Search Results (145)

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

Enhancing the thermal performance of building façades is vital for reducing energy demand in hot desert climates, where envelope heat gain increases cooling loads. This study investigates the integration of biomimicry into opaque ventilated façade (OVF) systems as a novel approach to reduce façade surface temperatures. Thirteen bio-inspired façade configurations, modeled after strategies observed in nature, were evaluated using computational fluid dynamics simulations to assess their effectiveness in increasing airflow and reducing inner skin surface temperatures. Results show that all proposed biomimetic solutions outperformed the baseline OVF in terms of thermal performance, with the wide top mound configuration achieving the greatest temperature reduction—up to 5.9 °C below the baseline OVF and 16.4 °C below an unventilated façade. The study introduces an innovative methodology that derives façade design parameters from nature and validates them through simulation. These findings highlight the potential of nature-based solutions to improve building envelope performance in extreme climates. Full article

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

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

During the service life of automotive panel stamping dies, the surface is often subjected to high loads and repeated friction, resulting in excessive wear. This leads to die failure, reduced machining accuracy, and decreased production efficiency. To enhance the anti-friction and wear-resistant performance of die steel surfaces, this study introduces the concept of biomimetic engineering in surface science. By mimicking microstructural configurations found in nature with outstanding wear resistance, biomimetic functional surfaces were designed and fabricated. Specifically, quadrilateral dimples inspired by the back of dung beetles, pentagonal scales from armadillo skin, and hexagonal scales from the belly of desert vipers were selected as biological prototypes. These surface textures were fabricated on Cr12MoV die steel using high-speed ball-end milling. Finite element simulations and dry sliding wear tests were conducted to systematically investigate the tribological behavior of surfaces with different dimple geometries. The results showed that the quadrilateral dimple surface derived from the dung beetle exhibited the best performance in reducing friction and wear. Furthermore, the milling parameters for this surface were optimized using response surface methodology. After optimization, the friction coefficient was reduced by 21.3%, and the wear volume decreased by 38.6% compared to a smooth surface. This study confirms the feasibility of fabricating biomimetic functional surfaces via high-speed ball-end milling and establishes an integrated surface engineering approach combining biomimetic design, efficient manufacturing, and parameter optimization. The results provide both theoretical and methodological support for improving the service life and surface performance of large automotive panel dies. Full article

Background: Vaccines have revolutionized disease prevention, yet their effectiveness is challenged by variable immunogenicity, individual response differences, and emerging variants. Biomimetic strategies, inspired by natural immune processes, offer new avenues to enhance vaccine performance. Objectives: This narrative review examines how bioinspired approaches—grounded in evolutionary medicine, immunology, and host–microbiota interactions—can improve vaccine immunogenicity and long-term protection. We further examine the evolutionary foundations of immune responses, highlighting how an evolutionary perspective can inform the development of durable, broadly protective, and personalized vaccines. Furthermore, mechanistic insights at the molecular and cellular level are explored, including Toll-like receptor (TLR) engagement, dendritic cell activation pathways, and MHC-I/MHC-II-mediated antigen presentation. These mechanisms are often mimicked in biomimetic systems to enhance uptake, processing, and adaptive immune activation. Results: The review highlights how immunosenescence, maternal immunity, genetic variation, and gut microbiota composition influence vaccine responses. Biomimetic platforms—such as nanoparticle carriers and novel adjuvants—enhance antigen presentation, boost adaptive immunity, and may overcome limitations in traditional vaccine approaches. Additionally, co-administration strategies, delivery systems, and microbiota-derived immunomodulators show promise in improving vaccine responsiveness. Conclusions: Integrating biomimetic and evolutionary principles into vaccine design represents a promising path toward safer, longer-lasting, and more effective immunizations Full article

Biomimetics has emerged as a transformative interdisciplinary approach that harnesses nature’s evolutionary strategies to develop sustainable solutions across diverse fields. This study explores its integrative role in shaping smart cities, advancing artificial intelligence and robotics, innovating biomedical applications, and enhancing computational design tools. By analysing the evolution of biomimetic principles and their technological impact, this work highlights how nature-inspired solutions contribute to energy efficiency, adaptive urban planning, bioengineered materials, and intelligent systems. Furthermore, this paper discusses future perspectives on biomimetics-driven innovations, emphasising their potential to foster resilience, efficiency, and sustainability in rapidly evolving technological landscapes. Particular attention is given to neuromorphic hardware, a biologically inspired computing paradigm that mimics neural processing through spike-based communication and analogue architectures. Key components such as memristors and neuromorphic processors enable adaptive, low-power, task-specific computation, with wide-ranging applications in robotics, AI, healthcare, and renewable energy systems. Furthermore, this paper analyses how self-organising cities, conceptualised as complex adaptive systems, embody biomimetic traits such as resilience, decentralised optimisation, and autonomous resource management. Full article

This paper presents a comprehensive review of multimodal emotion recognition (MER), a process that integrates multiple data modalities such as speech, visual, and text to identify human emotions. Grounded in biomimetics, the survey frames MER as a bio-inspired sensing paradigm that emulates the way humans seamlessly fuse multisensory cues to communicate affect, thereby transferring principles from living systems to engineered solutions. By leveraging various modalities, MER systems offer a richer and more robust analysis of emotional states compared to unimodal approaches. The review covers the general structure of MER systems, feature extraction techniques, and multimodal information fusion strategies, highlighting key advancements and milestones. Additionally, it addresses the research challenges and open issues in MER, including lightweight models, cross-corpus generalizability, and the incorporation of additional modalities. The paper concludes by discussing future directions aimed at improving the accuracy, explainability, and practicality of MER systems for real-world applications. 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

Underwater sensor deployment in military applications requires high precision, yet existing robotic solutions often lack the maneuverability and adaptability required for complex aquatic environments. To address this gap, this study proposes a bio-inspired underwater robot modeled after the marine iguana, which exhibits effective crawling and swimming capabilities. The research aims to develop a compact, multi-functional robot capable of precise sensor deployment and environmental detection. The methodology integrates a biomimetic mechanical design—featuring leg-based crawling, tail-driven swimming, a deployable head mechanism, and buoyancy control—with a multi-sensor control system for navigation and data acquisition. Gait and trajectory planning are optimized using kinematic modeling for both terrestrial and aquatic locomotion. Experimental results demonstrate the robot’s ability to perform accurate underwater sensor deployment, validating its potential for military applications. This work provides a novel approach to underwater deployment robotics, bridging the gap between biological inspiration and functional engineering. Full article

Task scheduling for multiple humanoid robot manipulators in industrial and collaborative settings remains a significant challenge due to the complexity of coordination, resource sharing, and real-time decision-making. In this study, we propose a framework for modeling task scheduling for multiple humanoid robot manipulators by using the symbolic discrete controller synthesis technique. We encode the task scheduling problem as discrete events using parallel synchronous dataflow equations and apply our synthesis algorithms to manage the task scheduling of multiple humanoid robots via the resulting controller. The control objectives encompass the fundamental behaviors of the system, strict rules, and mutual exclusions over shared resources, categorized as the safety property, whereas the optimization objectives are directed toward maximizing the throughput of robot-processed products with optimal efficiency. The humanoid robots considered in this study consist of two pairs of six-degree-of-freedom (6-DOF) robot manipulators, and the inverse kinematics problem of the 6-DOF arms is addressed using metaheuristic approaches inspired by biomimetic principles. Our approach is experimentally validated, and the results demonstrate high accuracy and performance compared to other approaches reported in the literature. Our approach achieved an average efficiency improvement of 40% in 70-robot systems, 20% in 30-robot systems, and 10% in 10-robot systems in terms of production throughput compared to systems without a controller. Full article

This paper presents the design and control of a quadruped robot equipped with a six-degree-of-freedom (6-DOF) bionic active torso based on a parallel mechanism. Inspired by the compliant and flexible torsos of quadrupedal mammals, the proposed torso structure enhances locomotion performance by enabling coordinated motion between the torso and legs. A complete kinematic model of the bionic torso and the whole body of the quadruped robot is developed. To address the variation in inertial properties caused by torso motion, a model predictive control (MPC) strategy with a variable center of mass (CoM) is proposed for integrated whole-body motion control. Comparative simulations under trot gait are conducted between rigid-torso and active-torso configurations. Results show that the active torso significantly improves gait flexibility, postural stability, and locomotion efficiency. This study provides a new approach to enhancing biomimetic locomotion in quadruped robots through active torso-leg coordination. Full article

Spoken Language Identification (SLID)-based applications have become increasingly important in everyday life, driven by advancements in artificial intelligence and machine learning. Multilingual countries utilize the SLID method to facilitate speech detection. This is accomplished by determining the language of the spoken parts using language recognizers. On the other hand, when working with multilingual datasets, the presence of multiple languages that have a shared origin presents a significant challenge for accurately classifying languages using automatic techniques. Further, one more challenge is the significant variance in speech signals caused by factors such as different speakers, content, acoustic settings, language differences, changes in voice modulation based on age and gender, and variations in speech patterns. In this study, we introduce the DBODL-MSLIS approach, which integrates biomimetic optimization techniques inspired by natural intelligence to enhance language classification. The proposed method employs Dung Beetle Optimization (DBO) with Deep Learning, simulating the beetle’s foraging behavior to optimize feature selection and classification performance. The proposed technique integrates speech preprocessing, which encompasses pre-emphasis, windowing, and frame blocking, followed by feature extraction utilizing pitch, energy, Discrete Wavelet Transform (DWT), and Zero crossing rate (ZCR). Further, the selection of features is performed by DBO algorithm, which removes redundant features and helps to improve efficiency and accuracy. Spoken languages are classified using Bayesian optimization (BO) in conjunction with a long short-term memory (LSTM) network. The DBODL-MSLIS technique has been experimentally validated using the IIIT Spoken Language dataset. The results indicate an average accuracy of 95.54% and an F-score of 84.31%. This technique surpasses various other state-of-the-art models, such as SVM, MLP, LDA, DLA-ASLISS, HMHFS-IISLFAS, GA base fusion, and VGG-16. We have evaluated the accuracy of our proposed technique against state-of-the-art biomimetic computing models such as GA, PSO, GWO, DE, and ACO. While ACO achieved up to 89.45% accuracy, our Bayesian Optimization with LSTM outperformed all others, reaching a peak accuracy of 95.55%, demonstrating its effectiveness in enhancing spoken language identification. The suggested technique demonstrates promising potential for practical applications in the field of multi-lingual voice processing. Full article

Polydopamine (PDA) is an emerging biomimetic material that stimulates the distinctive physicochemical properties of the blue mussel byssus. In this study, we report a rapid and facile method for the decoration of gold nanoparticles (AuNPs) onto the mussel-inspired polydopamine nanospheres (PDA NSs) via cold atmospheric plasma treatment. After 10 min of plasma treatment, AuNPs with a size of 10.3 ± 2.0 nm were formed on the surface of PDA NSs. This reaction was performed without the need for any additional reducing agents, thereby eliminating the use of harsh chemicals during the process. The synthesized AuNP-decorated PDA nanohybrids (PDA-Au) exhibit effective catalytic activity for the decoloration of Rhodamine B, with a pseudo-first-order rate constant of 1.405 min⁻¹. The green synthesis approach in this work highlights the potential of plasma-assisted methods for decorating biomimetic materials with metallic nanoparticles for catalytic and environmental applications. Full article