Bioinspiration & Biomimetics最新文献

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, machine learning, multi-objective optimization, and global sensitivity analysis, 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.

Birds utilize feathered wings where individual feathers serve as distinct control surfaces that deform locally under the effect of aerodynamic forces and introduce complex interaction effects. The role of these effects in improving lift generation remains unclear. To investigate this, we analyze a feather-inspired control surface of a flapping foil, composed of three pitching and heaving rigid membranes (referred to as feathers) designed to enhance lift in flapping flight. Two-dimensional numerical simulations are conducted at a Reynolds number of 5000, evaluating the performance of the proposed control surface at three Strouhal numbers (St=0.08,0.12, and 0.2), representative of small bird flight conditions. Our results show that specific combinations of feather lengths maximize the lift-to-power ratio for each Strouhal number. The best-performing cases generate up to twice the mean lift force of a single feather for the same power expenditure. AtSt = 0.12, varying the heave amplitude has minor effects on the peak feather performance. While the upstroke (downstroke) generally produces negative (positive) lift, performance gains are primarily driven by minimizing negative lift during the upstroke. We also quantify the inter-feather interaction effects, which are more pronounced at higher Strouhal numbers. The proposed control surface may be useful in developing efficient micro- and unmanned aerial vehicles.

Environmental perception is a crucial foundation for enhancing the application potential of biomimetic robots. Motivated by the complementary roles of visual and tactile sensing observed in rats, this work proposes a visual-tactile perception for a small-scale bio-inspired robotic rat. The method leverages binocular vision to estimate depth images through an attention-based network and improve perception and localization accuracy by 14.22% based on a dynamic objects removal module. Besides, the whisker sensor is applied to enhance the robot's ability to identify object contours and environmental boundaries in narrow spaces, with obstacle contour and environment boundary reconstruction goodness of fit exceeding 97.00% and 93.87%, respectively. In addition, by integrating the above individual perception methods, we achieve the fusion of vision and tactile sensing for complex environment perception. To the best of our knowledge, this is the first study to implement vision-tactile fusion perception on a miniature biomimetic robot through physical experiments. The experiments demonstrate that our method exhibits promising results on the robotic rat, reducing localization errors in narrow and dim scenes by an average of 29.14% compared to existing state-of-the-art methods.

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.

Soft-body grippers are highly pliable due to the use of flexible materials, which enable safe grasping of objects, but their load-bearing capacity is limited by the mechanical properties of the materials themselves. To address the problem of insufficient stiffness of traditional soft-body grippers, this paper proposes a reconfigurable exoskeleton bionic stiff-flexible gripper inspired by the hydraulic legs of spiders. The gripper is designed with three switchable working modes, and the quick-connect removal mechanism works in concert with the lower fixed jaws to adapt to the grasping needs of objects of different sizes and masses. The joints adopt balloon actuators that mimic the hydraulic leg mechanism of a spider, which can realize adaptive adjustment of the center position and drive the exoskeleton structure to complete the deformation action. Based on the contact-induced extrusion interaction between the balloon and the exoskeleton, a theoretical model of nonlinear actuation is developed. The balloon is simulated using finite element analysis to determine its effective operating range. Furthermore, a rectangular silicone membrane is designed to envelop the exoskeleton surface, enhancing the system's flexibility and ensuring consistent structural support. The experimental evaluation of the comprehensive grasping performance of the gripper shows that the reconfigurable exoskeleton gripper can effectively grasp objects in the size range of 3-230 mm with a maximum weight of 1 kg, which significantly extends the application range and load-bearing capacity of the traditional soft gripper.

Micro air vehicles (MAVs) 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 MAVs (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 emerging beyond-natural designs combining biological principles with engineered innovations. Finally, research frontiers in passive mechanisms applications are discussed, whose implementation will help to expand FWMAVs' operational envelopes and enhance mission versatility.

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 (CF) 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 CF, a crank length of 0.28Lsignificantly 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. IncreasedRestrengthens vortex interactions, whileStaffects wake structures. These findings offer theoretical insights for the optimized design of efficient, hybrid-driven robotic fish based on crank-linkage propulsion systems.

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 revolutionize 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 to be 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 motifs 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 behavior.

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.