Bioinspiration & Biomimetics最新文献

Soft actuators made of soft materials cannot generate precisely efficient output forces compared to rigid actuators. It is a promising strategy to equip soft actuators with variable stiffness modules of layer jamming mechanism, which could increase their stiffness as needed. Inspired by the gecko's the array of setae, bionic adhesive flaps with inclined micropillars are applied in layer jamming mechanism. In this paper, after the manufacturing process of the layer jamming actuator based on the bionic adhesive flaps is described, the equivalent stiffness models of the whole actuator are established in the unjammed and jammed states. And the shear adhesive force of a single micropillar is calculated based on the Kendall viscoelastic band model. The finite element simulation results of two bionic adhesive flaps show that the interlaminar shear stress and stiffness increase with the increase of pressure. The measurement of shear adhesive force show that the critical shear adhesive force of the bionic adhesive material is 3.2 times that of polyethylene terephthalate (PET) material, and exhibit the ability of anisotropic adhesion behavior. The variable stiffness performance of the layer jamming actuator based on bionic adhesive flaps is evaluated by three test methods, and the max stiffness reaches 8.027 N mm^-1, which is 1.5 times higher than the stiffness of the layer jamming actuator based on the PET flaps. All results of simulation and experiment effectively verify the validity and superiority of applying the bionic adhesive flaps to the layer jamming mechanism to enhance the stiffness.

In order to adapt to complex and changing environments, animals have a wide variety of locomotor forms, which has inspired the investigation of their deformation and driving mechanisms. In this paper, we propose a computational design method for muscle-driven soft robots to satisfy desired deformations, aiming to mimic the deformation behavior of muscle-driven animals in nature. In this paper, we generate the ideal muscle-driven layout for the soft robot by inputting an initial shape and a desired shape, so that it can closely achieve the desired deformation. The material point method is utilized to simulate the soft medium so as to achieve the effect of coupling and coordinated deformation of arbitrary shapes. Our method efficiently searches for muscle layouts corresponding to various deformations and realizes the deformation behaviors of a variety of bio-inspired robots, including soft robots such as bionic snakes, frogs, and human faces. Experimental results show that for both the bionic snake and frog soft robots, the overlap of the geometric contour regions between the actual and simulated deformations is more than 90%, which validates the effectiveness of the method. In addition, the global muscle distributions of the bionic snake and human face soft robots during motion are generated and validated by effective simulation.

In this paper, the innovative design of a robotic hand with soft jointed structure is carried out and a tendon-driven mechanism, a master-slave motor coordinated drive mechanism, a thumb coupling transmission mechanism and a thumb steering mechanism are proposed. These innovative designs allow for more effective actuation in each finger, enhancing the load capacity of the robotic hand while maintaining key performance indicators such as dexterity and adaptability. A mechanical model of the robotic finger was made to determine the application limitations and load capacity. The robotic hand was then prototyped for a set of experiments. The experimental results showed that the proposed theoretical model were reliable. Also, the fingertip force of the robotic finger could reach up to 10.3 N, and the load force could reach up to 72.8 N. When grasping target objects of different sizes and shapes, the robotic hand was able to perform the various power grasping and precision grasping in the Cutkosky taxonomy. Moreover, the robotic hand had good flexibility and adaptability by means of adjusting the envelope state autonomously.

Social infrastructure networks, essential for daily life and economic activities, encompass utilities such as water, electricity, roads, and telecommunications. Dynamic remodeling of these systems is crucial for responding to continuous changes, unexpected events, and increased demand. This study proposes a new dynamic remodeling model inspired by biological mechanisms, focusing on a model based on the chemotaxis of slime molds. Slime molds adapt spontaneously to environmental changes by remodeling through the growth and degeneration of tubes. This capability can be applied to optimizing and dynamic remodeling social infrastructure networks. This study elucidated the chemotactic response characteristics of slime molds using biological experiments. The mold's response was observed by considering changes in the concentration of chemicals as environmental changes, confirming that slime molds adapt to environmental changes by shortening their periodic cycles. Subsequently, based on this dynamic response, we propose a new dynamic model (oscillated Physarum solver, O-PS) that extends the existing Physarum solver (PS). Numerical simulations demonstrated that the O-PS possesses rapid and efficient path-remodeling capabilities. In particular, within a simplified maze network, the O-PS was confirmed to have the same shortest-path searching ability as the PS, while being capable of faster remodeling. This study offers a new approach for optimizing and dynamically remodeling social infrastructure networks by mimicking biological mechanisms, enabling the rapid identification of solutions considering multiple objectives under complex constraints. Furthermore, the variation in convergence speed with oscillation frequency in the O-PS suggests flexibility in responding to environmental changes. Further research is required to develop more effective remodeling strategies.

The hard suckers commonly used in surgical operations often cause adsorption extrusion damage to the biological tissue. To tackle this problem, from the perspective of bionics, through in-depth observation and research on the special sucker adsorption process and adsorption mechanism of hypostomus plecostomus (HP), this paper proposes a bionic soft hypostomus plecostomus sucker (BSHPS) with a variable stiffness gradient structure with a good adsorption performance on soft moist irregular biological tissues. The BSHPS comprises a lip disc, a pre-valvular cavity, and a post-valvular cavity. Through the volume changes of the pre- and post-valvular cavities, a pressure difference is generated between the inside and outside of the sucker, enabling the lip disc to remain sealed. The experiments were carried out by an automatic tensile force measurement system equipped with a vacuum pump, and the results showed that in slippery environment, the adsorption performance of the BSHPS is improved by a maximum of 61.9% compared to that in dry environment. On a biological tissue surface, the adsorption force is as high as 13.7765 N. The most important advantage of the proposed BSHPS is that it can be firmly adsorbed the surface of soft moist irregular biological tissues, effectively slowing down or avoiding adsorption extrusion damage to the biological tissue. Therefore, the BSHPS is expected to have good application prospects in modern surgical medicine.

Micro-sensors, such as pressure and flow sensors, are usually adopted to attain actual fluid information around swimming biomimetic robotic fish for hydrodynamic analysis and control. However, most of the reported micro-sensors are mounted discretely on body surfaces of robotic fish and it is impossible to analyzed the hydrodynamics between the caudal fin and the fluid. In this work, a biomimetic caudal fin integrated with a resistive pressure sensor is designed and fabricated by laser machined conductive carbon fibre composites. To analyze the pressure exerted on the caudal fin during underwater oscillation, the pressure on the caudal fin is measured under different oscillating frequencies and angles. Then a model developed from Bernoulli equation indicates that the maximum pressure difference is linear to the quadratic power of the oscillating frequency and the maximum oscillating angle. The fluid disturbance generated by caudal fin oscillating increases with an increase of oscillating frequency, resulting in the decrease of the efficiency of converting the kinetic energy of the caudal fin oscillation into the pressure difference on both sides of the caudal fin. However, perhaps due to the longer stability time of the disturbed fluid, this conversion efficiency increases with the increase of the maximum oscillating angle. Additionally, the pressure variation of the caudal fin oscillating with continuous different oscillating angles is also demonstrated to be detected effectively. It is suggested that the caudal fin integrated with the pressure sensor could be used for sensing thein situflow field in real time and analyzing the hydrodynamics of biomimetic robotic fish.

Nature abounds with examples of ultra-sensitive perception and agile body transformation for highly efficient predation as well as extraordinary adaptation to complex environments. Flytraps, as a representative example, could effectively detect the most minute physical stimulation of insects and respond instantly, inspiring numerous robotic designs and applications. However, current robotic flytraps face challenges in reproducing the ultra-sensitive insect-touch perception. In addition, fast and fully-covered capture of live insects with robotic flytraps remains elusive. Here we report a novel design of a robotic flytrap with an ultra-sensitive 'trichome' and bistable fast-response 'lobes'. Our results show that the 'trichome' of the proposed robotic flytrap could detect and respond to both the external stimulation of 0.45 mN and a tiny touch of a flying bee with a weight of 0.12 g. Besides, once the 'trichome' is triggered, the bistable 'lobes' could instantly close themselves in 0.2 s to form a fully-covered cage to trap the bees, and reopen to set them free after the tests. We introduce the design, modeling, optimization, and verification of the robotic flytrap, and envision broader applications of this technology in ultra-sensitive perception, fast-response grasping, and biomedical engineering studies.

This paper broadly summarizes the variation of design features found in vertebrate limbs and analyses the resultant versatility and multifunctionality in order to make recommendations for bioinspired robotics. The vertebrate limb pattern (e.g. shoulder, elbow, wrist and digits) has been proven to be very successful in many different applications in the animal kingdom. However, the actual level of optimality of the limb for each animal application is not clear because for some cases (e.g. whale flippers and bird wings), the basic skeletal layout is assumed to be highly constrained by evolutionary ancestry. This paper addresses this important and fundamental question of optimality by analysing six limbs with contrasting functions: human arm, whale flipper, bird wing, human leg, feline hindlimb and frog hindlimb. A central finding of this study is that the vertebrate limb pattern is highly versatile and optimal not just for arms and legs but also for flippers and wings. One key design feature of the vertebrate limb pattern is that of networks of segmented bones that enable smooth morphing of shapes as well as multifunctioning structures. Another key design feature is that of linkage mechanisms that fine-tune motions and mechanical advantage. A total of 52 biomechanical design features of the vertebrate limb are identified and tabulated for these applications. These tables can be a helpful reference for designers of bioinspired robotic and prosthetic limbs. The vertebrate limb has significant potential for the bioinspired design of robotic and prosthetic limbs, especially because of progress in the development of soft actuators.

Pigeons' unexpected competence in learning to categorize unseen histopathological images has remained an unexplained discovery for almost a decade (Levensonet al2015PLoS One10e0141357). Could it be that knowledge transferred from their bird's-eye views of the earth's surface gleaned during flight contributes to this ability? Employing a simulation-based verification strategy, we recapitulate this biological phenomenon with a machine-learning analog. We model pigeons' visual experience during flight with the self-supervised pre-training of a deep neural network on BirdsEyeViewNet; our large-scale aerial imagery dataset. As an analog of the differential food reinforcement performed in Levensonet al's study 2015PLoS One10e0141357), we apply transfer learning from this pre-trained model to the same Hematoxylin and Eosin (H&E) histopathology and radiology images and tasks that the pigeons were trained and tested on. The study demonstrates that pre-training neural networks with bird's-eye view data results in close agreement with pigeons' performance. These results support transfer learning as a reasonable computational model of pigeon representation learning. This is further validated with six large-scale downstream classification tasks using H&E stained whole slide image datasets representing diverse cancer types.

Membrane technology advancements within the past twenty years have provided a new perspective on environmentalism as engineers design membranes to separate greenhouse gases from the environment. Several scientific journals have published articles of experimental evidence quantifying carbon dioxide (CO₂), a common greenhouse gas, separation using membrane technology and ranking them against one another. On the other hand, natural systems such as the respiratory system of mammals also accomplish transmembrane transport of CO₂. However, to our knowledge, a comparison of these natural organic systems with engineered membranes has not yet been accomplished. The tracheal respiratory systems of insects transport CO₂at the highest rates in the animal kingdom. Therefore, this work compares engineered membranes to the tracheal systems of insects by quantitatively comparing greenhouse gas conductance rates. We demonstrate that on a per unit volume basis, locusts can transport CO₂approximately ∼100 times more effectively than the best current engineered systems. Given the same temperature conditions, insect tracheal systems transport CO₂three orders of magnitude faster on average. Miniaturization of CO₂capture systems based on insect tracheal system design has great potential for reducing cost and improving the capacities of industrial CO₂capture.