Bioinspiration & Biomimetics最新文献

Traditional soft robotic grippers often lack the structural rigidity required to maintain stable poses under external forces, as well as the fine control and precision offered by rigid grippers or conventional robotic hands. These limitations are particularly significant in tasks requiring dexterous manipulation, such as in-hand manipulating objects. This paper proposes a bio-inspired spine mechanism capable of self-adapting to the variable length of the finger, thus increasing strength and stiffness without compromising the intrinsic compliance of soft fingers. A passive inflatable soft fingertip design is further introduced to enhance grasp stability. The performance of the proposed soft fingers mounted on a reconfigurable palm is evaluated through stiffness characterization, grasping tests, and in-hand manipulation demonstrations. Experiments show that the spine substantially increases both front and side stiffness and improves grasp stability under dynamic conditions. With the combined advantages of reconfigurable palm mechanism and the adaptive soft fingers, the proposed Soft Reconfigurable Hand achieves robust grasping and stable in-hand manipulations across diverse tasks.

Micro air vehicles operating at ultracompact scales under low Reynolds number regimes confront inherent aerodynamic constraints. While fixed and rotary-wing systems suffer efficiency losses from dominant viscous forces, flapping-wing micro air vehicles (FWMAVs) circumvent these constraints through unsteady aerodynamic mechanisms. However, the challenge of integrating propulsion, actuation, and control within restricted volumes of FWMAVs necessitates biohybrid solutions leveraging insect-derived passive mechanisms. These mechanisms exploit inherent dynamic properties and natural physical interactions rather than programmed controllers or auxiliary power sources, effectively addressing fundamental engineering challenges through mechanical simplification and energy demand reduction. This review systematically examines passive mechanisms in hovering FWMAVs across biological foundations and engineered implementations. First, strategies for replicating insect wing motion patterns are introduced. Then, the intrinsic properties of flapping wings as well as effects on aerodynamic performance and flight stability are discussed. Further, comparative evaluations are presented between conventional FWMAVs and two emerging beyond-natural designs combining biological principles with engineered innovations. Finally, two research frontiers in passive mechanisms applications are mentioned, whose implementation will help to expand FWMAVs' operational envelopes and enhance mission versatility. .

Generating robust and adaptable legged locomotion with minimal control architecture remains an open challenge in bio-inspired robotics. Existing central pattern generator (CPG) approaches often rely on multi-neuron oscillators, asymmetrical network structures, abstract phase oscillators, or task-specific tuning to produce stable gaits. Here, we address this problem by introducing a minimal sensorimotor control framework based on single-neuron CPGs with proprioceptive feedback. Through stability analysis and physical experiments, we show that fully symmetric coupling between single-neuron units is sufficient to generate self-organized tripod-type gaits, enable reliable gait switching via single-pulse kick control, and sustain locomotion even under leg failure. In the strong-attractoring limit, coordinated locomotion emerges without intrinsic neural oscillations, driven solely by sensory feedback. The same framework, without parameter changes, also produces coordinated quadruped locomotion, illustrating its generality. This demonstrates that complex and robust locomotor patterns can arise from extremely simple decentralized mechanisms. Our results contribute to the search for generative principles underlying locomotion and provide a lightweight, extensible basis for bio-inspired control across diverse robotic platforms.

Humans can now emulate language in silica-based neural networks, but we remain ignorant about how language emerged in carbon-based neural networks in the first place. This gap represents not merely a scientific blind spot, but a unique opportunity to revolutionise artificial intelligence (AI) through biomimicry. By reverse-engineering the evolutionary principles that enabled language in the hominid lineage - principles still in operation today in nonhuman great apes, humans' closest living relatives - we can inspire language-based AI models radically more efficient and sustainable. Current AI models achieve remarkable performance through brute-force scaling of data and compute, yet they remain orders of magnitude less energy-efficient than the human brain. In contrast, language in ape-like hominid ancestors evolved under stringent energetic and ecological constraints, yielding sophisticated combinatorial systems, rhythmic hierarchies, recursive call structures, and context-dependent vocal deception using minimal neural and energetic resources. These naturally selected patterns and rules honed over millions of generations offer the true "foundational algorithms" of language and a proven blueprint for sustainable intelligence. Bridging carbon- and silica-based language systems through biomimicry will accelerate truly sustainable AI but also illuminate why language alone - over every conceivable alternative - was elected as the foundational medium and architecture for advanced intelligent behaviour.

Aquatic organisms exhibit remarkable diversity in swimming strategies, even within shared modes such as body-caudal fin (BCF) propulsion. Here, we investigate the biomechanical underpinnings of BCF swimming by mapping performance trade-offs across a 6-dimensional design space. Using a computational framework that integrates computational fluid dynamics (CFD), machine learning (ML), multi-objective optimization (MOO), and global sensitivity analysis (GSA), we identified distinct Pareto-optimal fronts between swimming speed and cost of transport. Along these fronts, we uncovered key performance relationships, including that propulsive efficiency is maximized when speed and cost of transport are weighted nearly equally in the objective function, highlighting the benefits of balancing competing demands. We further demonstrate that multiple combinations of kinematic traits can yield comparable performance, revealing both redundancies and sensitivities that provide a mechanistic basis for the diversity of swimming patterns observed in fish. Together, these results generate new biological hypotheses and suggest how evolutionary pressures may shape locomotor design.

Understanding terrestrial locomotion in walking fish species can unlock new insights into vertebrate evolution and inspire versatile robotic systems capable of traversing diverse environments. We introduce a novel, single-actuator continuum robot inspired by the terrestrial locomotion of the gray bichir (Polypterus senegalus), which employs a simple rotating helix to reproduce realistic undulatory movements. We hypothesized that a simplified robotic model with minimal actuation could accurately replicate the terrestrial locomotion patterns observed inP. senegalus. Using a 'robot-twin' methodology, we developed four helix configurations directly informed by the observed gait postures of real fish specimens and compared robotic performance and kinematics against biological data. We found that helix geometry significantly influenced both locomotion speed and lateral stability, with designs closely mimicking biological curvatures often exhibiting trade-offs between accuracy and performance. The fastest helix configuration produced the greatest lateral oscillation, whereas the most biologically accurate shape resulted in reduced locomotion efficiency. Additionally, integrating passive leg structures greatly enhanced stability, mirroring the biomechanical function of pectoral fins in the real fish. These findings underscore the value of minimalistic robotic designs in understanding fish-like locomotion and pave the way for future robotic platforms using reduced degrees of freedom.

Neural models inspired by the locust's lobula giant movement detectors (LGMD), noted for their low power consumption and high computational efficiency, have significantly advanced visual collision detection from image streams. However, their performance often deteriorates in visually noisy environments. Biological studies indicate inherent randomness in synaptic transmission, suggesting that introducing probabilistic modeling could more accurately represent biological uncertainty and improve robustness against noise. A preliminary study recently demonstrated that incorporating a Bernoulli-distribution probability could enhance the LGMD model's robustness under noisy visual conditions. To further investigate which probability distribution optimally improves looming detection performance, this study proposed integrating a Gaussian-distribution probability into an LGMD neural network model with ON/OFF-contrast channels. The parameters of this model were searched through evolutionary computation across diverse day and night collision scenarios. Compared with the previous work, the method demonstrated superior robustness in both realistic and artificially noisy environments, achieving an 83% improvement regarding the distinct ratio, a metric to quantify sensitivity to noisy signals. An interesting finding through tests in generalized scenarios indicated that while the introduction of probability significantly enhances LGMD model's performance, the specific type of probability distribution is less critical. Moreover, this research explored variations in probability parameters across the ON/OFF-channels and suggested that stochastic signal processing not only effectively simulates uncertainty in neuronal transmission but also modulates signal propagation strength. This dual functionality balances neural processing and significantly enhances the robustness of looming detection in noisy visual conditions.

Modern bio-inspired robotic fish design increasingly focuses on integrating biological inspiration with engineering-oriented structural solutions to enhance locomotion performance and meet practical application demands. Among these, the crank-linkage propulsion system presents a structurally efficient solution capable of delivering stable and effective thrust under high-frequency actuation. However, most existing numerical studies remain centered on fully biomimetic simulations, lacking systematic guidance for the engineering implementation of such transmission mechanisms. Starting from a tuna-inspired robotic fish model, this study systematically investigates the effects of crank length and caudal fin morphology on hydrodynamic performance and vortex dynamics. The influence of key flow parameters, namely the Reynolds number (Re) and Strouhal number (St), on propulsion characteristics is also considered. Results demonstrate that crank length significantly influences thrust generation by modulating interactions between the leading-edge vortex (LEV) and the posterior body vortex (PBV). For a tuna-inspired trunk and caudal fin, a crank length of 0.28L significantly enhances thrust generation through the synergistic interaction between PBV-induced LEV intensification and periodic vortex evolution, while maintaining nearly constant propulsive efficiency. Investigations on fin morphology reveal that, under constant chord length and fin area, propulsive efficiency generally decreases with increasing aspect ratio. Fins with aspect ratios close to 1 and area concentration near the trailing edge, such as the truncate type, enhance thrust generation by delaying LEV detachment and intensifying vorticity strength. Increased Re strengthens vortex interactions, while St affects wake structures. These findings offer theoretical insights for the optimized design of efficient, hybrid-driven robotic fish based on crank-linkage propulsion systems.

Lizards are among the most biomechanically versatile animals, exhibiting a broad range of physical and behavioral adaptations, such as adhesion, agile locomotion, vertical climbing, righting reflexes, and various tail-assisted aerial maneuvers. These features have inspired a growing body of biomimetic technologies spanning robotics, medical devices, and control algorithms. This survey provides a comprehensive overview of lizard-inspired design principles and their applications in engineering systems. Starting from biological foundations, we review key physical and behavioral traits and map them to their engineered analogs, including soft adhesion mechanisms, metaheuristic control algorithms, and multi-modal locomotion systems. Special attention is given to lizard righting strategies in the development of self-righting robotic platforms. The survey also extends to the extraterrestrial relevance of lizard-inspired systems, highlighting studies of lizard behavior under altered gravity conditions. Applications in space robotics are explored through gecko-inspired adhesive grippers, locomotion analogies for planetary rovers, and dynamic parallels between lizard biomechanics and free-floating space manipulators. Despite the growing body of work, a comprehensive synthesis uniting terrestrial and extraterrestrial biomimetic insights has been lacking. This review aims to bridge that gap by mapping the trajectory of lizard-inspired biomechanics from biological foundations to robotic implementations, highlighting key achievements, interdisciplinary linkages, and frontiers for future exploration.

Origami-inspired folding enables structures to achieve multiple stable configurations, but predicting and controlling these states remain challenging. In nature, insects such as the earwig (Forficula auricularia) utilize non-Euclidean folding principles, leveraging asymmetric resilin-rich creases for compact storage and rapid deployment. Inspired by this, we investigate the bistable and multi-stable behavior of origami-inspired eggbox and saddle units, focusing on how mirroring configurations dictate stability. Through analytical energy modeling and experiments, we confirm that bistability in single units arises from a dominant folding (dihedral) angle-similar to the primary hinge regulation in earwig wings-enabling single-input actuation. In two-unit assemblies, mirroring along the dominant fold axis enforces synchronized snap-through, yielding a coupled bistable system, whereas mirroring along a secondary axis allows independent flipping, resulting in four stable states. Building upon this bioinspired principle, we extend the design to incorporate both deficit and redundant angles while maintaining a symmetric folding scheme, offering a systematic approach to programming multi-stability in origami-based structures. These findings provide a bioinspired strategy for programming multi-stable origami structures through geometric constraints and mirroring. The ability to toggle between synchronized and independent snap-through simplifies control and enables shape transformations without continuous actuation. This approach has broad applications in deployable structures, bioinspired soft robotics, and adaptive materials, leveraging multi-stability for efficient morphing.