Bioinspiration & Biomimetics最新文献

Flying insects rely mainly upon visual motion to detect and track objects. There has been a lot of research on fly inspired algorithms for object detection, but few have been developed based on visual motion alone. One of the daunting difficulties is that the neural and circuit mechanisms underlying the foreground-background segmentation are still unclear. Our previous modeling study proposed that the lobula held parallel pathways with distinct directional selectivity, each of which could retinotopically discriminate figures moving in its own preferred direction based on relative motion cues. The previous model, however, did not address how the multiple parallel pathways gave the only detection output at their common downstream. Since the preferred directions of the pathways along either horizontal or vertical axis were opposite to each other, the background moving in the opposite direction to an object also activated the corresponding lobula pathway. Indiscriminate or ungated projection from all the pathways to their downstream would mix objects with the moving background, making the previous model fail with non-stationary background. Here, we extend the previous model by proposing that the background motion-dependent gating of individual lobula projections is the key to object detection. Large-field lobula plate tangential cells are hypothesized to perform the gating to realize bioinspired background subtraction. The model is shown to be capable of implementing a robust detection of moving objects in video sequences with either a moving camera that induces translational optic flow or a static camera. The model sheds light on the potential of the concise fly algorithm in real-world applications.

Energy harvesting techniques can exploit even subtle passive motion like that of plant leaves in wind as a consequence of contact electrification of the leaf surface. The effect is strongly enhanced by artificial materials installed as 'artificial leaves' on the natural leaves creating a recurring mechanical contact and separation. However, this requires a controlled mechanical interaction between the biological and the artificial component during the complex wind motion. Here, we build and test four artificial leaf designs with varying flexibility and degrees of freedom across the blade operating onNerium oleanderplants. We evaluate the apparent contact area (up to 10 cm²per leaf), the leaves' motion, together with the generated voltage, current and charge in low wind speeds of up to 3.3 m s^-1and less. Single artificial leaves produced over 75 V and 1µA current peaks. Softer artificial leaves increase the contact area accessible for energy conversion, but a balance between softer and stiffer elements in the artificial blade is optimal to increase the frequency of contact-separation motion (here up to 10 Hz) for energy conversion also below 3.3 m s^-1. Moreover, we tested how multiple leaves operating collectively during continuous wind energy harvesting over several days achieve a root mean square power of ∼6µW and are capable to transfer ∼80µC every 30-40 min to power a wireless temperature and humidity sensor autonomously and recurrently. The results experimentally reveal design strategies for energy harvesters providing autonomous micro power sources in plant ecosystems for example for sensing in precision agriculture and remote environmental monitoring.

Gravitational forces can induce deviations in body posture from desired configurations in multi-legged arboreal robot locomotion with low leg stiffness, affecting the contact angle between the swing leg's end-effector and the climbing surface during the gait cycle. The relationship between desired and actual foot positions is investigated here in a leg-stiffness-enhanced model under external forces, focusing on the challenge of unreliable end-effector attachment on climbing surfaces in such robots. Inspired by the difference in ceiling attachment postures of dead and living geckos, feedforward compensation of the stance phase legs is the key to solving this problem. A feedforward gravity compensation (FGC) strategy, complemented by leg coordination, is proposed to correct gravity-influenced body posture and improve adhesion stability by reducing body inclination. The efficacy of this strategy is validated using a quadrupedal climbing robot, EF-I, as the experimental platform. Experimental validation on an inverted surface (ceiling walking) highlights the benefits of the FGC strategy, demonstrating its role in enhancing stability and ensuring reliable end-effector attachment without external assistance. In the experiment, robots without FGC only completed 3 out of 10 trials, while robots with FGC achieved a 100% success rate in the same trials. The speed was substantially greater with FGC, achieving 9.2 mm s^-1in the trot gait. This underscores the proposed potential of the FGC strategy in overcoming the challenges associated with inconsistent end-effector attachment in robots with low leg stiffness, thereby facilitating stable locomotion even at an inverted body attitude.

Biohybrid systems in which robotic lures interact with animals have become compelling tools for probing and identifying the mechanisms underlying collective animal behavior. One key challenge lies in the transfer of social interaction models from simulations to reality, using robotics to validate the modeling hypotheses. This challenge arises in bridging what we term the 'biomimicry gap', which is caused by imperfect robotic replicas, communication cues and physics constraints not incorporated in the simulations, that may elicit unrealistic behavioral responses in animals. In this work, we used a biomimetic lure of a rummy-nose tetra fish (Hemigrammus rhodostomus) and a neural network (NN) model for generating biomimetic social interactions. Through experiments with a biohybrid pair comprising a fish and the robotic lure, a pair of real fish, and simulations of pairs of fish, we demonstrate that our biohybrid system generates social interactions mirroring those of genuine fish pairs. Our analyses highlight that: 1) the lure and NN maintain minimal deviation in real-world interactions compared to simulations and fish-only experiments, 2) our NN controls the robot efficiently in real-time, and 3) a comprehensive validation is crucial to bridge the biomimicry gap, ensuring realistic biohybrid systems.

The diversity in butterfly morphology has attracted many people around the world since ancient times. Despite morphological diversity, the wing and body kinematics of butterflies have several common features. In the present study, we constructed a bottom-up butterfly model, whose morphology and kinematics are simplified while preserving the important features of butterflies. The present bottom-up butterfly model is composed of two trapezoidal wings and a rod-shaped body with a thorax and abdomen. Its wings are flapped downward in the downstroke and backward in the upstroke by changing the geometric angle of attack (AOA). The geometric AOA is determined by the thorax-pitch and wing-pitch angles. The thorax-pitch angle is actively controlled by abdominal undulation, and the wing-pitch angle is passively determined because of a rotary spring representing the basalar and subalar muscles connecting the wings and thorax. We investigated the effectiveness of abdominal undulation for thorax-pitch control and how wing-pitch flexibility affects aerodynamic-force generation and thorax-pitch control, through numerical simulations using the immersed boundary-lattice Boltzmann method. As a result, the thorax-pitch angle perfectly follows the desired angle through abdominal undulation. In addition, there is an optimal wing-pitch flexibility that maximizes the flying speed in both the forward and upward directions, but the effect of wing-pitch flexibility on thorax-pitch control is not significant. Finally, we compared the flight behavior of the present bottom-up butterfly model with that of an actual butterfly. It was found that the present model does not reproduce reasonable body kinematics but can provide reasonable aerodynamics in butterfly flights.

In order to enhance energy absorption, this study draws inspiration from the diagonal bilinear robust square lattice structure found in deep-sea glass sponges, proposing a design for thin-walled structures with superior folding capabilities and high strength-to-weight ratio. Firstly, the crashworthiness of bionic glass sponge tube (BGSTO) is compared with that of equal-wall-thickness equal-mass four-X tube through both experiments and simulations, and it is obtained that the specific energy absorption of BGSTO is increased by 78.64%. And the crashworthiness of BGSTO is also most significant compared to that of multicellular tubes with the similar number of crystalline cells. Additionally, we found that the double-line spacing of the glass sponge can be freely adjusted without changing the material amount. Therefore, based on BGSTO, we designed two other double-line structures, BGSTA and BGSTB. Then with equal wall thickness and mass as a prerequisite, this study proceeds to design and compare the energy absorption properties of three bilinear thin-walled tubes in both axial and lateral cases. The deformation modes and crashworthiness of the three types of tubes with variable bilinear spacing (β_O/A/B) are comparatively analysed. The improved complex proportional assessment (COPRAS) synthesis decision is used to obtain that BGSTO exhibits superior crashworthiness over the remaining two kinds of tubes. Finally, a surrogate model is established to perform multi-objective optimization on the optimal bilinear configuration BGSTO selected by the COPRAS method.

Shoulder joints determine the motion range of the upper limb. Thus, the compact and well-stiffened spherical parallel mechanism (SPM) has emerged as the mainstream shoulder prosthesis design approaches. However, the SPM's moving pairs of redundant motions impose excessive constraints that limit its workspace. Therefore, amplifying the workspace of the SPM to cover the motion range required by human daily activities is a pressing problem in shoulder prosthesis design. To address this challenge, this study proposed a workspace amplification approach through the kinematic analysis of a symmetrically arranged 2 degrees of freedom (DoFs) passive mechanism, together with the designed and optimized 3-RRR SPM, to construct an anthropomorphic shoulder. The effectiveness and reliability of the proposed mechanism was verified through thorough analyses. Simulation and experiment results demonstrated that the workspace amplification mechanism could significantly increase the maximum motion match index between the shoulder prosthesis and the daily workspace of the human shoulder from only 26.3% to 94.79%, solving the problem that the traditional SPM-based prostheses cannot satisfy the motion range required by daily activities. Moreover, the proposed mechanism has the potential to amplify the workspace of most parallel mechanisms with multiple DoFs after proper modification.

Swarm robots are frequently preferred for the exploration of harsh environments and search and rescue operations. This study explores the factors that influence the movement strategies of autonomous robot swarms and their impact on swarm distribution in the field, employing simulation-based analysis. The research consists of two parts: initially, robots undergo free-fall as passive entities, followed by a phase where they employ predefined movement strategies from their fall positions. The study aims to investigate how the initial position and related parameters affect movement characteristics and the ultimate swarm distribution. To achieve this objective, four parameters-radius, height, mass, and the Coefficient of Restitution-were identified, each assigned three different values. The study observes the effects of these parameters on robot motion, considering motion strategies such as Random Walk, Levy Walk, Markov Process, and Brownian Motion. Results indicate that increasing parameter values induce changes in the position values of the free-falling swarm in the first part, which is the initial position for the second part, influencing movement strategies in diverse ways. The outcomes are analyzed concerning the radial and angular spread of the robots. Radial spread measures how far swarm elements spread from their initial positions, while angular spread indicates how homogeneously the robots are distributed according to the polar angle. The study comprehensively investigates how the movement strategies of autonomous robot swarms are impacted by parameters and how these effects manifest in the results. The findings are anticipated to enhance the effective utilization of autonomous robot swarms in exploration missions.

Vertebrates possess a biomechanical structure with redundant muscles, enabling adaptability in uncertain and complex environments. Harnessing this inspiration, musculoskeletal systems offer advantages like variable stiffness and resilience to actuator failure and fatigue. Despite their potential, the complex structure presents modelling challenges that are difficult to explicitly formulate and control. This difficulty arises from the need for comprehensive knowledge of the musculoskeletal system, including details such as muscle arrangement, and fully accessible muscle and joint states. Whilst existing model-free methods do not need explicit formulations, they also underutilise the benefits of muscle redundancy. Consequently, they necessitate retraining in the event of muscle failure and require manual tuning of parameters to control joint stiffness limiting their applications under unknown payloads. Presented here is a model-free local inverse statics controller for musculoskeletal systems, employing a feedforward neural network trained on motor babbling data. Experiments with a musculoskeletal leg model showcase the controller's adaptability to complex structures, including mono and bi-articulate muscles. The controller can compensate for changes such as weight variations, muscle failures, and environmental interactions, retaining reasonable accuracy without the need for any additional retraining.

This study aims to investigate the feasibility of using an artificial lateral line (ALL) system for predicting the real-time position and pose of an undulating swimmer with Carangiform swimming patterns. We established a 3D computational fluid dynamics simulation to replicate the swimming dynamics of a freely swimming mackerel under various motion parameters, calculating the corresponding pressure fields. Using the simulated lateral line data, we trained an artificial neural network to predict the centroid coordinates and orientation of the swimmer. A comprehensive analysis was further conducted to explore the impact of sensor quantity, distribution, noise amplitude and sampling intervals of the ALL array on predicting performance. Additionally, to quantitatively assess the reliability of the localization network, we trained another neural network to evaluate error magnitudes for different input signals. These findings provide valuable insights for guiding future research on mutual sensing and schooling in underwater robotic fish.