Bioinspiration & Biomimetics最新文献

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.

The reef ecosystem plays a vital role as a habitat for fish species with limited swimming capabilities, serving not only as a sanctuary and food source but also influencing their behavioral tendencies. Understanding the intricate mechanism through which fish adeptly navigate the moving targets within reef environments within complex water flow, all while evading obstacles and maintaining stable postures, has remained a challenging and prominent subject in the realms of fish behavior, ecology, and biomimetics alike. An integrated simulation framework is used to investigate fish predation problems within intricate environments, combining deep reinforcement learning algorithms (DRL) with high-precision fluid-structure interaction numerical methods-immersed boundary lattice Boltzmann method (lB-LBM). The Soft Actor-Critic (SAC) algorithm is used to improve the intelligent fish's capacity for random exploration, tackling the multi-objective sparse reward challenge inherent in real-world scenarios. Additionally, a reward shaping method tailored to its action purposes has been developed, capable of capturing outcomes and trend characteristics effectively. The convergence and robustness advantages of the method elucidated in this paper are showcased through two case studies: one addressing fish capturing randomly moving targets in hydrostatic flow field, and the other focusing on fish counter-current foraging in reef environments to capture drifting food. A comprehensive analysis was conducted of the influence and significance of various reward types on the decision-making processes of intelligent fish within intricate environments.

This smooth flat experimental study investigates the capability of mako shark scales to control flow separation when placed downstream of the onset of turbulent boundary layer separation and within the reattachment region. The objective of the study is to validate the hypothesis that the shark scales' bristling and recoiling would prevent the flow separation on the flank region (the fastest flow region) of the shark. A rotating cylinder was used to induce an adverse pressure gradient over a flat plate to produce a region of separated flow where the shark skin specimen was mounted. Two types of mako shark scales (flank (B2) and between flank and dorsal fin (B1)) were positioned in the preferred flow direction on a flat plate. The B2 scales are slender, 200μm tall, and can bristle up to 50°. In contrast, B1 scales are wider, shorter, and can bristle at 30°. The bristling angle and shape are the main mechanisms by which the scales act to inhibit flow from moving upstream near the wall. Thus, the difference in the bristling angles and structures of the scales is attributed to the fact that the B2 scales function in a thicker boundary layer (behind the shark's gills) where they must bristle sufficiently high into the boundary layer to control the flow separation, and because the adverse pressure gradient in this region is higher where flow separation is more likely. The scales are placed in the reattachment region to elucidate their ability to control and reattach an already separated turbulent flow. The results show that B2 scales placed in the reattachment region reduce the size of the turbulent separation bubble and decrease the turbulent kinetic energy, while B1 scales have the opposite effect.

Despite progress developing experimentally-consistent models of insect in-flight sensing and feedback for individual agents, a lack of systematic understanding of the multi-agent and group performance of the resulting bio-inspired sensing and feedback approaches remains a barrier to robotic swarm implementations. This study introduces the small-target motion reactive (STMR) swarming approach by designing a concise engineering model of the small target motion detector (STMD) neurons found in insect lobula complexes. The STMD neuron model identifies the bearing angle at which peak optic flow magnitude occurs, and this angle is used to design an output feedback switched control system. A theoretical stability analysis provides bi-agent stability and state boundedness in group contexts. The approach is simulated and implemented on ground vehicles for validation and behavioral studies. The results indicate despite having the lowest connectivity of contemporary approaches (each agent instantaneously regards only a single neighbor), STMR achieves collective group motion. STMR group level metric analysis also highlights continuously varying polarization and decreasing heading variance.

Legged robots have received widespread attention in academia and engineering owing to their excellent terrain adaptability. However, most legged robots can only adapt to high-hardness environments instead of flexible environments. Expanding the motion range of legged robots to water is a promising but challenging work. Inspired by basilisk lizards which can run on water surfaces by feet, this paper proposes a bipedal robot for water running by hydrodynamics instead of buoyancy. According to the motion parameters of the basilisk lizard during water running, a single-degree of freedom bipedal mechanism is proposed to reproduce the motion trajectory of the feet of the basilisk lizard. Scale optimization is conducted by a particle swarm optimization algorithm to determine the geometrical parameters of the mechanism. The effects of motion frequency and foot area on mechanism performance are studied and the optimal solutions are determined by the maximum single-cycle lift impulse through numerical calculations. A bipedal water running robot prototype was fabricated, and the experimental results show that the prototype can generate enough support for the robot running on the water by providing a maximum lift of 2.4 times its weight (160 g) and reaching a horizontal forward speed range of 0.3-0.8 m s^-1, compared with the basilisk lizard weighs 2-200 g, generates a lift impulse that is 111%-225% of its body weight, and moves at a speed of 1.3 ± 0.1 m s^-1.

In traditional hydraulic robotics, actuators must be sized for the highest possible load, resulting in significant energy losses when operating in lower force regimes. Variable recruitment fluidic artificial muscle (FAM) bundles offer a novel bio-inspired solution to this problem. Divided into individual MUs, each with its own control valve, a variable recruitment FAM bundle uses a switching control scheme to selectively bring MUs online according to load demand. To date, every dynamic variable recruitment study in the literature has considered homogeneous bundles containing MUs of equal size. However, natural mammalian muscle MUs are heterogeneous and primarily operate based on Henneman's size principle, which states that MUs are recruited from smallest to largest for a given task. Is it better for a FAM variable recruitment bundle to operate according to this principle, or are there other recruitment orders that result in better performance? What are the appropriate criteria for switching between recruitment states for these different recruitment orders? This paper seeks to answer these questions by performing two case studies exploring different bundle MU size distributions, analyzing the tradeoffs between tracking performance and energetics, and determining how these tradeoffs are affected by different MU recruitment order and recruitment state transition thresholds. The only difference between the two test cases is the overall force capacity (i.e. total size) of the bundle. For each test case, a Pareto frontier for different MU size distributions, recruitment orders, and recruitment state transition thresholds is constructed. The results show that there is a complex relationship between overall bundle size, MU size distributions, recruitment orders, and recruitment state transition thresholds corresponding to the best tradeoffs change along the Pareto frontier. Overall, these two case studies validate the use of Henneman's Size Principle as a variable recruitment strategy, but also demonstrate that it should not be the only variable recruitment method considered. They also motivate the need for a more complex variable recruitment scheme that dynamically changes the recruitment state transition threshold and recruitment order based on loading conditions and known system states, along with a co-design problem that optimizes total bundle size and MU size distribution.