Biomimetics最新文献

Background: The use of ex vivo assays associated with biomaterials may allow the short-term visualization of a specific cell type response inserted in a local microenvironment. Blood is the first component to come into contact with biomaterials, providing blood clot formation, being substantial in new tissue formation. Thus, this research investigated the physiological blood clot (PhC) patterns formed in 3D scaffolds (SCAs), based on chitosan and 20% beta-tricalcium phosphate and its effect on osteogenesis. Initially, SCA were inserted for 16 h in rats calvaria defects, and, after that, osteoblasts cells (OSB; UMR-106 lineage) were seeded on the substrate formed. The groups tested were SCA + OSB and SCA + PhC + OSB. Cell viability was checked by MTT and mineralized matrix formation in OSB using alizarin red (ARS). The alkaline phosphatase (ALP) and bone sialoprotein (BSP) expression in OSB was investigated by indirect immunofluorescence (IF). The OSB and PhC morphology was verified by scanning electron microscopy (SEM).

Results: The SCA + PhC + OSB group showed greater cell viability (p = 0.0169). After 10 days, there was more mineralized matrix deposition (p = 0.0365) and high ALP immunostaining (p = 0.0021) in the SCA + OSB group. In contrast, BSP was more expressed in OSB seeded on SCA with PhC (p = 0.0033).

Conclusions: These findings show the feasibility of using PhC in ex vivo assays. Additionally, its inclusion in the experiments resulted in a change in OSB behavior when compared to in vitro assays. This "closer to nature" environment can completely change the scenario of a study.

In this paper, a deep reinforcement learning (DRL) approach based on generative adversarial imitation learning (GAIL) and long short-term memory (LSTM) is proposed to resolve tracking control problems for robotic manipulators with saturation constraints and random disturbances, without learning the dynamic and kinematic model of the manipulator. Specifically, it limits the torque and joint angle to a certain range. Firstly, in order to cope with the instability problem during training and obtain a stability policy, soft actor-critic (SAC) and LSTM are combined. The changing trends of joint position over time are more comprehensively captured and understood by employing an LSTM architecture designed for robotic manipulator systems, thereby reducing instability during the training of robotic manipulators for tracking control tasks. Secondly, the obtained policy by SAC-LSTM is used as expert data for GAIL to learn a better control policy. This SAC-LSTM-GAIL (SL-GAIL) algorithm does not need to spend time exploring unknown environments and directly learns the control strategy from stable expert data. Finally, it is demonstrated by the simulation results that the end effector of the robot tracking task is effectively accomplished by the proposed SL-GAIL algorithm, and more superior stability is exhibited in a test environment with interference compared with other algorithms.

This work addresses the position tracking control design of the stator vane driven by electro-hydrostatic actuators facing uncertain aerodynamic disturbances. Rapidly changing aerodynamic conditions impose complex disturbance torques on the guide vanes. Consequently, a challenging task is to enhance control precision in complex uncertain environments. Inspired by the principles of mammalian muscle movement, a novel robust control strategy based on the backstepping method has been proposed. Using backstepping, virtual rotational speed and virtual pressure difference force are designed, which decompose the high-order position closed-loop control problem into three lower-order parts, eliminating the need for matching conditions. Subsequently, robust controllers were designed, and stability proofs and performance analyses of the controllers were provided. This control strategy was tested through numerical hydraulic simulation. The results show that compared to other control methods, this approach significantly improves tracking accuracy and robustness. Therefore, it is believed that this method has the potential to become a new generation solution for such problems.

This study investigates the use of Brewers' Spent Grains (BSGs) as a sustainable biocomposite building materials, using cornstarch as a biopolymer binder. BSG aggregates are compared with hemp shives, a conventional aggregate known for its thermal properties. Starch is employed as a natural binder in three different formulations to further reduce the carbon footprint of the building material. Considering aggregates, the first formulation contains only BSGs, the second consists of half BSGs and half hemp shives, and the third uses only hemp shives. In addition, morphological analysis using Scanning Electron Microscopy (SEM) is conducted to examine the microstructure and porosity of the raw BSG and hemp shives. Hygrothermal properties are measured using Heat Flow Meter (HFM) and Dynamic Vapor Sorption (DVS) techniques, while mechanical properties are also assessed. Results indicate that the thermal conductivity of the BSG formulation (0.131 W/(m·K)) is double that of the hemp shives formulation (0.067 W/(m·K)), whereas the mixed BSG/hemp shives formulation exhibits a thermal conductivity of 0.106 W/(m·K). However, DVS measurements reveal better hygrothermal properties for the BSG formulation compared to the hemp shives formulation. Lastly, mechanical properties are found to be nearly equivalent across the three formulations. These findings suggest that BSG waste has potential as a viable material for use in construction. Further work on formulation optimization and durability is necessary to fully realize the potential of this waste in promoting a circular economy within the building materials industry.

In recent years, bioinspired insect flight has become a prominent research area, with a particular focus on beetle-inspired aerial vehicles. Studying the unique flight mechanisms and structural characteristics of beetles has significant implications for the optimization of biomimetic flying devices. Among beetles, Allomyrina dichotoma (rhinoceros beetle) exhibits a distinct wing deployment-flight-retraction sequence, whereby the interaction between the hindwings and protective elytra contributes to lift generation and maintenance. This study investigates A. dichotoma's wing deployment, flight, and retraction behaviors through motion analysis, uncovering the critical role of the elytra in wing folding. We capture the kinematic parameters throughout the entire flight process and develop an accurate kinematic model of A. dichotoma flight. Using smoke visualization, we analyze the flow field generated during flight, revealing the formation of enhanced leading-edge vortices and attached vortices during both upstroke and downstroke phases. These findings uncover the high-lift mechanism underlying A. dichotoma's flight dynamics, offering valuable insights for optimizing beetle-inspired micro aerial vehicles.

Worms are organisms characterized by simple structures, low energy consumption, and stable movement. Inspired by these characteristics, worm-like soft robots demonstrate exceptional adaptability to unstructured environments, attracting considerable interest in the field of biomimetic engineering. The primary challenge currently involves improving the motion performance of worm-like robots from the perspectives of actuation and anchoring. In this study, a single segment worm-like soft robot driven by electrohydraulic actuators is proposed. The robot consists of a soft actuation module and two symmetrical anchoring modules. The actuation modules enable multi-degree-of-freedom motion of the robot using symmetric dual-electrode electrohydraulic actuators, while the anchoring modules provide active friction control through bistable electrohydraulic actuators. A hierarchical microstructure design is used for the biomimetic adhesive surface, enabling rapid, reversible, and stable attachment to and detachment from different surfaces, thereby improving the robot's surface anchoring performance. Experimental results show that the designed robot can perform peristaltic and bending motions similar to a worm. It achieves rapid bidirectional propulsion on both dry and wet surfaces, with a maximum speed of 10.36 mm/s (over 6 velocity/length ratio (min^-1)).

This paper addresses the design, development, control, and experimental evaluation of a soft robot arm whose actuation is inspired by the muscular structure of the octopus arm, one of the most agile biological manipulators. The robot arm is made of soft silicone and thus possesses enhanced compliance, which is beneficial in a variety of applications where the arm may come into contact with delicate features of its environment. The arm is composed of three elongated segments arranged in series, each one of which contains several pneumatically actuated chambers embedded in its silicone body, which may induce various types of deformations of the segment. By combining the segment deformations, and by imitating the antagonistic muscle group functionality of the octopus, the robot arm can bend in various directions, increase or decrease its length, as well as twist around its central axis. This is one of the few octopus-inspired soft robotic arms where twisting is replicated in its motion characteristics, thus greatly expanding the arm's potential applications. We present the design process and the development steps of the soft arm, where the molding of two-part silicone of low hardness in 3d-printed molds is employed. In addition, we present the control methodology and the experimental evaluation of both a standalone segment and the entire three-segment arm. This experimental evaluation involves model-free closed-loop control schemes, exploiting visual feedback from a pair of external cameras in order to reconstruct in real time the shape of the soft arm and the pose of its tip.

The realization of hand function reengineering using a manipulator is a research hotspot in the field of robotics. In this paper, we propose a multimodal perception and control method for a robotic hand to assist the disabled. The movement of the human hand can be divided into two parts: the coordination of the posture of the fingers, and the coordination of the timing of grasping and releasing objects. Therefore, we first used a pinhole camera to construct a visual device suitable for finger mounting, and preclassified the shape of the object based on YOLOv8; then, a filtering process using multi-frame synthesized point cloud data from miniature 2D Lidar, and DBSCAN algorithm clustering objects and the DTW algorithm, was proposed to further identify the cross-sectional shape and size of the grasped part of the object and realize control of the robot's grasping gesture; finally, a multimodal perception and control method for prosthetic hands was proposed. To control the grasping attitude, a fusion algorithm based on information of upper limb motion state, hand position, and lesser toe haptics was proposed to realize control of the robotic grasping process with a human in the ring. The device designed in this paper does not contact the human skin, does not produce discomfort, and the completion rate of the grasping process experiment reached 91.63%, which indicates that the proposed control method has feasibility and applicability.

Image super-resolution (SR) is a formidable challenge due to the intricacies of the underwater environment such as light absorption, scattering, and color distortion. Plenty of deep learning methods have provided a substantial performance boost for SR. Nevertheless, these methods are not only computationally expensive but also often lack flexibility in adapting to severely degraded image statistics. To counteract these issues, we propose a dynamic structure-aware modulation network (DSMN) for efficient and accurate underwater SR. A Mixed Transformer incorporated a structure-aware Transformer block and multi-head Transformer block, which could comprehensively utilize local structural attributes and global features to enhance the details of underwater image restoration. Then, we devised a dynamic information modulation module (DIMM), which adaptively modulated the output of the Mixed Transformer with appropriate weights based on input statistics to highlight important information. Further, a hybrid-attention fusion module (HAFM) adopted spatial and channel interaction to aggregate more delicate features, facilitating high-quality underwater image reconstruction. Extensive experiments on benchmark datasets revealed that our proposed DSMN surpasses the most renowned SR methods regarding quantitative and qualitative metrics, along with less computational effort.

Marginal enamel fractures (MEF) are a common clinical concern in dentistry, particularly in anterior teeth. These fractures occur at the enamel margins and their etiopathogenesis involves a complex interplay of mechanical, chemical, and biological factors. The ongoing research focuses on an overview of MEF to improve the knowledge about this condition. Understanding the multifaceted nature of MEF is crucial for devising effective preventive and therapeutic strategies in contemporary restorative dentistry. Indeed, mechanical stresses, such as occlusal forces and parafunctional habits are primary contributors for MEF. Additionally, it can happen at the enamel-restoration interface due to expansion and contraction of restorative materials. Chemical degradation, including acid erosion and the breakdown of adhesive bonds, further exacerbates the vulnerability of enamel. Biological factors, such as enamel composition and the presence of micro-cracks also play a role in the development of MEF. Clinical management of MEF involves subtractive or additive techniques, repairing or replacing the compromised tooth structure using techniques to ensure the integration with the natural enamel.