Bioinspiration & Biomimetics最新文献

Schooling fish often self-organize into a variety of collective patterns, from polarized schooling to rotational milling. Mathematical models support the emergence of these large-scale patterns from local decentralized interactions, in the absence of individual memory and group leadership. In a popular model where individual fish interact locally following rules of avoidance, alignment, and attraction, the group exhibits collective memory: changes in individual behavior lead to emergent patterns that depend on the group's past configurations. However, the mechanisms driving this collective memory remain obscure. Here, we combine numerical simulations with tools from bifurcation theory to uncover that the transition from milling to schooling in this model is driven by a noisy transcritical bifurcation where the two collective states intersect and exchange stability. We further show that key features of the group dynamics-the bifurcation character, transient milling, and collective memory-can be captured by a phenomenological model of the group polarization. Our findings demonstrate that collective memory arises from a noisy bifurcation rather than from structural bistability, thus resolving a long-standing ambiguity about its origins and contributing fundamental understanding to collective phase transitions in a prevalent model of fish schooling.

Previous studies have shown that many insects frequently alternate between hovering and forward flight with comparable power expenditure. However, hovering is relatively rare in butterflies, raising the question of whether this behavior entails higher energy costs-a possibility that remains unexplored due to limited research on the power of butterfly hovering. To address this gap, this study employs an integrated experimental and computational approach to investigate the aerodynamic and inertial forces governing power consumption during butterfly hovering. High-speed videography is employed to capture detailed morphology and kinematic parameters of the body and wings. The flow field around the wings is simulated using the lattice Boltzmann method coupled with the immersed boundary method, enabling high-fidelity resolution of unsteady aerodynamic forces and moments. Theoretical analyses are further applied to interpret the contributions to aerodynamic and inertial power. Results demonstrate that, despite substantial wing inertia and pronounced body pitching motion, aerodynamic power constitutes the dominant portion of total power expenditure. Inertial power arises primarily from wing motion rather than body dynamics. The mass-specific power of hovering butterflies is approximately 28 W kg^-1, which aligns with the range observed in most insects (20-60 W kg^-1). Furthermore, even with the assumption of complete elastic energy storage, the maximum energy savings amount to only about 10% of the total power. These findings offer new insights into the energetics of butterfly flight and provide valuable guidance for the design of bio-inspired flapping-wing micro aerial vehicles.

Achieving large initial coil pitches and contractile strokes in twisted and coiled polymer artificial muscles often requires complex and multi-step fabrication processes. We present a self-induced large-pitch (SLiP) method for producing polymer muscles with large and stable initial coil pitches through a single-step annealing process, which can be tuned by only adjusting the annealing temperature and duration. The resulting muscles demonstrate contractile and tensile strains of 95.1% and 560%, respectively, under different chiral configurations, as well as a maximum specific power of 3.5 kW kg^-1and a contractile stroke rate of 40% s^-1. SLiP muscles are used in various biomimetic and soft robotic prototypes, including a biomimetic arm, large-deformation tentacles, a crawling robot, and a biomimetic hand. This method offers a practical route for realizing polymer muscles with giant stroke and preload-free actuation in soft robotics.

Soft actuators possess both active and passive degrees of freedom (DOFs). This paper proposes the concept of using soft actuators to drive rigid-soft coupled single-open-chain (SOC) configurations. Research focuses on the active and passive DOF characteristics of the soft actuator within this framework. First, based on the graphical approach and generalized function set theory, the motion characteristics at the input points of the human skeletal structure and the corresponding skeletal muscle motion characteristics are analyzed. Then, the matching relationship is mapped onto the rigid-soft coupled configuration. The motion requirements at the input points of the rigid branch and the end characteristics that the soft actuator should possess are analyzed. Furthermore, based on the end characteristics of the soft actuator, the discrete element method is employed to analyze the types of micro-units constituting its deformation characteristics. Finally, an experimental setup featuring a rigid-soft coupled SOC mechanism based on pneumatic soft actuators was constructed. Results demonstrate that the soft actuator achieves active deformation during passive bending, twisting, and combined bending-twisting deformations. The influence of passive DOFs on active DOFs within specific ranges in the rigid-soft coupled composite configuration was analyzed.

Humpback whales produce a wide variety of frequency-modulated vocalizations, called song units. Modeling and synthesis of these units form the basis for many bio-inspired applications, including underwater covert communication and naturalistic playback experiments. Conventional synthesis methods are based on fundamental frequency contour modeling of single-segment signals, which exhibit limitations in terms of synthesis flexibility and similarity. To address the above limitations, this paper proposes a humpback whale song unit synthesis method based on small-sample training. Fundamental frequency contours and line spectral pairs are extracted from humpback whale song units collected in marine environments to construct the training dataset. Using these parameters, a hidden Markov model (HMM) is established for parameter training, and probability density functions are obtained for each HMM state. To address high-frequency jitter in generated fundamental frequency contours, a parameter generation method that combines dynamic feature constraints with variational mode decomposition denoising is introduced, yielding smoother fundamental frequency curves. For enhanced synthesis flexibility, state duration modification and fundamental frequency modification methods are proposed based on parameter distributions. Finally, the generated parameters are converted into time-domain waveforms using a linear predictive coding-pitch vocoder. To comprehensively evaluate the synthesis performance, an assessment framework based on statistical parametric analysis and t-distributed stochastic neighbor embedding is established. Simulation results demonstrate that the proposed humpback whale song unit synthesis system achieves superior flexibility and similarity compared to the conventional approach based on single whistles modeling, ultimately enhancing performance in bio-inspired applications.

Fish swim with flexible fins that stand in stark contrast to the rigid propulsors of engineered vehicles. Using numerical simulations of the dynamics of flow-structure interaction, we have found that dorso-ventral deformation in flexible caudal fins results in a 70% increase in efficiency of caudal fin swimmers compared to a rigid fin generating the same amount of thrust. By correlating fin deformation to the flow physics, we find that the greater power requirements of rigid fins can be largely attributed to their propensity to generate high-magnitude lateral forces. In contrast, flexible fins achieve high efficiency local-redirection of force where deformations orient pressure forces on the fin in fore-aft and dorso-ventral directions to reduce the power demand of generating thrust forces. These deformations occur at phases in the tail-beat cycle where the fin experiences large lateral velocities and pressure differentials and this reduces the net power expended by the flexible fins. In this way, the flexibility of a caudal fin offers a simple and elegant solution for efficient locomotion which does not require sensing, computation and control that might otherwise be provided by the nervous system of a fish or a computer within a underwater vehicle. These flow-induced dorso-ventral fin deformations therefore imbue a mechanical intelligence in these fins that provides propulsive advantages to caudal fin swimmers and they also offer solutions for efficient propulsion in engineered systems.

Gecko-inspired adhesives offer strong, reversible, and directionally tunable adhesion, yet fabrication methods often depend on cleanroom lithography or proprietary molds, limiting scalability and accessibility. This study presents a low-cost, modular fabrication strategy combining high-resolution digital light processing 3D printing with 1000 lines/mm optical diffraction gratings to create hierarchical elastomeric adhesives. The resulting structures feature macroscale micropillars and embedded sub-micron surface topography, enabling effective contact splitting without advanced microfabrication. Mechanical testing reveals a nonlinear increase in shear performance with contact area, with maximum shear forces exceeding 80 N at 103.2 cm². Peel testing across varied angles and surface areas demonstrates anisotropic adhesion, with peak peel strength of 21.94 N and detachment energy of 3.88 Jm-2at a 30° peel angle for patch area of 103.2 cm². A comparative cost analysis highlights the accessibility of this method, revealing a 10-100xreduction in fabrication cost relative to cleanroom and roll-to-roll-based techniques. This approach enables reproducible microstructure transfer, optical validation, and application-specific tunability, offering a practical, scalable pathway for bio-inspired adhesives in robotics, wall-climbing systems, and soft interface applications.

This paper presents a coupled framework for simulating fish schooling, integrating social interactions through a self-propelled particle (SPP) model and flow dynamics via computational fluid dynamics (CFD). In the SPP model, the fish interact with a finite number of topologically defined neighbors, whereas in the CFD model, the fish follow the positions and orientations prescribed by the SPP model through undulatory motion. The undulatory kinematics are generated using a pre-trained deep reinforcement learning model from prior simulation data. Although the CFD trajectories do not exactly match those of the SPP model, they closely approximate them, providing a useful degree of flexibility that allows for physical realism while preserving computational efficiency. For example, in simulations of a minimal two-fish group, the trailing fish achieves stable locomotion through a slight side-slip, an emergent behavior not explicitly encoded in the SPP input. The model is further extended to large schools, demonstrating that group efficiency increases with the Reynolds number because of more favorable hydrodynamic interactions.

The presence of gaps between feathers is known to enhance the aerodynamic performance of birds during flapping flight. To investigate the underlying flow mechanisms of this phenomenon, we numerically investigate a two-dimensional zero-thickness flat-plate airfoil with a hinged perforation. The immersed boundary-lattice Boltzmann method is employed to simulate the flow over a zero-thickness flat-plate airfoil, where the hinge-representing a feather gap-is prescribed to open during the upstroke and close during the downstroke. The effects of the gap position, size, and maximum opening angle on aerodynamic performance are systematically analyzed. The results demonstrated that, within the studied parameter ranges, upward perforations increased the pressure differential near the leading edge, while downward perforations reduced it. However, hinge-related vortices generated downstream of the perforations partially offset these pressure differential effects. For downward perforations, optimal positioning, larger sizes, and greater maximum opening angles significantly improved the lift and lift efficiency. Upward perforations enhanced the aerodynamic performance only under restricted perforated conditions: positioning at a distance of 0.25 times the chord length from the leading edge, sizes of less than 0.2 times the chord length, and maximum opening angles of less than 30°. Downward perforations generally outperformed upward configurations in terms of lift and lift efficiency, making them preferable for engineering applications, though self-propulsion effects and three-dimensional flow interactions require further investigation. These findings provide insights for optimizing the perforation designs in micro-flapping-wing vehicles.

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.