Biomimetics, Volume 10, Issue 6 (June 2025) – 50 articles

In this study, the power system of a bionic robotic fish has been significantly simplified, resulting in a reduced volume and enhanced flexibility of both the structure and movement. To comprehensively understand the dynamics, a fluid–structure interaction (FSI) analysis was conducted, considering the intricate interplay between the mollusk’s structure and the surrounding fluid. This analysis took into account the dissipation due to fluid viscosity and the influence of the wake performance around the mollusk. The study examined the relationships between the driving frequency of the input signal and various parameters such as fluid pressure, propulsion force, and propulsion displacement of the soft robot fish head. With the robot fish’s head fixed, the amplitude of propulsion motion and propulsion force were measured. The simulation results closely matched the experimental findings, indicating their potential to predict the propulsion characteristics of the soft robot fish in fluid environments and further improve its performance. Full article

Although existing motion transfer methods for bionic robot arms are based on kinematic equivalence or simplified dynamic models, they frequently fail to tackle dynamic compliance and real-time adaptability in complex human-like motions. To address this shortcoming, this study presents a motion transfer method from the human arm to a bionic robot arm based on the hybrid PSO-RF (Particle Swarm Optimization-Random Forest) algorithm to improve joint space mapping accuracy and dynamic compliance. Initially, a high-precision optical motion capture (Mocap) system was utilized to record human arm trajectories, and Kalman filtering and a Rauch–Tung–Striebel (RTS) smoother were applied to reduce noise and phase lag. Subsequently, the joint angles of the human arm were computed through geometric vector analysis. Although geometric vector analysis offers an initial estimation of joint angles, its deterministic framework is subject to error accumulation caused by the occlusion of reflective markers and kinematic singularities. To surmount this limitation, this study designed five action sequences for the establishment of the training database for the PSO-RF model to predict joint angles when performing different actions. Ultimately, an experimental platform was built to validate the motion transfer method, and the experimental verification showed that the system attained high prediction accuracy (R² = 0.932 for the elbow joint angle) and real-time performance with a latency of 0.1097 s. This paper promotes compliant human–robot interaction by dealing with joint-level dynamic transfer challenges, presenting a framework for applications in intelligent manufacturing and rehabilitation robotics. Full article

Integrating bioinspired design and additive manufacturing into engineering education fosters innovation to meet the growing demand for accessible, personalized assistive technologies. This paper presents the outcomes of an international course, “3D Prosthetics and Orthotics”, offered to undergraduate students in the Biomimetic program at Westfälische Hochschule (Germany), in collaboration with the 3D Orthotics and Prosthetics Laboratory at the Federal University of São Paulo—UNIFESP (Brazil). The course combined theoretical and hands-on modules covering digital modeling (CAD), simulation (CAE), and fabrication (CAM), enabling students to develop bioinspired assistive devices through a Project-based learning approach. Working in interdisciplinary teams, students addressed real-world rehabilitation challenges by translating biological mechanisms into engineered solutions using additive manufacturing. Resulting prototypes included a hand prosthesis based on the Fin Ray effect, a modular finger prosthesis inspired by tendon–muscle antagonism, and a cervical orthosis designed based on stingray morphology. Each device was digitally modeled, mechanically analyzed, and physically fabricated using open-source and low-cost methods. This initiative illustrates how biomimetic mechanisms and design can be integrated into education to generate functional outcomes and socially impactful health technologies. Grounded in the Mao3D open-source methodology, this experience demonstrates the value of combining nature-inspired principles, digital fabrication, Design Thinking, and international collaboration to advance inclusive, low-cost innovations in assistive technology. Full article

To improve the traction performance of mobile mechanisms on soft ground, such as paddy fields, tidal flats, and swamps, a mallard (Anas platyrhynchos) foot was adopted as a bionic prototype to explore the influence and contribution of the plantar morphology of the toes and webbing on the anti-subsidence function during its locomotion on wet and soft substrates and to apply this to the bionic design of high-traction wheel grousers. A handheld three-dimensional laser scanner was used to scan the main locomotion postures of a mallard foot during ground contact, and the Geomagic Studio software was utilized to repair the scanned model. As a result, the main three-dimensional geometric models of a mallard foot during the process of touching the ground were obtained. The plantar morphology of a mallard foot was divided into three typical parts: the plantar irregular edge curve, the lateral webbing surface, and the medial webbing surface. The main morphological feature curves/surfaces were extracted through computer-aided design software for the fitting and construction of a mathematical model to obtain the fitting equations of the three typical parts, and the mathematical model construction of the plantar irregular morphology of the mallard foot was completed. In order to verify the sand-fixing and flow-limiting characteristics of this morphological feature, based on the discrete element method (DEM), the numerical simulation of the interaction between the plantar surface of the mallard foot and sand particles was carried out. The simulation results show that during the process of the mallard foot penetration into the loose medium, the lateral and medial webbing surfaces cause the particles under the foot to mainly move downward, effectively preventing the particles from spreading around and significantly enhancing the solidification effect of the particles under the sole. Based on the principle and technology of engineering bionics, the plantar morphology and movement attitude characteristics of the mallard were extracted, and the characteristics of concave middle and edge bulge were applied to the wheel grouser design of paddy field wheels. Two types of bionic wheel grousers with different curved surfaces were designed and compared with the traditional wheel grousers of the paddy field wheel. Through pressure-bearing simulation and experiments, the resistance of different wheel grousers during the process of penetrating into sand particles was compared, and the macro–micro behaviors of particle disturbance during the pressure-bearing process were analyzed. The results show that a bionic wheel grouser with unique curved surfaces can well encapsulate sand particles at the bottom of the wheel grouser, and it also has a greater penetration resistance, which plays a crucial role in improving the traction performance of the paddy field wheel and reducing the disturbance to the surrounding sand particles. This paper realizes the transformation from the biological model to the mathematical model of the plantar morphology of the mallard foot and applies it to the bionic design of the wheel grousers of the paddy field wheels, providing a new solution for improving the traction performance of mobile mechanisms on soft ground. Full article

Snake-like robots face critical challenges in energy-efficient locomotion and smooth gait transitions, limiting their real-world deployment. This study introduces a bio-inspired compliant joint design integrated with a hierarchical neural oscillator network and an energy-optimized control framework. The joint mimics biological skeletal flexibility using specialized wheeled mechanisms and adaptive parallel linkages, while the control network enables adaptive gait generation and seamless transitions through a phase-smoothing algorithm. Critically, this work adopts a synergistic design philosophy where mechanical components and control parameters are co-optimized through shared dynamic modeling. The proposed predictive control strategy optimizes locomotion speed while minimizing energy consumption. Experimental simulations demonstrate that the method achieves an 18% higher average forward speed (0.0563 m/s vs. 0.0478 m/s) with 7% lower energy use (0.1952 J vs. 0.2107 J) compared to conventional approaches. Physical prototype testing confirms these improvements under real-world conditions, showing a 12.9% speed increase (0.0531 m/s vs. 0.0470 m/s) and 7.3% energy reduction (0.2147 J vs. 0.2317 J). By unifying mechanical flexibility and adaptive control parameter tuning, this work bridges dynamic performance and energy efficiency, offering a robust solution for unstructured environments. Full article

An enhanced snow geese algorithm (ESGA) is proposed to address the problems of the weakened population diversity and unbalanced search tendencies encountered by the snow geese algorithm (SGA) in the search process. First, an adaptive switching strategy is used to dynamically select the search strategy to balance the exploitation and exploration capabilities. Second, a dominant group guidance strategy is introduced to improve the population quality. Finally, a dominant stochastic difference search strategy is designed to enrich the population diversity and help it escape from the local optimum by co-directing effects in multiple directions. Ablation experiments were performed on the CEC2017 test set to illustrate the improvement mechanism and the degree of compatibility of their improved strategies. The proposed ESGA with a highly cited algorithm and the powerful improved algorithm are compared on the CEC2022 test suite, and the experimental results confirm that the ESGA outperforms the compared algorithms. Finally, the ability of the ESGA to solve complex problems is further highlighted by solving the robot path planning problem. Full article

Sericin, a natural glycoprotein constituting 20–30% of the silk cocoon, has emerged as a promising biomaterial due to its excellent biological properties, including biocompatibility, antioxidant properties and potential applications in regenerative medicine. The quality and the features of sericin are strongly dependent on the extraction and purification methods, which can employ mild conditions to preserve the molecular integrity of the protein or recovery techniques from waste streams produced during the industrial degumming processes. The silk industry prioritizes fiber yield over protein preservation, so often harsh alkaline conditions at high temperatures are adopted. These divergent approaches result in fundamentally different products with distinct molecular characteristics and functional capabilities. This review comprehensively examines the current technological approaches for sericin extraction techniques and for its recovery from textile industry waste, focusing on how these aspects affect the biological properties of the protein and the potential applications. Full article

Background: Bone resorption following tooth loss poses significant challenges for dental implant success. Guided bone regeneration (GBR) techniques, particularly in vertically deficient ridges, often require complex procedures and soft tissue management. This case report introduces a modified occlusive barrier with a window, combined with tricalcium phosphate, to address these challenges. Methods: A 26-year-old female with significant bone loss in the mandibular anterior region underwent GBR using a digitally designed titanium occlusive barrier. The barrier was fabricated using CAD/CAM technology and secured with screws. A blood clot mixed with tricalcium phosphate was used to promote bone regeneration. Postoperative care included regular irrigation, de-epithelialization, and follow-up over six months. Implant placement and histological analysis were performed to evaluate outcomes. Case Presentation: The patient achieved 8.8 mm of vertical and 7.6 mm of horizontal bone regeneration. Histological analysis confirmed the presence of mature, mineralized bone, and keratinized gingiva. The implant was successfully placed, and a fixed prosthesis was restored after four months, with stable results at a three-year follow-up. Conclusion: This technique demonstrates effective bone and soft tissue regeneration in a single procedure, eliminating the need for autologous bone grafts and secondary surgeries. The use of a digitally designed occlusive barrier offers precision, reduces morbidity, and simplifies the surgical process, suggesting a promising advancement in GBR. Further studies are needed to validate these findings. Full article

The cambered deflectors in aquacultural facilities are applied to enhance hydrodynamic efficiencies or enable flow fields to be fully developed. Given the anticipated improvements with the bio-inspired profiles or tandem configurations, the hydrodynamics of cambered deflectors with the above features are investigated at $Re=1\times {10}^5$. The relationship between force coefficients and local flow behaviors for both bionic and non-bionic isolated deflectors, as well as tandem deflectors, is revealed using $k-\omega$ SST simulation. The dependencies of force coefficients on gap (G), stagger (S), and inclination angles ($\theta$) in tandem deflectors are illustrated using an updated metamodeling workflow with simulated data. It is demonstrated that the variations of force coefficients over angles of attack are related to flow physics in boundary-layer regions. The non-bionic isolated deflector with the $\theta ={10}^{\circ }$ prevails as the decent performances of $C_L$ and $\gamma$ globally, which is chosen in the following studies. Regarding tandem deflectors, $\theta$ plays a more vital role in drag coefficients ($C_D$) and lift coefficients ($C_L$), while the influence of S is not quite considerable compared to G. Aiming for cost minimizations and lift improvements, an optimized tandem case is obtained and justified with the superiorities in flow fields. This study has provided novel insights into the designs and optimizations of cambered deflectors in aquacultural engineering. Full article

This study proposes a novel effective improved whale optimization algorithm via angle penalized distance (IWOA-APD) for automatic train operation (ATO) to effectively improve the ATO quality. Specifically, aiming at the high-quality target speed curve of urban rail trains, a target speed curve multi-objective optimization model for ATO is established with energy saving, punctuality, accurate stopping, and comfort as the indexes; and the comprehensive evaluation strategy utilizing angle-penalized distance as the evaluation index is proposed to enhance the assessment’s rationality and applicability. On this basis, the IWOA-APD is proposed using strategies of non-linear decreasing convergence factor, solutions of out-of-bounds eliminating via combination of reflection and refraction, mechanisms of genetic evolution with variable probability, and elite maintenance based on fusion distance and crowding degree distance. In addition, the detailed design scheme of IWOA-APD is given. The test results show that the proposed IWOA-APD achieves significant performance improvements compared to traditional MOWOA. In the optimization scenario from Lvshun New Port Station to Tieshan Town Station of Dalian urban rail transit line No.12, the IGD value shows a remarkable 69.1% reduction, while energy consumption decreases by 12.5%. The system achieves a 64.6% improvement in punctuality and a 76.5% enhancement in parking accuracy. Additionally, comfort level improves by 15.9%. Full article

This study presents an integrated stiffness modeling and evaluation framework for an orthopedic surgical robot, aiming to enhance cutting accuracy and operational stability. A comprehensive stiffness model is developed, incorporating the stiffness of the end-effector, cutting tool, and force sensor. End-effector stiffness is computed using the virtual joint method based on the Jacobian matrix, enabling accurate analysis of stiffness distribution within the robot’s workspace. Joint stiffness is experimentally identified through laser tracker-based displacement measurements under controlled loads and calculated using a least-squares method. The results show displacement errors below 0.3 mm and joint stiffness estimation errors under 1.5%, with values more consistent and stable than those reported for typical surgical robots. Simulation studies reveal spatial variations in operational stiffness, identifying zones of low stiffness and excessive stiffness. Compared to prior studies where stiffness varied over 50%, the proposed model exhibits superior uniformity. Experimental validation confirms model fidelity, with prediction errors generally below 5%. Cutting experiments on porcine femurs demonstrate real-world applicability, achieving average stiffness prediction errors below 3%, and under 1% in key directions. The model supports stiffness-aware trajectory planning and control, reducing cutting deviation by up to 10% and improving workspace stiffness stability by 30%. This research offers a validated, high-accuracy approach to stiffness modeling for surgical robots, bridging the gap between simulation and clinical application, and providing a foundation for safer, more precise robotic orthopedic procedures. Full article

We experimentally studied the effect of a surfactant on bubble formation on a superhydrophobic surface (SHS). The bubble was created by injecting gas through an orifice on the SHS at a constant flow rate in the quasi-static regime. The surfactant, 1-pentanol, was mixed with water at concentration C ranging from 0 to 0.08 mol/L, corresponding to surface tension σ ranging from 72 to 43 mN/m. We found that as C increased, the bubble detachment volume (V_d) and maximum bubble base radius (R_d^max) decreased. For a low surfactant concentration, the static contact angle θ₀ remained nearly constant, and V_d and R_d^max decreased due to lower surface tensions, following the scaling laws R_d^max~σ^1/2 and V_d~σ^3/2. The bubble shapes at different concentrations were self-similar. The bubble height, bubble base radius, radius at the bubble apex, and neck radius all scaled with the capillary length. For high surfactant concentrations, however, θ₀ was greatly reduced, and V_d and R_d^max decreased due to the combined effects of reduced θ₀ and smaller σ. Lastly, we found that the surfactant had a negligible impact on the forces acting on the bubble, except for reducing their magnitudes, and had little effect on the dynamics of bubble pinch-off, except for reducing the time and length scales. Overall, our results provide a better understanding of bubble formation on complex surfaces in complex liquids. Full article

In this work, we propose the integration of a mechanism to enable smooth transitions between different locomotion patterns in a hexapod robot. Specifically, we utilize a spiking neural network (SNN) functioning as a Central Pattern Generator (CPG) to generate three distinct locomotion patterns, or gaits: walk, jog, and run. This network produces coordinated spike trains, mimicking those generated in the brain, which are translated into synchronized robot movements via PWM signals. Subsequently, these spike trains are compared using a similarity metric known as SPIKE-synchronization to identify the optimal point for transitioning from one gait to another. This approach aims to achieve three main objectives: first, to maintain the robot’s balance during transitions; second, to ensure that gait transitions are almost imperceptible; and third, to improve energy efficiency by reducing abrupt changes in the robot’s actuators (servomotors). To validate our proposal, we incorporated FSR sensors on the robot’s legs to detect the rigidity of the terrain it navigates. Based on the terrain’s rigidity, the robot dynamically transitions between gaits. The system was tested in real time on a physical hexapod robot across four different types of terrain. Although the method was validated exclusively on a hexapod robot, it can be extended to any legged robot. Full article

With the advancement of industrial digitization, utilizing large datasets for model training to boost performance is a pivotal technical approach for industry progress. However, raw training datasets often contain abundant redundant features, which increase model training’s computational cost and impair generalization ability. To tackle this, this study proposes the bionic ABCCOA algorithm, an enhanced version of the bionic Coati Optimization Algorithm (COA), to improve redundant feature elimination in datasets. To address the bionic COA’s inadequate global search performance in feature selection (FS) problems, leading to lower classification accuracy, an adaptive search strategy is introduced. This strategy combines individual learning capability and the learnability of disparities, enhancing global exploration. For the imbalance between the exploration and exploitation phases in the bionic COA algorithm when solving FS problems, which often traps it in suboptimal feature subsets, a balancing factor is proposed. By integrating phase control and dynamic adjustability, a good balance between the two phases is achieved, reducing the likelihood of getting stuck in suboptimal subsets. Additionally, to counter the bionic COA’s insufficient local exploitation performance in FS problems, increasing classification error rates, a centroid guidance strategy is presented. By combining population centroid guidance and fractional-order historical memory, the algorithm lowers the classification error rate of feature subsets and speeds up convergence. The bionic ABCCOA algorithm was tested on the CEC2020 test functions and engineering problem, achieving an over 90% optimization success rate and faster convergence, confirming its efficiency. Applied to 27 FS problems, it outperformed comparative algorithms in best, average, and worst fitness function values, classification accuracy, feature subset size, and running time, proving it an efficient and robust FS algorithm. Full article

This paper introduces a novel nature-inspired optimization algorithm called the Grizzly Bear Fat Increase Optimizer (GBFIO). The GBFIO algorithm mimics the natural behavior of grizzly bears as they accumulate body fat in preparation for winter, drawing on their strategies of hunting, fishing, and eating grass, honey, etc. Hence, three mathematical steps are modeled and considered in the GBFIO algorithm to solve the optimization problem: (1) finding food sources (e.g., vegetables, fruits, honey, oysters), based on past experiences and olfactory cues; (2) hunting animals and protecting offspring from predators; and (3) fishing. Thirty-one standard benchmark functions and thirty CEC2017 test benchmark functions are applied to evaluate the performance of the GBFIO, such as unimodal, multimodal of high dimensional, fixed dimensional multimodal, and also the rotated and shifted benchmark functions. In addition, four constrained engineering design problems such as tension/compression spring design, welded beam design, pressure vessel design, and speed reducer design problems have been considered to show the efficiency of the proposed GBFIO algorithm in solving constrained problems. The GBFIO can successfully solve diverse kinds of optimization problems, as shown in the results of optimization of objective functions, especially in high dimension objective functions in comparison to other algorithms. Additionally, the performance of the GBFIO algorithm has been compared with the ability and efficiency of other popular optimization algorithms in finding the solutions. In comparison to other optimization algorithms, the GBFIO algorithm offers yields superior or competitive quasi-optimal solutions relative to other well-known optimization algorithms. Full article

Bio-inspired joints play a pivotal role in legged robots, directly determining their motion capabilities and overall system performance. While shape memory alloy (SMA) actuators present superior power density and silent operation compared to conventional electromechanical drives, their inherent nonlinear hysteresis and restricted strain capacity (typically less than 5%) limit actuation range and control precision. This study proposes a bio-inspired joint integrating an antagonistic actuator configuration and differential dual-diameter pulley collaboration, achieving amplified joint stroke (±60°) and bidirectional active controllability. Leveraging a comprehensive experimental platform, precise reference input tracking is realized through adaptive fuzzy control. Furthermore, an SMA-driven bio-inspired leg is developed based on this joint, along with a motion retargeting framework to map human motions onto the robotic leg. Human gait tracking experiments conducted on the leg platform validate its motion performance and explore practical applications of SMA in robotics. Full article

This paper presents an enhanced artificial lemming algorithm (EALA) for solving complex unmanned aircraft system (UAV) path planning problems in three-dimensional environments. Key improvements include chaotic initialization, adaptive perturbation, and hybrid mutation, enabling a better exploration–exploitation balance and local refinement. Validation on the IEEE CEC2017 and CEC2022 benchmark functions demonstrates the EALA’s superior performance, achieving faster convergence and better algorithm performance compared to the standard ALA and 10 other algorithms. When applied to UAV path planning in large- and medium-scale environments with realistic obstacle constraints, the EALA generates Pareto-optimal paths that minimize length, curvature, and computation time while guaranteeing collision avoidance. Benchmark tests and realistic simulations show that the EALA outperforms 10 algorithms. This method is particularly suited for mission-critical applications with strict safety and time constraints. Full article

Few design methods exist that provide clearly structured, visually intuitive, and easily monitored scaffolding for navigating the considerable complexity of biomimetic processes. To this end, we present a holistic biomimicry process framework informed by design abstraction models that clarifies core skills involved and how they combine to form essential practices, such as biology-to-design and challenge-to-biology. The structure–function and conditions-conducive-to-life perspectives are facilitated, ensuring support for sustainability considerations. We pair this framework with two process tools that make biomimicry design moves explicit for learners, educators, and practitioners. We share examples of the use of these tools from an upper-level undergraduate engineering course in biomimicry. Our observations indicate that the framework and tools support process acquisition, design iteration, knowledge transfer, and sustainability integration in biomimicry education and practice by enabling common design behaviors such as variation, iteration, debugging, and reflection. Full article

Biological systems, ranging from ant colonies to neural ecosystems, exhibit remarkable self-organizing intelligence. Inspired by these phenomena, this study investigates how bio-inspired computing principles can bridge game-theoretic rationality and multi-agent adaptability. This study systematically reviews the convergence of multi-agent reinforcement learning (MARL) and game theory, elucidating the innovative potential of this integrated paradigm for collective intelligent decision-making in dynamic open environments. Building upon stochastic game and extensive-form game-theoretic frameworks, we establish a methodological taxonomy across three dimensions: value function optimization, policy gradient learning, and online search planning, thereby clarifying the evolutionary logic and innovation trajectories of algorithmic advancements. Focusing on complex smart city scenarios—including intelligent transportation coordination and UAV swarm scheduling—we identify technical breakthroughs in MARL applications for policy space modeling and distributed decision optimization. By incorporating bio-inspired optimization approaches, the investigation particularly highlights evolutionary computation mechanisms for dynamic strategy generation in search planning, alongside population-based learning paradigms for enhancing exploration efficiency in policy refinement. The findings reveal core principles governing how groups make optimal choices in complex environments while mapping the technological development pathways created by blending cross-disciplinary methods to enhance multi-agent systems. Full article

Biological systems can adaptively navigate multi-terrain environments via morphological and behavioral flexibility. While robotic systems increasingly achieve locomotion versatility in one or two domains, integrating terrestrial, aquatic, and aerial mobility into a single platform remains an engineering challenge. This work tackles this by introducing a bipedal robot equipped with a reconfigurable locomotion framework, enabling seven adaptive policies: (1) thrust-assisted jumping, (2) legged crawling, (3) balanced wheeling, (4) tricycle wheeling, (5) paddling-based swimming, (6) air-propelled drifting, and (7) quadcopter flight. Field experiments and indoor statistical tests validated these capabilities. The robot achieved a 3.7-m vertical jump via thrust forces counteracting gravitational forces. A unified paddling mechanism enabled seamless transitions between crawling and swimming modes, allowing amphibious mobility in transitional environments such as riverbanks. The crawling mode demonstrated the traversal on uneven substrates (e.g., medium-density grassland, soft sand, and cobblestones) while generating sufficient push forces for object transport. In contrast, wheeling modes prioritize speed and efficiency on flat terrain. The aquatic locomotion was validated through trials in static water, an open river, and a narrow stream. The flight mode was investigated with the assistance of the jumping mechanism. By bridging terrestrial, aquatic, and aerial locomotion, this platform may have the potential for search-and-rescue and environmental monitoring applications. Full article

In the process of mural protection and restoration, using optimization algorithms for image segmentation is a common method for restoring mural details. However, existing optimization-based image segmentation methods often lack image segmentation quality. To alleviate the aforementioned issues, this paper proposes a mural image segmentation algorithm based on OPBNGO by integrating the Northern Goshawk Optimization (NGO) algorithm with the off-center learning strategy, partitioned learning strategy, and Bernstein-weighted learning strategy. In OPBNGO, firstly, the off-center learning strategy is proposed, which effectively improves the global search ability of the algorithm by utilizing biased center individuals. Secondly, the partitioned learning strategy is introduced, which achieves a better balance between the exploration and development phases by applying diverse learning methods to the population. Finally, the Bernstein-weighted learning strategy is proposed, which effectively improves the algorithm’s development performance. Subsequently, the OPBNGO algorithm is applied to solve the image segmentation problem for eight mural images. Experimental results show that it achieves a winning rate of over 96.87% in terms of fitness function value, achieves a winning rate of over 93.75% in terms of FSIM, SSIM, and PSNR metrics, and can be considered a promising mural image segmentation algorithm. Full article

In this work, an adaptive robust control method based on Radial Basis Function Neural Network (RBFNN) is proposed. Inspired by the local response characteristics of biological neurons, this method can reduce the influence of nonlinear errors and unknown perturbations in the extreme working conditions of the aircraft, such as high dynamics and strong vibration, so as to achieve high tracking accuracy. In this method, the dynamic model of the nonlinear error of the fiber optic gyroscope is proposed, and then the unknown external interference observer is designed for the system to realize the estimation of the unknown disturbances. The controller design method combines the design of the adaptive law outside the finite approximation domain of the achievable condition design of the sliding mode surface, and adjusts the controller parameters online according to the conditions satisfied by the real-time error state, breaking through the limitation of the finite approximation domain of the traditional neural network. In the finite approximation domain, an online adaptive controller is constructed by using the universal approximation ability of RBFNN, so as to enhance the robustness to nonlinear errors and external disturbances. By designing the output constraint mechanism, the dynamic stability of the system is further guaranteed under the constraints, and finally its effectiveness is verified by simulation analysis, which provides a new solution for high-precision inertial navigation. Full article

Mycelium-based biocomposites represent a novel class of environmentally friendly materials. This study investigated the potential of using Salix psammophila and peanut straw as substrates for cultivating Pleurotus ostreatus and Ganoderma lucidum, respectively, to fabricate mycelium-based cushioning materials. The results demonstrated that the Pleurotus ostreatus-based cushion material using Salix psammophila (POSM) outperformed the Ganoderma lucidum-based cushion material using peanut straw (GLPM) in terms of overall performance. Both materials presented optimal comprehensive properties when the cultivation period reached 30 days. Increasing the substrate length enhanced most of the material properties. The resulting density ranged from 0.13 to 0.16 g/cm³, which was higher than that of polystyrene foam. The contact angles of both materials exceeded 120°, whereas their elastic springback rates reached 50.2% and 43.2%, and their thermal conductivities were 0.049 W/m·K and 0.051 W/m·K, respectively. Additionally, thermogravimetric analysis revealed that both materials exhibited similar thermal degradation behavior and relatively high thermal stability. These findings align with those of previous studies on mycelium composites and indicate that the physical and mechanical properties of the materials are largely comparable to those of expanded polystyrene (EPS). In conclusion, the developed mycelium-based cushioning materials promote the efficient utilization of agricultural residues and hold promise as a sustainable alternative to EPS, offering broad application prospects in the transportation and packaging sectors. Full article

Biomaterials and biomedical devices interact with the human body at different levels. At one end of the spectrum, medical devices in contact with tissue pose risks depending on whether they are deployed on the skin or implanted. On the other hand, food packaging and associated material technologies must also be biocompatible to prevent the transfer of harmful molecules and contamination of food, which could impact human health. These seemingly unlinked domains converge into a shared need for the elaboration of new laboratory evaluation protocols that consider recent advances in biomaterials and biodevices, coupled with increasing legal restrictions on the use of animal models. Here, we aim to select and prescribe physiologically relevant microenvironment conditions for biocompatibility testing of novel biomaterials and biodevices. Our discussion spans (1) the development of testing protocols according to material classes, (2) current legislation and standards, and (3) the preparation of biomimetic setups that replicate the microenvironment, with a focus on the multidisciplinary dimension of such studies. Testing spans several characterization domains, beginning with chemical properties, followed by mechanical integrity and, finally, biological response. Biomimetic testing conditions typically include temperature fluctuations, humidity, mechanical stress and loading, exposure to body fluids, and interaction with multifaceted biological systems. Full article

The pressing need to address climate change and environmentally related challenges highlights the importance of reimagining educational approaches to equip students with the skills required for innovation and sustainability. This study proposes a novel holistic pedagogic framework for architectural education that integrates biomimicry, systems thinking, and Bloom’s Revised Taxonomy to advance innovation, sustainability, and transformative learning. Developed through a triangulated methodological approach—combining reflective practitioner inquiry, design-based research, and conceptual model development—the framework draws from multiple theoretical perspectives to create a cognitively structured, interdisciplinary, and ecologically grounded educational model. Bloom’s Taxonomy provides a scaffold for learning progression, while the Function–Structure–Behavior (FSB) schema enhances the establishment of cross-disciplinary bridges to enable students to address complex design challenges. The framework is informed by insights from the literature and patterns observed in bio-inspired studios, student projects, and interdisciplinary workshops. These examples highlight how the approach supports systems thinking, ecological literacy, and ethical decision-making through iterative, experiential, and metacognitive learning. Rather than offering a fixed intervention, the framework is presented as a flexible, adaptable model that aligns learning outcomes with real-world complexity. It enables learners to navigate interdisciplinary knowledge, reflect critically on design processes and co-create regenerative solutions. By positioning nature as mentor, model, and measure, this pedagogic framework reimagines architectural education as a catalyst for sustainability and systemic change in the built environment. Full article

Developing safe and efficient cryoprotectants is critical for effective cryopreservation in biomedical applications. Inspired by natural antifreeze proteins (AFPs), a series of linear and star-shaped peptoids featuring isopropanol side chains to mimic the amphiphilic characteristics of threonine were prepared. The effects of chain length and molecular topology on antifreeze properties were systematically investigated. Both ice recrystallization inhibition (IRI) and ice crystal growth suppression improved with increasing chain length, and star-shaped peptoids exhibited superior performance. Notably, the star-shaped peptoid S-(A₆)₃ demonstrated excellent antifreeze activity and low cytotoxicity, highlighting its promise as a novel, non-toxic alternative to conventional cryoprotectants like DMSO. These findings provide valuable insight into the structure-property relationship of peptoids for cryopreservation applications. Full article

In the field of ultra-wideband (UWB) indoor localization, traditional backpropagation neural networks (BPNNs) are limited by their susceptibility to local minima, which restricts their ability to achieve global optimization. To overcome this challenge, this paper proposes a novel hybrid algorithm, termed ARO-BP, which integrates the Artificial Rabbit Optimization (ARO) algorithm with a BPNN. The ARO algorithm optimizes the initial weights and thresholds of the BPNN, enabling the model to escape local optima and converge to a global solution. Experiments were conducted in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments using a four-base-station configuration. The results demonstrate that the ARO-BP algorithm significantly outperforms traditional BPNNs. In LOS conditions, the ARO-BP model achieves a localization error of 6.29 cm, representing a 49.48% reduction compared to the 12.45 cm error of the standard BPNN. In NLOS scenarios, the error is further reduced to 9.86 cm (a 46.96% improvement over the 18.59 cm error of the baseline model). Additionally, in dynamic motion scenarios, the trajectory predicted by ARO-BP closely aligns with the ground truth, demonstrating superior stability. These findings validate the robustness and precision of the proposed algorithm, highlighting its potential for real-world applications in complex indoor environments. Full article

Multi-robot systems are significant in decision-making capabilities and applications, but avoiding collisions during movement remains a critical challenge. Existing decentralized obstacle avoidance strategies, while low in computational cost, often fail to ensure safety effectively. To address this issue, this paper leverages graph neural networks (GNNs) and deep reinforcement learning (DRL) to aggregate high-dimensional features as inputs for reinforcement learning (RL) to generate paths. Additionally, it introduces safety constraints through an artificial potential field (APF) to optimize these trajectories. Additionally, a constrained nonlinear optimization method further refines the APF-adjusted paths, resulting in the development of the GNN-RL-APF-Lagrangian algorithm. By combining APF and nonlinear optimization techniques, experimental results demonstrate that this method significantly enhances the safety and obstacle avoidance capabilities of multi-robot systems in complex environments. The proposed GNN-RL-APF-Lagrangian algorithm achieves a 96.43% success rate in sparse obstacle environments and 89.77% in dense obstacle scenarios, representing improvements of 59% and 60%, respectively, over baseline GNN-RL approaches. The method maintains scalability up to 30 robots while preserving distributed execution properties. Full article

As a soft actuator for fish robots, an oblique cone dielectric elastomer actuator (DEA) module inspired by the structure of white muscles in fish was proposed in the authors’ previous study. However, a mathematical model of an oblique cone DEA was not established, and designing a drive module that took into account its driving characteristics and passivity for integration into a fish robot remained a challenge. The purpose of this paper is to develop a vertebrate fish robot using multiple oblique cone DEA modules to achieve fish-like bending capability. First, an oblique cone DEA module was modeled for the design of a fish robot. The relationships among bending angle, blocking torque, driving voltage, and design parameters were established and confirmed by comparing the calculated and experimental results. Based on the modeling results, we designed an oblique cone DEA module-connected vertebrate fish robot. Finally, the experimental results of the fabricated fish robot demonstrated that the model-based design enabled flexible body swinging and swimming through a multiple-module-connected vertebrate structure. Full article

This study presents a novel, bioinspired perching mechanism designed to enhance the landing and takeoff capabilities of flapping wing micro aerial vehicles (FWMAVs). Drawing inspiration from the human hand, the lightweight gripper integrates a compliant claw structure actuated by shape memory alloys (SMAs) that mimic muscle movement. These SMA springs act as compact, lightweight substitutes for traditional actuators like motors or solenoids. The mechanism operates via short electrical impulses that trigger both opening and closing motions. A detailed design process was undertaken to optimize phalange lengths for cylindrical grasping and to select appropriate SMAs for reliable performance. Weighing only 50 g, the gripper leverages the high power-to-weight ratio and flexibility of SMAs, with the springs directly embedded within the phalanges to reduce size and mass while preserving high-force output. Experimental results demonstrate fast actuation and a grasping force of approximately 16 N, enabling the gripper to hold objects of varying shapes and sizes and perform perching, grasping, and carrying tasks. Compared to existing solutions, this mechanism offers a simpler, highly integrated structure with enhanced miniaturization and adaptability, making it especially suitable for low-payload MAV platforms like FWMAVs. Full article