Bioinspiration & Biomimetics最新文献

Natural strategies for structural transition during metamorphosis display remarkable efficiency and mechanical sophistication. In butterflies, the transition from pupa to adult involves not only dramatic morphological transformation but also a finely tuned mechanical breakthrough. In this combined experimental and theoretical investigation, we report a pre-cracked fracture mechanism in the butterflyIdea leuconoe, where the adult butterfly exits through a structural weak link-referred to as a suture-embedded within the pupal shell. This suture functions as a precracked line, enabling a rapid and well controlled rupture during eclosion. First, we observed that the butterfly's legs, strategically oriented inside the pupal cavity, engage with the inner wall of the pupal shell to form a natural force amplification system. Acting in a lever-like configuration, small muscular forces are converted into substantial opening torques, allowing a rapid emergence. Second, we validated that the inversed-V-shaped pupal sutures facilitates concentration of stress along the suture direction, thereby promoting crack initiation and propagation. Combining time-lapse video recordings, anatomical observations, and mechanical modeling, we revealed that this system achieves both mechanical efficiency and controllability during eclosion. Beyond shedding light on a biomechanical secret of insect development, these insights may inspire robotic systems with embedded fracture-guiding protective shells.

Inspired by the actuation mechanism of octopus tentacles, this study proposes a pneumatic bio-inspired soft actuator with variable stiffness to improve load-bearing capability and manipulation performance while enabling flexible stiffness control. The designed actuator exhibits multi-modal deformation capabilities, such as elongation, bending, and circumferential deflection. To establish a theoretical framework for structural optimization, numerical simulations were carried out to investigate the influence of chamber geometry, wall thickness, and length on the actuator's behavior. A deformation analysis model was developed utilizing the Yeoh hyperelastic constitutive model and the moment equilibrium principle to characterize the correlation between input pressure and the resulting bending angle and elongation. Furthermore, a variable stiffness model was formulated using the pseudo-rigid-body model approach rooted in energy equivalence. By synthesizing material properties with discrete kinematic mechanisms, the mapping between system stiffness and actuation pressure was identified. Finally, prototypes were manufactured via rapid prototyping, and a custom experimental platform was built for validation. Experimental data confirmed the validity of both the static and variable-stiffness models. The proposed method achieved a 40% increase in stiffness and a 23.59% enhancement in horizontal contact force, thereby validating the practicality and efficacy of the pneumatic soft actuator. The strategies and findings detailed herein offer significant insights for the development of pneumatic and hydraulic soft robotics.

Stiffness exerts a significant influence on swimming locomotion of fish. Biological experiments have demonstrated that the stiffness of different body segments in fish is inherently heterogeneous. Although the stiffness variation patterns from head to tail exhibit certain interspecific differences among various fish species, they generally follow a decreasing trend from anterior to posterior. In this study, based on the resistive drag model, we discretized the original model and incorporated improvements such as the distribution function of active bending moments. The analytical model developed herein integrates the stiffness distribution of the fish body into the analysis of its locomotion. Through this model, several typical stiffness distribution patterns were investigated, with a particular focus on sub-topics such as various decreasing distributions and the effects of different segment quantities. The results indicate that a rapidly decreasing stiffness distribution with a low ratio of minimum-to-maximum stiffness yields the optimal swimming performance. This work serves not as a substitute for but rather a supplement to pertinent biological experiments. Simultaneously, it constitutes a foundational study for variable-stiffness distribution robotic fish, informing and guiding future design endeavors.

Animals and humans rely on optic flow to navigate cluttered and unknown environments. While most previous studies have focused on how organisms achieve self-motion perception through optic flow information, biological neural networks for navigation based on optic flow remain unexplored. Here, we propose a biologically plausible neural network model for optic flow-based reactive navigation. The model incorporates a primary visual cortex, which is responsible for generating a cortical representation of the optic flow field; a higher-order cortex, which calculates the focus of expansion (FOE) of the optic flow field; and a cerebellum, which generates motor commands. A feedback inhibitory pathway from V1 layer VI to layer IV is introduced, enhancing heading sensitivity and enabling rapid adaptation in dynamic environments. To achieve precise obstacle localization, we propose a dual encoding strategy that combines optic flow with depth maps derived from the optic flow field, FOE, and control acceleration. This strategy mitigates distortions in depth estimation near the expansion center and ensures more reliable obstacle representation. The cerebellum outputs motor commands for heading direction and speed control based on the output of the visual cortex. Simulations and real-world experiments with an intelligent vehicle confirm that the proposed model enables collision-free navigation across diverse scenarios and outperforms classical optic flow balance strategies in complex environments. These findings demonstrate that biologically inspired neural networks provide a feasible solution for visual reactive navigation in autonomous agents.

We examine fire-ant rafts as a model system of biological active matter composed of cohesive agents that interact through simple local rules to produce emergent collective dynamics. A hallmark of these rafts is treadmilling, a process enabled by the continuous cycling of ants through a multi-phase system, comprising in a minimal representation a solid-like network phase and a dilute motile phase that can migrate outside the network. Treadmilling requires the breaking of detailed balance in the fluxes between the phases, a signature of out-of-equilibrium systems. By combining experimental data with discrete agent-based simulations and a new continuum model, we show that simple rules governing the actions of single ants, based only on the positions, velocities and local forces an ant perceives and defining its next actions within a phase and triggering conditions for transition between phases, suffice to replicate the complex behavior of treadmilling and shape morphing of the raft as emergent phenomena. We also show that two principles hold empirically in the network phase: homeostasis of area density, a constraint that couples ant activity level to shape morphing in a very simple way; and the invariance of the network topology over relevant timescales, which supports global geometrical stability in the face of chaotic ant motions. Refined by evolution over very long times, the principles and rules governing fire ant rafts suggest design possibilities for achieving stable shape morphing in decentralized systems of synthetic programmable matter.

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.

This paper presents a polycentric pneumatic semi-active knee (SAK) based on a flexible soft bladder and an irregular four-bar linkage mechanism. The SAK innovatively employs the flexible soft bladder to drive the irregular four-bar linkage mechanism, providing active torque for knee extension. Additionally, an elastic recovery device is designed to provide return torque for knee flexion. In active mode, an air pump inflates the bladder to drive the four-bar linkage mechanism for active extension, while the elastic recovery device stores elastic potential energy. In passive mode, the stored elastic potential energy is released to provide recovery torque for the passive flexion. Furthermore, this paper analyzes the driving characteristics of the knee actuator using the principle of virtual work, and designs a lower limb test platform to measure its active and recovery torque with a more accurate torque measurement method that can eliminate the influence of the knee actuator's weight on the measurement results. Experimental results demonstrate that, the measured output torque closely aligns with the calculated values within a certain angular range, and the recovery torque provided by the elastic recovery device configured with a single elastic band also achieved the expected performance, with the maximum recovery torque reaching 0.56 Nm. The SAK proposed in this paper not only features an instantaneous center of rotation trajectory that aligns with the human knee joint, leading to better coordination between the knee prosthesis and the human body, but also offers advantages such as low cost, compact structure, and monotonic control.

The field of soft robotics has shown unprecedented growth in research efforts, scientific achievements, and technological advancements. Bioinspiration and biomimetics have played an instrumental role in the birth and growth of soft robotics. What is next for this field? To promote soft robotics research to the next level and have a broader impact in robotics and engineering fields, in this roadmap, we argue that two research directions should be strengthened (i) more structured, formal methods and tools for designing and developing soft robots and bioinspired robots (ii) more concrete applications of bioinspired soft robots in diverse sectors of human activities. This article provides a roadmap for the design of bioinspired soft robots, the integration of soft robot systems, and their applications in industry and services. Scientists and experts describe the state-of-the art and the perspectives of bioinspired, model-informed design of soft robots, outlining the challenges in developing complex soft robotic systems, and applications of soft robots in diverse fields.

Primary flight feather shafts, a critical structural component of avian flight, exhibit excellent mechanical properties. The cross-sectional and medullary foam internal cavity structures of the feather shaft exhibit a gradual variation along the shaft; however, the mechanism by which this gradual variation influences the mechanical properties of the shaft remains unclear. In this study, the structural characteristics of a primary flight feather shaft were analyzed. Subsequently, the effects of gradual variations in the cross-sectional shape and medullary foam internal cavity structure along the shaft on its buckling resistance, torsional stiffness, and bending behavior were investigated. The experimental results showed that, along the length of the primary flight feather shaft, its cross-sectional shape transitions progressively from circular to approximately pentagonal and finally to quadrilateral, while its medullary foam cavity structure gradually changes from a circular to an inverted triangular shape. Feather shafts with an approximately pentagonal cross-section and an elliptical medullary foam cavity structure exhibit excellent buckling resistance, torsional resistance, and bending stability. Finally, based on the structural characteristics of the feather shaft, bionic samples with different cross-sectional shapes and medullary foam cavity structures were fabricated using fused deposition modeling (FDM), and their bending properties were assessed through three-point bending tests. The experimental results demonstrated that the bioinspired prototype, featuring an approximately pentagonal cross-section and an elliptical medullary foam cavity structure exhibited optimal bending properties, achieving a maximum specific load-bearing capacity of 102.64±1.66 N/g. This study provides bio-inspired insights into the design of lightweight structures.

Aquatic animals and underwater robots operate in a complex physical world and must coordinate their bodies to achieve behavioral objectives such as navigation and predation. With recent developments in deep reinforcement learning (RL), it is now possible for scientists and engineers to synthesize sensorimotor strategies (policies) for specific tasks using physically simulated bodies and environments. However, beyond solving individual control problems, these methods offer an exciting framework for understanding the organization of an animal sensorimotor system in connection with its morphology and physical interaction with the environment, as well as for deriving general design rules for bioinspired underwater robots. Although algorithms and code implementing both learning agents and environments are increasingly available, the basic assumptions and modeling choices that go into the formulation of an embodied feedback control problem using deep reinforcement learning may not be immediately apparent. In this tutorial, we provide a self-contained introduction to model-free reinforcement learning for embodied agents in underwater environments, with a focus on actor-critic methods. We first present the mathematical formulation of RL, highlighting where physical modeling choices enter. We then discuss the practical aspects of implementing actor-critic algorithms. Drawing on recent examples of RL-controlled swimmers, we provide guidelines for choosing observations, actions, and rewards consistent with biological behavior, and we outline how RL can be used as a tool to explore hypotheses about the feedback control underlying animal and robotic behavior.