Bioinspiration & Biomimetics最新文献

Recent neuroscience discoveries on human hand synergies have inspired the development of underactuated robotic hands, which replicate human-like grasping capabilities using a minimal number of actuators. However, a generalized methodology for determining the parameters of such bio-inspired underactuated hands to maximize anthropomorphic grasping abilities remains a significant challenge. To address this, we propose a novel framework based on Hertz contact theory to establish a general underactuated grasping model. Within this framework, we introduce evaluation indices and constraint conditions integrating morphological parameter ranges of the human hand derived from a scientific analysis in our prior work and an approximation index between human hand motions and robotic hand motions, aimed at: 1) biomimetic part: ensuring that the robotic hand's morphology, motion, and posture closely mimic those of the human hand, and 2) robotic part: maximizing the Euclidean norms of normal contact forces between the robotic hand and the object during grasping. To streamline the parameter optimization process, we devise a comprehensive, step-by-step strategy that groups parameters sequentially, enabling rapid convergence to optimal solutions. As a case study, we design and develop a dual-actuated robotic hand, comparing unaltered and optimized parameter schemes through extensive simulations and experimental validations. The results demonstrate the effectiveness of our method and suggest its potential applicability to a wide range of underactuated robots and bionic systems. This work provides a systematic approach to advancing the design and optimization of anthropomorphic robotic hands, bridging the gap between biological inspiration and engineering implementation.

Inspired by the stabilization of a bird's head by the arc-shaped supporting structure of its neck, a nonlinear vibration isolator that imitates these properties is proposed. The geometry and stiffness properties of the isolator, which consists of three rods connected by torsional springs, are designed for a specific payload to realize an isolator with a very low natural frequency offering good vibration isolation properties over a wide frequency range. A prototype is constructed to isolate a smart phone camera mounted on a bicycle from vibration excitation due to a rough road. The results show that the isolator is effective above a frequency of approximately 1 Hz.

Riblets inspired by the dermal denticles of shark skin are widely recognized for their drag-reducing performance. Although previous research has predominantly focused on two-dimensional riblet geometries, three-dimensional (3D) topographies remain underexplored due to the complex architecture of denticle-inspired surfaces. Natural riblet arrays, comprising thousands of interconnected dermal denticles, pose challenges in terms of parameterization, simulation, and fabrication. This work addresses these challenges by introducing a 3D, riblet-reinforced surface topography design that reduces drag, suppresses flow-induced noise, and simplifies both parameterization and prototyping, ultimately providing a scalable solution for towed array sonar applications. Leveraging Bayesian optimization, our computational fluid dynamics (CFD) results reveal that the optimal design decreases the overall sound pressure level by 6.87 dB and reduces drag by 0.34%, effectively balancing noise mitigation with hydrodynamic performance. The design that achieves the greatest noise reduction lowers flow noise by 8.81 dB, albeit with a slight increase in drag. The most effective design for drag reduction yields a 5.18% decrease, accompanied by significant noise suppression across key frequency bands. Flow field analysis demonstrates that our design alters the near-wall vorticity dynamics by promoting the formation of vortex rings that detach from the surface, thereby reducing turbulent energy transfer and limiting sound pressure fluctuations relative to a smooth surface design. To this end, the combination of CFD simulations and Bayesian optimization offers an efficient pathway to refine riblets-reinforced surface topographies, paving the way for advanced bioinspired designs that improve acoustic performance and efficiency in underwater applications.

The passive compliance of a soft worm-like body can be a key advantage for traversal of complex confined spaces, but in practice, the body's stiffness and contact friction often require experimental adjustments. Here, for the first time, we develop a dynamic, 3D simulation that enables systematic testing of robot parameters (e.g. stiffness and friction) in different radius of curvature environments, which will help us better understand design trade-offs in creating soft robots that mimic worm-like locomotion. Specifically, we use the open-source physics engine MuJoCo because it is established for both biomechanical and robotic modeling, as well as multi-point contact dynamics, which are present in confined spaces. The model has sensory capabilities analogous to the stretch and tactile proprioception of an earthworm and is amenable to both feedforward and feedback control. After validating our model by comparing to our previous physical robot, we quantify locomotion performance over a range of friction coefficients, structural stiffnesses, and turning radii. We found that speed increased with friction coefficient on flat ground for higher stiffness models, but decreased with friction coefficient for lower stiffness models, both on flat ground and in pipe bends. For turning radii greater than 0.45 m, speed and stiffness also had a positive correlation, however, below the critical turning radius of 0.45 m, increasing stiffness had no appreciable influence on speed. This simulation can potentially be used to optimize designs for particular environments, to better understand the influence of passive vs. active control on individual and coupled segments, and perhaps offer a deeper understanding of how animals and robots can employ soft structures. For example, we can posit from our results that changing stiffness will not increase speed below the critical turning radius, meaning further experiments should focus on other parameters or actively controlled turning to improve speed through tighter turns.

The focus of this work is to investigate the influence of stiffness distribution in the fish tail on swimming performance and to determine the optimal stiffness distribution. Targeting fish employing the body and/or caudal fin (BCF) swimming mode, we constructed an fluid-structure interaction (FSI) simulation model based on the characteristics of BCF locomotion. Using this FSI model, we systematically examined multiple typical stiffness distributions along the inter-ray and ray-aligned directions, summarizing the underlying patterns in these two directions. Subsequently, we expanded the dataset obtained from the FSI simulations. Based on the expanded dataset, we developed a surrogate model using support vector regression (SVR) enhanced by the young's double-slit experiment optimization algorithm (YDSE). An improved particle swarm optimization algorithm was then applied to this surrogate model to identify the stiffness distributions corresponding to maximum thrust and highest efficiency, respectively. Compared to the original dataset, the optimized solutions obtained through YDSE-SVR iteration increased thrust by 4.94% and efficiency by 6.86%. Finally, we analyzed the mechanisms behind the differences in thrust and efficiency using pressure contours and streamline diagrams. The derived patterns regarding the influence of fish tail stiffness distribution on swimming performance can provide insights for robotic fish design.

Collective intelligence in biological groups can be employed to inspire the control of artificial complex systems, such as swarm robotics. However, modeling for the social interactions between individuals is still a challenging task. Without loss of generality, we propose a deep attention network model that incorporates the principles of biological Hard Attention mechanisms, that means an individual only pay attention to one or two neighbors for collective motion decision in large group. The model is trained by the collective movement data of five rummy-nose tetra fish (Hemigrammus rhodostomus). The structure of the model enforces individual agents to consider information from at most two neighboring agents. Meanwhile, the model can reveal hidden locations, where highly influential neighbors frequently appear. These findings demonstrate that the proposed Hard Attention Model aligns with the information processing mechanisms, which is observed in fish schooling. Experimental results indicate that the model exhibits a strong ability to decouple sparse information for collective movement with robust metrics. It can also perform excellent scalability in different group sizes. The simulation and real robots experiment show that the model provides a powerful tool for analyzing multi-level behaviors in complex systems and offers significant insights for the distributed control of swarm robotics.

The female sawfly (Insecta: Hymenoptera, Symphyta) uses a double blade reciprocating saw-like ovipositor to cut into plant tissue and lay its eggs within the cut. Whereas extensive study was carried out for wood-boring ovipositors, little is known about how sawflies achieve such controlled cutting in soft substrates. This suggests a mechanism that balances effective cutting with minimal tissue disruption. This article reports a novel passive selective cutting mechanism in which the saw discriminates between material properties of the plant tissue without active sensing or external control, something rarely achieved in human-made systems. Scaled-up biomimetic blades replicating key ovipositor features were tested on synthetic substrates (agar and ballistic gelatine) across a range of stiffnesses. Experimental results reveal a force-dependent threshold above which the saw is displaced rather than cutting. This threshold depends on the interaction between the shape of the saw teeth and the substrate properties and is consistent across multiple sawfly species. These findings reveal a previously undescribed bioinspired cutting principle with potential for surgical tools that avoid damaging sensitive tissues, and broader applications where passive, material-specific selectivity is required without the complexity of sensors or active feedback control. .

The energetic consequences of swimming within a neighboring fish's vortex street remain a central question in collective locomotion. Recent flume experiments in which a flapping hydrofoil generated a biomimetic wake demonstrated that a trout can station-keep behind the foil while displaying kinematics markedly different from those used in uniform flow. To examine the underlying hydrodynamics, we accurately replicate the fish-foil system by first reproducing the experimentally recorded motions using a joint-based kinematic reconstruction method, and then we simulate the fluid dynamics with three-dimensional computational fluid dynamics. A companion simulation without the foil is also conducted to isolate wake effects. Relative to uniform-flow swimming, the presence of the foil wake reduces the trout's cycle-averaged hydrodynamic power expenditure by 11.4 ± 0.0003%, a benefit that arises because vortex columns shed by the foil create coherent negative-pressure corridors along the fish's lateral surface. Power reduction is realized when the trout's long-wavelength body wave remains phase-locked with the downstream advection of these vortex structures, enabling the fish to harvest pressure-induced thrust while minimizing added-mass losses. These findings provide a mechanistic explanation for wake exploitation in schooling fish, establish phase synchrony as a key control parameter for hydrodynamic benefit, and offer design guidelines for paired biomimetic underwater vehicles that seek to emulate schooling to improve propulsive efficiency.

Conventional rigid grippers remain the most-used robotic grippers in industrial assembly tasks. However, they are limited in their ability to handle a diverse range of objects. This study draws inspiration from nature to address these limitations, employing multidisciplinary methods, such as computer-aided design, parametric modeling, finite element analysis, 3D printing, and mechanical testing. Computational analysis of three distinct mandible morphs from the stag beetleCyclommatus mniszechirevealed that key geometric features-specifically mandible curvature and denticle arrangement-govern a functional trade-off between grasping ability and structural safety. This analysis identified a specific morphology optimized for superior grabbing performance, which served as the template for our design. Leveraging these biological principles, we used parametric modeling to design, and 3D printing to fabricate, a series of novel, mechanically intelligent grippers. Mechanical testing of these prototypes validated our design approach, demonstrating that specific modifications to curvature could significantly enhance the gripper's load-bearing capacity while minimizing object damage. This work establishes a clear pathway from biomechanical analysis to engineered application, offering a robust and cost-efficient blueprint for developing next-generation grippers that operate effectively without complex sensing or actuation systems for tasks in manufacturing, logistics, and healthcare.

Harbor seals possess a remarkable ability to detect hydrodynamic footprints left by moving objects, even long after the objects have passed, through interactions between wake flows and their uniquely shaped whiskers. While the flow-induced vibration of harbor seal whisker models has been extensively studied, their response to unsteady wakes generated by upstream moving bodies remains poorly understood. This study investigates the wake-induced vibration (WIV) of a flexibly mounted harbor seal-inspired whisker positioned downstream of a forced-oscillating circular cylinder, simulating the hydrodynamic footprint of a moving object. Unlike conventional WIV studies, where the upstream wake is passively formed behind a stationary body and governed solely by its geometry and flow speed, the upstream cylinder in this work undergoes prescribed oscillations. This approach enables independent control over the wake characteristics-such as wake width and shedding frequency-decoupling them from the physical attributes of the upstream source and allowing a more direct assessment of the whisker's sensing response to dynamic wake conditions. Experiments were conducted across a range of reduced velocities (U∗= 3.4-25) and Reynolds numbers (Re= 500-2700), with upstream oscillation frequencies varied from 0.5 to 2 times the natural frequency of the whisker. Volumetric particle tracking velocimetry (PTV) was used to characterize the flow field, complemented byQ-criterion and proper orthogonal decomposition analyses. Results show that while the whisker suppresses its own vortex-induced vibration in open flow, it oscillates strongly at the frequency of the upstream forcing when exposed to wake disturbances, demonstrating its capability to detect and respond to hydrodynamic trails of moving objects. These findings highlight the potential of harbor seal whisker-inspired designs for biomimetic underwater sensing and navigation systems.