene nanosheets create a tortuous gas barrier that cuts the oxygen permeability from 11.3 Barrer to 0.9 Barrer; and (iii) the biomimetic surface traps air pockets, raising the water contact angle from 87° to 135°. This integrated approach delivers one of the highest combined corrosion potentials and impedance values reported for thin-film coatings, pointing to a general strategy for durable steel protection. Full article
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23 pages, 3542 KiB  
Article
An Intuitive and Efficient Teleoperation Human–Robot Interface Based on a Wearable Myoelectric Armband
by Long Wang, Zhangyi Chen, Songyuan Han, Yao Luo, Xiaoling Li and Yang Liu
Biomimetics 2025, 10(7), 464; https://doi.org/10.3390/biomimetics10070464 - 15 Jul 2025
Viewed by 404
Abstract
Although artificial intelligence technologies have significantly enhanced autonomous robots’ capabilities in perception, decision-making, and planning, their autonomy may still fail when faced with complex, dynamic, or unpredictable environments. Therefore, it is critical to enable users to take over robot control in real-time and [...] Read more.
Although artificial intelligence technologies have significantly enhanced autonomous robots’ capabilities in perception, decision-making, and planning, their autonomy may still fail when faced with complex, dynamic, or unpredictable environments. Therefore, it is critical to enable users to take over robot control in real-time and efficiently through teleoperation. The lightweight, wearable myoelectric armband, due to its portability and environmental robustness, provides a natural human–robot gesture interaction interface. However, current myoelectric teleoperation gesture control faces two major challenges: (1) poor intuitiveness due to visual-motor misalignment; and (2) low efficiency from discrete, single-degree-of-freedom control modes. To address these challenges, this study proposes an integrated myoelectric teleoperation interface. The interface integrates the following: (1) a novel hybrid reference frame aimed at effectively mitigating visual-motor misalignment; and (2) a finite state machine (FSM)-based control logic designed to enhance control efficiency and smoothness. Four experimental tasks were designed using different end-effectors (gripper/dexterous hand) and camera viewpoints (front/side view). Compared to benchmark methods, the proposed interface demonstrates significant advantages in task completion time, movement path efficiency, and subjective workload. This work demonstrates the potential of the proposed interface to significantly advance the practical application of wearable myoelectric sensors in human–robot interaction. Full article
(This article belongs to the Special Issue Intelligent Human–Robot Interaction: 4th Edition)
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16 pages, 1648 KiB  
Article
Biomimetic Stator Vane Design for Radial Turbines in Waste Heat Recovery Applications
by Fuhaid Alshammari, Ibrahim Alatawi and Muapper Alhadri
Biomimetics 2025, 10(7), 463; https://doi.org/10.3390/biomimetics10070463 - 15 Jul 2025
Viewed by 357
Abstract
Organic Rankine Cycle (ORC) systems are widely used for converting low-temperature waste heat into useful power, but their overall efficiency depends heavily on the turbine’s performance, particularly the stator vane design in radial turbines. This study introduces a biomimetic approach to turbine design [...] Read more.
Organic Rankine Cycle (ORC) systems are widely used for converting low-temperature waste heat into useful power, but their overall efficiency depends heavily on the turbine’s performance, particularly the stator vane design in radial turbines. This study introduces a biomimetic approach to turbine design by implementing cambered stator vanes inspired by bird feather geometry. These specially shaped vanes are added to a radial inflow turbine and compared to a traditional design that uses straight (symmetric) vanes. The new cambered design helps guide the airflow more effectively, leading to higher tangential speeds and better energy transfer. Simulations show that this design increases the turbine’s power output from 388.6 kW to 394.87 kW and improves the system’s overall efficiency from 8.78% to 10.12%. A detailed study of different camber levels found that moderate curvatures (around 8–12%) gave the best results. Overall, this study demonstrates that implementing biomimetic cambered stator vanes in radial turbines can significantly enhance turbine performance and improve cycle-level efficiency in ORC systems for waste heat recovery. Full article
(This article belongs to the Special Issue Nature-Inspired Science and Engineering for Sustainable Future)
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22 pages, 5271 KiB  
Article
Impact of Biomimetic Fin on Pitching Characteristics of a Hydrofoil
by Faraz Ikram, Muhammad Yamin Younis, Bilal Akbar Chuddher, Usman Latif, Haroon Mushtaq, Kamran Afzal, Muhammad Asif Awan, Asad Ijaz and Noman Bashir
Biomimetics 2025, 10(7), 462; https://doi.org/10.3390/biomimetics10070462 - 15 Jul 2025
Viewed by 468
Abstract
Biomimetic design for engineering applications may suggest the optimal performance of engineering devices. In this work the passive/pure pitching characteristics of a hydrofoil are investigated experimentally with and without a pair of biomimetic fin strips placed symmetrically on the two sides of the [...] Read more.
Biomimetic design for engineering applications may suggest the optimal performance of engineering devices. In this work the passive/pure pitching characteristics of a hydrofoil are investigated experimentally with and without a pair of biomimetic fin strips placed symmetrically on the two sides of the foil leading edge. The work is performed in a recirculating water channel at low Reynolds numbers (Re) with a range of 1300 ≤ Re ≤ 3200. Using high-speed videography and Particle Image Velocimetry (PIV), the pitching characteristics and wakes are visualized. Passive pitching characteristics, i.e., the pitching amplitude and pitching frequency of the hydrofoils, are investigated based on their trailing edge movement. Significant improvement in both pitching frequency and amplitudes are observed for the foil with fin strips compared to the baseline simple foil. Comparing the pitching characteristics of the two foils, it is observed that the hydrofoil with biomimetic fin strips exhibits 25% and 21% higher pitching amplitude and pitching frequency, respectively, compared to that of the baseline at comparable Reynolds numbers. The initiation of pitching for the finned foil is also observed at comparatively low Reynolds numbers. The wake is also studied using time mean and fluctuating velocity profiles obtained using PIV. Full article
(This article belongs to the Section Locomotion and Bioinspired Robotics)
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14 pages, 5881 KiB  
Communication
The Effects of Turbulent Biological Tissue on Adjustable Anomalous Vortex Laser Beam
by Yiqun Zhang, Wu Wang, Xiaokun Ding, Liyu Sun, Zhenyang Qian, Huilin Jiang, Yansong Song and Runwei Ding
Biomimetics 2025, 10(7), 461; https://doi.org/10.3390/biomimetics10070461 - 14 Jul 2025
Viewed by 285
Abstract
In this work, we present a new partially coherent adjustable anomalous vortex laser beam (PCAAVLB) and introduce it into turbulent biological tissue. The equation of such PCAAVLB in turbulent biological tissue is obtained. By numerical analysis, the evolution of the intensity of such [...] Read more.
In this work, we present a new partially coherent adjustable anomalous vortex laser beam (PCAAVLB) and introduce it into turbulent biological tissue. The equation of such PCAAVLB in turbulent biological tissue is obtained. By numerical analysis, the evolution of the intensity of such PCAAVLB in turbulent biological tissue is analyzed. It is found that the PCAAVLB in biological tissue can lose its ring shape and become a Gaussian beam, and a PCAAVLB with smaller topological charge M or coherence length σ will evolve into a Gaussian profile faster. The PCAAVLB in turbulent biological tissue with a smaller small-length-scale factor l0 or larger fractal dimension D will evolve into a Gaussian profile faster and have a larger intensity as z increases. The results may have potential applications in sensing under biological tissue environments and laser imaging in biology. Full article
(This article belongs to the Special Issue Advanced Biologically Inspired Vision and Its Application)
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39 pages, 5277 KiB  
Review
AI-Driven Control Strategies for Biomimetic Robotics: Trends, Challenges, and Future Directions
by Hoejin Jung, Soyoon Park, Sunghoon Joe, Sangyoon Woo, Wonchil Choi and Wongyu Bae
Biomimetics 2025, 10(7), 460; https://doi.org/10.3390/biomimetics10070460 - 14 Jul 2025
Viewed by 1073
Abstract
Biomimetic robotics aims to replicate biological movement, perception, and cognition, drawing inspiration from nature to develop robots with enhanced adaptability, flexibility, and intelligence. The integration of artificial intelligence has significantly advanced the control mechanisms of biomimetic robots, enabling real-time learning, optimization, and adaptive [...] Read more.
Biomimetic robotics aims to replicate biological movement, perception, and cognition, drawing inspiration from nature to develop robots with enhanced adaptability, flexibility, and intelligence. The integration of artificial intelligence has significantly advanced the control mechanisms of biomimetic robots, enabling real-time learning, optimization, and adaptive decision-making. This review systematically examines AI-driven control strategies for biomimetic robots, categorizing recent advancements and methodologies. First, we review key aspects of biomimetic robotics, including locomotion, sensory perception, and cognitive learning inspired by biological systems. Next, we explore various AI techniques—such as machine learning, deep learning, and reinforcement learning—that enhance biomimetic robot control. Furthermore, we analyze existing AI-based control methods applied to different types of biomimetic robots, highlighting their effectiveness, algorithmic approaches, and performance compared to traditional control techniques. By synthesizing the latest research, this review provides a comprehensive overview of AI-driven biomimetic robot control and identifies key challenges and future research directions. Our findings offer valuable insights into the evolving role of AI in enhancing biomimetic robotics, paving the way for more intelligent, adaptive, and efficient robotic systems. Full article
(This article belongs to the Special Issue Recent Advances in Bioinspired Robot and Intelligent Systems)
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16 pages, 7005 KiB  
Article
A Biomimetic Microchannel Heat Sink for Enhanced Thermal Performance in Chip Cooling
by Kaichen Wang, Yan Shi, Junjie Chen and Yuchi Dai
Biomimetics 2025, 10(7), 459; https://doi.org/10.3390/biomimetics10070459 - 12 Jul 2025
Viewed by 646
Abstract
The rapid advancement of artificial intelligence continuously increases the demand for high computing power, leading to substantial rises in chip power consumption and heat generation. As a result, efficient thermal management has become essential. Inspired by the placoid scales on shark skin, we [...] Read more.
The rapid advancement of artificial intelligence continuously increases the demand for high computing power, leading to substantial rises in chip power consumption and heat generation. As a result, efficient thermal management has become essential. Inspired by the placoid scales on shark skin, we designed a bionic microchannel heat sink by introducing biomimetic structures on the inner channel surfaces to enhance heat dissipation. Numerical simulations are performed to investigate thermal behavior under different structural configurations. The results show that the arrangement, number, and inclination angle of the placoid structures significantly influence heat transfer by modifying flow patterns, enlarging the heat transfer area, and altering the thermal boundary layer. Notably, at a flow velocity of 2 m/s, the cooling performance differs significantly between inclination angles of 0° and 17°. Moreover, the influence of different quantities of placoid structures shows a consistent trend across various flow rates. These findings demonstrate that bionic surface structures can effectively improve the thermal performance of microchannel heat sinks, offering a promising strategy for high-performance chip cooling. Full article
(This article belongs to the Special Issue Biological and Bioinspired Materials and Structures: 2nd Edition)
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37 pages, 6555 KiB  
Review
Biomimetic Lattice Structures Design and Manufacturing for High Stress, Deformation, and Energy Absorption Performance
by Víctor Tuninetti, Sunny Narayan, Ignacio Ríos, Brahim Menacer, Rodrigo Valle, Moaz Al-lehaibi, Muhammad Usman Kaisan, Joseph Samuel, Angelo Oñate, Gonzalo Pincheira, Anne Mertens, Laurent Duchêne and César Garrido
Biomimetics 2025, 10(7), 458; https://doi.org/10.3390/biomimetics10070458 - 12 Jul 2025
Viewed by 1876
Abstract
Lattice structures emerged as a revolutionary class of materials with significant applications in aerospace, biomedical engineering, and mechanical design due to their exceptional strength-to-weight ratio, energy absorption properties, and structural efficiency. This review systematically examines recent advancements in lattice structures, with a focus [...] Read more.
Lattice structures emerged as a revolutionary class of materials with significant applications in aerospace, biomedical engineering, and mechanical design due to their exceptional strength-to-weight ratio, energy absorption properties, and structural efficiency. This review systematically examines recent advancements in lattice structures, with a focus on their classification, mechanical behavior, and optimization methodologies. Stress distribution, deformation capacity, energy absorption, and computational modeling challenges are critically analyzed, highlighting the impact of manufacturing defects on structural integrity. The review explores the latest progress in hybrid additive manufacturing, hierarchical lattice structures, modeling and simulation, and smart adaptive materials, emphasizing their potential for self-healing and real-time monitoring applications. Furthermore, key research gaps are identified, including the need for improved predictive computational models using artificial intelligence, scalable manufacturing techniques, and multi-functional lattice systems integrating thermal, acoustic, and impact resistance properties. Future directions emphasize cost-effective material development, sustainability considerations, and enhanced experimental validation across multiple length scales. This work provides a comprehensive foundation for future research aimed at optimizing biomimetic lattice structures for enhanced mechanical performance, scalability, and industrial applicability. Full article
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17 pages, 1714 KiB  
Review
Tissue-Engineered Tracheal Reconstruction
by Se Hyun Yeou and Yoo Seob Shin
Biomimetics 2025, 10(7), 457; https://doi.org/10.3390/biomimetics10070457 - 11 Jul 2025
Viewed by 851
Abstract
Tracheal reconstruction remains a formidable clinical challenge, particularly for long-segment defects that are not amenable to standard surgical resection or primary anastomosis. Tissue engineering has emerged as a promising strategy for restoring the tracheal structure and function through the integration of biomaterials, stem [...] Read more.
Tracheal reconstruction remains a formidable clinical challenge, particularly for long-segment defects that are not amenable to standard surgical resection or primary anastomosis. Tissue engineering has emerged as a promising strategy for restoring the tracheal structure and function through the integration of biomaterials, stem cells, and bioactive molecules. This review provides a comprehensive overview of recent advances in tissue-engineered tracheal grafts, particularly in scaffold design, cellular sources, fabrication technologies, and early clinical experience. Innovations in biomaterial science, three-dimensional printing, and scaffold-free fabrication approaches have broadened the prospects for patient-specific airway reconstruction. However, persistent challenges, including incomplete epithelial regeneration and mechanical instability, have hindered its clinical translation. Future efforts should focus on the design of modular biomimetic scaffolds, the enhancement of immunomodulatory strategies, and preclinical validation using robust large animal models. Sustained interdisciplinary collaboration among surgical, engineering, and biological fields is crucial for advancing tissue-engineered tracheal grafts for routine clinical applications. Within this context, biomimetic approaches, including three-dimensional bioprinting, hybrid materials, and scaffold-free constructs, are gaining prominence as strategies to replicate the trachea’s native architecture and improve graft integration. Full article
(This article belongs to the Special Issue Biomimetic Application on Applied Bioengineering)
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19 pages, 3024 KiB  
Article
Feedback-Driven Dynamical Model for Axonal Extension on Parallel Micropatterns
by Kyle Cheng, Udathari Kumarasinghe and Cristian Staii
Biomimetics 2025, 10(7), 456; https://doi.org/10.3390/biomimetics10070456 - 11 Jul 2025
Viewed by 417
Abstract
Despite significant advances in understanding neuronal development, a fully quantitative framework that integrates intracellular mechanisms with environmental cues during axonal growth remains incomplete. Here, we present a unified biophysical model that captures key mechanochemical processes governing axonal extension on micropatterned substrates. In these [...] Read more.
Despite significant advances in understanding neuronal development, a fully quantitative framework that integrates intracellular mechanisms with environmental cues during axonal growth remains incomplete. Here, we present a unified biophysical model that captures key mechanochemical processes governing axonal extension on micropatterned substrates. In these environments, axons preferentially align with the pattern direction, form bundles, and advance at constant speed. The model integrates four core components: (i) actin–adhesion traction coupling, (ii) lateral inhibition between neighboring axons, (iii) tubulin transport from soma to growth cone, and (iv) orientation dynamics guided by substrate anisotropy. Dynamical systems analysis reveals that a saddle–node bifurcation in the actin adhesion subsystem drives a transition to a high-traction motile state, while traction feedback shifts a pitchfork bifurcation in the signaling loop, promoting symmetry breaking and robust alignment. An exact linear solution in the tubulin transport subsystem functions as a built-in speed regulator, ensuring stable elongation rates. Simulations using experimentally inferred parameters accurately reproduce elongation speed, alignment variance, and bundle spacing. The model provides explicit design rules for enhancing axonal alignment through modulation of substrate stiffness and adhesion dynamics. By identifying key control parameters, this work enables rational design of biomaterials for neural repair and engineered tissue systems. Full article
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15 pages, 33163 KiB  
Article
An Optimised Spider-Inspired Soft Actuator for Extraterrestrial Exploration
by Jonah Mack, Maks Gepner, Francesco Giorgio-Serchi and Adam A. Stokes
Biomimetics 2025, 10(7), 455; https://doi.org/10.3390/biomimetics10070455 - 11 Jul 2025
Viewed by 565
Abstract
Extraterrestrial exploration presents unique challenges for robotic systems, as traditional rigid rovers face limitations in stowage volume, traction on unpredictable terrain, and susceptibility to damage. Soft robotics offers promising solutions through bio-inspired designs that can mimic natural locomotion mechanisms. Here, we present an [...] Read more.
Extraterrestrial exploration presents unique challenges for robotic systems, as traditional rigid rovers face limitations in stowage volume, traction on unpredictable terrain, and susceptibility to damage. Soft robotics offers promising solutions through bio-inspired designs that can mimic natural locomotion mechanisms. Here, we present an optimised, spider-inspired soft jumping robot for extraterrestrial exploration that addresses key challenges in soft robotics: actuation efficiency, controllability, and deployment. Drawing inspiration from spider physiology—particularly their hydraulic extension mechanism—we develop a lightweight limb capable of multi-modal behaviour with significantly reduced energy requirements. Our 3D-printed soft actuator leverages pressure-driven collapse for efficient retraction and pressure-enhanced rapid extension, achieving a power-to-weight ratio of 249 W/kg. The integration of a non-backdriveable clutch mechanism enables the system to hold positions with zero energy expenditure—a critical feature for space applications. Experimental characterisation and a subsequent optimisation methodology across various materials, dimensions, and pressures reveal that the robot can achieve jumping heights of up to 1.86 times its body length. The collapsible nature of the soft limb enables efficient stowage during spacecraft transit, while the integrated pumping system facilitates self-deployment upon arrival. This work demonstrates how biologically inspired design principles can be effectively applied to develop versatile robotic systems optimised for the unique constraints of extraterrestrial exploration. Full article
(This article belongs to the Special Issue Bio-Inspired and Biomimetic Intelligence in Robotics: 2nd Edition)
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49 pages, 5383 KiB  
Article
Chaotic Mountain Gazelle Optimizer Improved by Multiple Oppositional-Based Learning Variants for Theoretical Thermal Design Optimization of Heat Exchangers Using Nanofluids
by Oguz Emrah Turgut, Mustafa Asker, Hayrullah Bilgeran Yesiloz, Hadi Genceli and Mohammad AL-Rawi
Biomimetics 2025, 10(7), 454; https://doi.org/10.3390/biomimetics10070454 - 10 Jul 2025
Viewed by 395
Abstract
This theoretical research study proposes a novel hybrid algorithm that integrates an improved quasi-dynamical oppositional learning mutation scheme into the Mountain Gazelle Optimization method, augmented with chaotic sequences, for the thermal and economical design of a shell-and-tube heat exchanger operating with nanofluids. The [...] Read more.
This theoretical research study proposes a novel hybrid algorithm that integrates an improved quasi-dynamical oppositional learning mutation scheme into the Mountain Gazelle Optimization method, augmented with chaotic sequences, for the thermal and economical design of a shell-and-tube heat exchanger operating with nanofluids. The Mountain Gazelle Optimizer is a recently developed metaheuristic algorithm that simulates the foraging behaviors of Mountain Gazelles. However, it suffers from premature convergence due to an imbalance between its exploration and exploitation mechanisms. A two-step improvement procedure is implemented to enhance the overall search efficiency of the original algorithm. The first step concerns substituting uniformly random numbers with chaotic numbers to refine the solution quality to better standards. The second step is to develop a novel manipulation equation that integrates different variants of quasi-dynamic oppositional learning search schemes, guided by a novel intelligently devised adaptive switch mechanism. The efficiency of the proposed algorithm is evaluated using the challenging benchmark functions from various CEC competitions. Finally, the thermo-economic design of a shell-and-tube heat exchanger operated with different nanoparticles is solved by the proposed improved metaheuristic algorithm to obtain the optimal design configuration. The predictive results indicate that using water + SiO2 instead of ordinary water as the refrigerant on the tube side of the heat exchanger reduces the total cost by 16.3%, offering the most cost-effective design among the configurations compared. These findings align with the demonstration of how biologically inspired metaheuristic algorithms can be successfully applied to engineering design. Full article
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24 pages, 1908 KiB  
Perspective
Biomimetic Additive Manufacturing: Engineering Complexity Inspired by Nature’s Simplicity
by Antreas Kantaros, Theodore Ganetsos, Evangelos Pallis and Michail Papoutsidakis
Biomimetics 2025, 10(7), 453; https://doi.org/10.3390/biomimetics10070453 - 10 Jul 2025
Viewed by 889
Abstract
Nature’s principles offer design references for additive manufacturing (AM), enabling structures that achieve remarkable efficiency through hierarchical organization rather than material excess. This perspective article proposes a framework for integrating biomimetic principles into AM beyond morphological mimicry, focusing on functional adaptation and sustainability. [...] Read more.
Nature’s principles offer design references for additive manufacturing (AM), enabling structures that achieve remarkable efficiency through hierarchical organization rather than material excess. This perspective article proposes a framework for integrating biomimetic principles into AM beyond morphological mimicry, focusing on functional adaptation and sustainability. By emulating biological systems like nacre, spider silk, and bone, AM utilizes traditional geometric replication to embed multifunctionality, responsiveness, and resource efficiency. Recent advances in the fields of 4D printing, soft robotics, and self-morphing systems demonstrate how time-dependent behaviors and environmental adaptability can be engineered through bioinspired material architectures. However, challenges in scalable fabrication, dynamic material programming, and true functional emulation (beyond morphological mimicry) necessitate interdisciplinary collaboration. In this context, the synthesis of biological intelligence with AM technologies offers sustainable, high-performance solutions for aerospace, biomedical, and smart infrastructure applications, once challenges related to material innovation and standardization are overcome. Full article
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25 pages, 6826 KiB  
Article
Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements
by Shuangling Ma, Zijie Situ, Xiaobo Peng, Zhangyang Li and Ying Huang
Biomimetics 2025, 10(7), 452; https://doi.org/10.3390/biomimetics10070452 - 9 Jul 2025
Viewed by 459
Abstract
Brain–Computer Interfaces (BCIs) enable direct communication between the brain and external devices by decoding motor intentions from EEG signals. However, the existing multi-class classification methods for motor imagery EEG (MI-EEG) signals are hindered by low signal quality and limited accuracy, restricting their practical [...] Read more.
Brain–Computer Interfaces (BCIs) enable direct communication between the brain and external devices by decoding motor intentions from EEG signals. However, the existing multi-class classification methods for motor imagery EEG (MI-EEG) signals are hindered by low signal quality and limited accuracy, restricting their practical application. This study focuses on rehabilitation training scenarios, aiming to capture the motor intentions of patients with partial or complete motor impairments (such as stroke survivors) and provide feedforward control commands for exoskeletons. This study developed an EEG acquisition protocol specifically for use with lower-limb rehabilitation motor imagery (MI). It systematically explored preprocessing techniques, feature extraction strategies, and multi-classification algorithms for multi-task MI-EEG signals. A novel 3D EEG convolutional neural network (3D EEG-CNN) that integrates time/frequency features is proposed. Evaluations on a self-collected dataset demonstrated that the proposed model achieved a peak classification accuracy of 66.32%, substantially outperforming conventional approaches and demonstrating notable progress in the multi-class classification of lower-limb motor imagery tasks. Full article
(This article belongs to the Special Issue Advances in Brain–Computer Interfaces 2025)
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15 pages, 1662 KiB  
Article
YOLO-HVS: Infrared Small Target Detection Inspired by the Human Visual System
by Xiaoge Wang, Yunlong Sheng, Qun Hao, Haiyuan Hou and Suzhen Nie
Biomimetics 2025, 10(7), 451; https://doi.org/10.3390/biomimetics10070451 - 8 Jul 2025
Viewed by 535
Abstract
To address challenges of background interference and limited multi-scale feature extraction in infrared small target detection, this paper proposes a YOLO-HVS detection algorithm inspired by the human visual system. Based on YOLOv8, we design a multi-scale spatially enhanced attention module (MultiSEAM) using multi-branch [...] Read more.
To address challenges of background interference and limited multi-scale feature extraction in infrared small target detection, this paper proposes a YOLO-HVS detection algorithm inspired by the human visual system. Based on YOLOv8, we design a multi-scale spatially enhanced attention module (MultiSEAM) using multi-branch depth-separable convolution to suppress background noise and enhance occluded targets, integrating local details and global context. Meanwhile, the C2f_DWR (dilation-wise residual) module with regional-semantic dual residual structure is designed to significantly improve the efficiency of capturing multi-scale contextual information by expanding convolution and two-step feature extraction mechanism. We construct the DroneRoadVehicles dataset containing 1028 infrared images captured at 70–300 m, covering complex occlusion and multi-scale targets. Experiments show that YOLO-HVS achieves mAP50 of 83.4% and 97.8% on the public dataset DroneVehicle and the self-built dataset, respectively, which is an improvement of 1.1% and 0.7% over the baseline YOLOv8, and the number of model parameters only increases by 2.3 M, and the increase of GFLOPs is controlled at 0.1 G. The experimental results demonstrate that the proposed approach exhibits enhanced robustness in detecting targets under severe occlusion and low SNR conditions, while enabling efficient real-time infrared small target detection. Full article
(This article belongs to the Special Issue Advanced Biologically Inspired Vision and Its Application)
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16 pages, 18636 KiB  
Article
Design of a Modular Wall-Climbing Robot with Multi-Plane Transition and Cleaning Capabilities
by Boyu Wang, Weijian Zhang, Jianghan Luo and Qingsong Xu
Biomimetics 2025, 10(7), 450; https://doi.org/10.3390/biomimetics10070450 - 8 Jul 2025
Viewed by 573
Abstract
This paper presents the design and development of a new modular wall-climbing robot—Modular Wall Climbing-1 (MC-1)—for solving the problem of autonomous wall switching observed in wall-climbing robots. Each modular robot is capable of independently adhering to vertical surfaces and maneuvering, making it a [...] Read more.
This paper presents the design and development of a new modular wall-climbing robot—Modular Wall Climbing-1 (MC-1)—for solving the problem of autonomous wall switching observed in wall-climbing robots. Each modular robot is capable of independently adhering to vertical surfaces and maneuvering, making it a fully autonomous robotic system. Multiple modules of MC-1 are connected by an electromagnet-based magnetic attachment method, and wall transitions are achieved using a servo motor mechanism. Moreover, an ultrasonic sensor is employed to measure the unknown wall-inclination angle. Mechanical analysis is conducted for MC-1 at rest individually and in combination to determine the required suction force. Experimental investigations are performed to assess the robot’s crawling ability, loading capacity, and wall-transition performance. The results demonstrate that the MC-1 robot is capable of multi-angle wall transitions for executing multiple tasks. It provides a new approach for wall-climbing robots to collaborate during wall transitions through a quick attachment-and-disassembly device and an efficient wall detection method. Full article
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21 pages, 5444 KiB  
Article
Diagnosis of Schizophrenia Using Feature Extraction from EEG Signals Based on Markov Transition Fields and Deep Learning
by Alka Jalan, Deepti Mishra, Marisha and Manjari Gupta
Biomimetics 2025, 10(7), 449; https://doi.org/10.3390/biomimetics10070449 - 7 Jul 2025
Viewed by 737
Abstract
Diagnosing schizophrenia using Electroencephalograph (EEG) signals is a challenging task due to the subtle and overlapping differences between patients and healthy individuals. To overcome this difficulty, deep learning has shown strong potential, especially given its success in image recognition tasks. In many studies, [...] Read more.
Diagnosing schizophrenia using Electroencephalograph (EEG) signals is a challenging task due to the subtle and overlapping differences between patients and healthy individuals. To overcome this difficulty, deep learning has shown strong potential, especially given its success in image recognition tasks. In many studies, one-dimensional EEG signals are transformed into two-dimensional representations to allow for image-based analysis. In this work, we have used the Markov Transition Field for converting EEG signals into two-dimensional images, capturing both the temporal patterns and statistical dynamics of the data. EEG signals are continuous time-series recordings from the brain, where the current state is often influenced by the immediately preceding state. This characteristic makes MTF particularly suitable for representing such data. After the transformation, a pre-trained VGG-16 model is employed to extract meaningful features from the images. The extracted features are then passed through two separate classification pipelines. The first uses a traditional machine learning model, Support Vector Machine, while the second follows a deep learning approach involving an autoencoder for feature selection and a neural network for final classification. The experiments were conducted using EEG data from the open-access Schizophrenia EEG database provided by MV Lomonosov Moscow State University. The proposed method achieved a highest classification accuracy of 98.51 percent and a recall of 100 percent across all folds using the deep learning pipeline. The Support Vector Machine pipeline also showed strong performance with a best accuracy of 96.28 percent and a recall of 97.89 percent. The proposed deep learning model represents a biomimetic approach to pattern recognition and decision-making. Full article
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