Biomimetics最新文献

This paper presents a novel soft crawling robot controlled by gesture recognition, aimed at enhancing the operability and adaptability of soft robots through natural human-computer interactions. The Leap Motion sensor is employed to capture hand gesture data, and Unreal Engine is used for gesture recognition. Using the UE4Duino, gesture semantics are transmitted to an Arduino control system, enabling direct control over the robot's movements. For accurate and real-time gesture recognition, we propose a threshold-based method for static gestures and a backpropagation (BP) neural network model for dynamic gestures. In terms of design, the robot utilizes cost-effective thermoplastic polyurethane (TPU) film as the primary pneumatic actuator material. Through a positive and negative pressure switching circuit, the robot's actuators achieve controllable extension and contraction, allowing for basic movements such as linear motion and directional changes. Experimental results demonstrate that the robot can successfully perform diverse motions under gesture control, highlighting the potential of gesture-based interaction in soft robotics.

Artificial intelligence, with its remarkable adaptability, has gradually integrated into daily life. The emergence of the self-attention mechanism has propelled the Transformer architecture into diverse fields, including a role as an efficient and precise diagnostic and predictive tool in medicine. To enhance accuracy, we propose the Double-Attention (DA) method, which improves the neural network's biomimetic performance of human attention. By incorporating matrices generated from shifted images into the self-attention mechanism, the network gains the ability to preemptively acquire information from surrounding regions. Experimental results demonstrate the superior performance of our approaches across various benchmark datasets, validating their effectiveness. Furthermore, the method was applied to patient kidney datasets collected from hospitals for diabetes diagnosis, where they achieved high accuracy with significantly reduced computational demands. This advancement showcases the potential of our methods in the field of biomimetics, aligning well with the goals of developing innovative bioinspired diagnostic tools.

Stereo-orientation selectivity is a fundamental neural mechanism in the brain that plays a crucial role in perception. However, due to the recognition process of high-dimensional spatial information commonly occurring in high-order cortex, we still know little about the mechanisms underlying stereo-orientation selectivity and lack a modeling strategy. A classical explanation for the mechanism of two-dimensional orientation selectivity within the primary visual cortex is based on the Hubel-Wiesel model, a cascading neural connection structure. The local-to-global information aggregation thought within the Hubel-Wiesel model not only contributed to neurophysiology but also inspired the development of computer vision fields. In this paper, we provide a clear and efficient conceptual understanding of stereo-orientation selectivity and propose a quantitative explanation for its generation based on the thought of local-to-global information aggregation within the Hubel-Wiesel model and develop an artificial visual system (AVS) for stereo-orientation recognition. Our approach involves modeling depth selective cells to receive depth information, simple stereo-orientation selective cells for combining distinct depth information inputs to generate various local stereo-orientation selectivity, and complex stereo-orientation selective cells responsible for integrating the same local information to generate global stereo-orientation selectivity. Simulation results demonstrate that our AVS is effective in stereo-orientation recognition and robust against spatial noise jitters. AVS achieved an overall over 90% accuracy on noise data in orientation recognition tasks, significantly outperforming deep models. In addition, the AVS contributes to enhancing deep models' performance, robustness, and stability in 3D object recognition tasks. Notably, AVS enhanced the TransNeXt model in improving its overall performance from 73.1% to 97.2% on the 3D-MNIST dataset and from 56.1% to 86.4% on the 3D-Fashion-MNIST dataset. Our explanation for the generation of stereo-orientation selectivity offers a reliable, explainable, and robust approach for extracting spatial features and provides a straightforward modeling method for neural computation research.

Background: Exoskeletons are used in rehabilitation centers for people with spinal cord injuries (SCI) due to the potential benefits they offer for locomotor rehabilitation. The acceptability of exoskeletons is crucial to promote rehabilitation and to ensure a successful implementation of this technology. The objective was to explore the acceptability of overground wearable powered exoskeleton used in rehabilitation among people with SCI. Methods: Fourteen individuals with SCI (9 men, mean [SD] age 47 years [14.8], a majority with traumatic and thoracic lesion (T6-T12)) who had utilized an exoskeleton in Canada or in France during their rehabilitation participated in a semi-structured interview. A thematic analysis using the theoretical framework of acceptability was carried out. Results: Participants were motivated to use an exoskeleton during their rehabilitation. They reported several perceived benefits to its use, including better walking pattern, increased endurance, and greater muscle mass. They also experienced mild pain, notable concentration demands, and fatigue. Most participants reported that using exoskeletons in their rehabilitation process was appropriate and relevant to them. Conclusions: Exoskeletons are generally well accepted by participants in this study. Adjustments in their use, such as conducting training sessions in obstacle-free environment and technological improvements to address the device's restrictive characteristics, heaviness, and massiveness are however still needed.

Pneumatic artificial muscles (PAMs) are flexible actuators that can be contracted or expanded by applying air pressure. They are used in robotics, prosthetics, and other applications requiring flexible and compliant actuation. PAMs are basically designed to mimic the function of biological muscles, providing a high force-to-weight ratio and smooth, lifelike movement. Inflation and deflation of these muscles can be controlled rapidly, allowing for fast actuation. In this work, a continuum mechanics-based model is developed to predict the output parameters of PAMs, like actuation force. Comparison of the model results with experimental data shows that the model efficiently predicts the mechanical behaviour of PAMs. Using the actuation force-air pressure-contraction relation provided by the proposed mechanical model, a dynamic model is derived for a multi-link PAM-actuated robot manipulator. An adaptive fixed-time fast terminal sliding mode control is proposed to track the desired joint position trajectories despite the model uncertainties and external disturbances with unknown magnitude bounds. Furthermore, the performance of the proposed controller is compared with an adaptive backstepping fast terminal sliding mode controller through numerical simulations. The simulations show faster convergence and more precise tracking for the proposed controller.

Global optimization problems, prevalent across scientific and engineering disciplines, necessitate efficient algorithms for navigating complex, high-dimensional search spaces. Drawing inspiration from the resilient and adaptive growth strategies of moss colonies, the moss growth optimization (MGO) algorithm presents a promising biomimetic approach to these challenges. However, the original MGO can experience premature convergence and limited exploration capabilities. This paper introduces an enhanced bio-inspired algorithm, termed crisscross moss growth optimization (CCMGO), which incorporates a crisscross (CC) strategy and a dynamic grouping parameter, further emulating the biological mechanisms of spore dispersal and resource allocation in moss. By mimicking the interwoven growth patterns of moss, the CC strategy facilitates improved information exchange among population members, thereby enhancing offspring diversity and accelerating convergence. The dynamic grouping parameter, analogous to the adaptive resource allocation strategies of moss in response to environmental changes, balances exploration and exploitation for a more efficient search. Key findings from rigorous experimental evaluations using the CEC2017 benchmark suite demonstrate that CCMGO consistently outperforms nine established metaheuristic algorithms across diverse benchmark functions. Furthermore, in a real-world application to a three-channel reservoir production optimization problem, CCMGO achieves a significantly higher net present value (NPV) compared to benchmark algorithms. This successful application highlights CCMGO's potential as a robust and adaptable tool for addressing complex, real-world optimization challenges, particularly those found in resource management and other nature-inspired domains.

With advancements in bipedal locomotion for humanoid robots, a critical challenge lies in generating gaits that are bounded to ensure stable operation in complex environments. Traditional Model Predictive Control (MPC) methods based on Linear Inverted Pendulum (LIP) or Cart-Table (C-T) methods are straightforward and linear but inadequate for robots with flexible joints and linkages. To overcome this limitation, we propose a Flexible MPC (FMPC) framework that incorporates joint dynamics modeling and emphasizes bounded gait control to enable humanoid robots to achieve stable motion in various conditions. The FMPC is based on an enhanced flexible C-T model as the motion model, featuring an elastic layer and an auxiliary second center of mass (CoM) to simulate joint systems. The flexible C-T model's inversion derivation allows it to be effectively transformed into the predictive equation for the FMPC, therefore enriching its flexible dynamic behavior representation. We further use the Zero Moment Point (ZMP) velocity as a control variable and integrate multiple constraints that emphasize CoM constraint, embed explicit bounded constraint, and integrate ZMP constraint, therefore enabling the control of model flexibility and enhancement of stability. Since all the above constraints are shown to be linear in the control variables, a quadratic programming (QP) problem is established that guarantees that the CoM trajectory is bounded. Lastly, simulations validate the effectiveness of the proposed method, emphasizing its capacity to generate bounded CoM/ZMP trajectories across diverse conditions, underscoring its potential to enhance gait control. In addition, the validation of the simulation of real robot motion on the robots CASBOT and Openloong, in turn, demonstrates the effectiveness and robustness of our approach.

In recent years, unmanned aerial vehicle (UAV) technology has advanced significantly, enabling its widespread use in critical applications such as surveillance, search and rescue, and environmental monitoring. However, planning reliable, safe, and economical paths for UAVs in real-world environments remains a significant challenge. In this paper, we propose a multi-strategy improved red-tailed hawk (IRTH) algorithm for UAV path planning in real environments. First, we enhance the quality of the initial population in the algorithm by using a stochastic reverse learning strategy based on Bernoulli mapping. Then, the quality of the initial population is further improved through a dynamic position update optimization strategy based on stochastic mean fusion, which enhances the exploration capabilities of the algorithm and helps it explore promising solution spaces more effectively. Additionally, we proposed an optimization method for frontier position updates based on a trust domain, which better balances exploration and exploitation. To evaluate the effectiveness of the proposed algorithm, we compare it with 11 other algorithms using the IEEE CEC2017 test set and perform statistical analysis to assess differences. The experimental results demonstrate that the IRTH algorithm yields competitive performance. Finally, to validate its applicability in real-world scenarios, we apply the IRTH algorithm to the UAV path-planning problem in practical environments, achieving improved results and successfully performing path planning for UAVs.

To develop a new type of biomimetic single-cell and multi-cell energy-absorbing box (tube) featuring conical tubes at the intersection of cell walls, it is necessary to address the issue of large bottom-space requirements in current conical energy-absorbing tubes with superior crashworthiness due to their large semi-apical angles. This study proposes adding corrugations to conical tubes with small semi-apical angles and modifying the bottom by replacing the last one or two inclined corrugations with vertical ones. Finite element simulation results show that, compared to conventional conical tubes, adding corrugations reduces the optimal semi-apical angle of conical tubes by 5°, with the optimal range being 5-10°. Furthermore, the modification method of replacing inclined corrugations with vertical ones effectively mitigates the challenges of increasing peak crushing force and large end-peak crushing force as the semi-apical angle increases. This structural optimization lays a foundation for the development of new biomimetic single-cell and multi-cell energy-absorbing boxes (tubes) incorporating conical tubes.

Fibrous by-products, including defective or double cocoons, are obtained during silk processing. These cocoons primarily contain fibroin and sericin (SS) proteins along with minor amounts of wax and mineral salts. In conventional textile processes, SS is removed in the production of smooth, lustrous silk threads, and is typically discarded. However, SS has garnered attention for its antioxidant, antibacterial, biocompatible, and anticancer properties as well as its excellent moisture absorption, making it a promising polymer for biomedical applications. Owing to its functional groups (carboxyl, amino, and hydroxyl), SS can blend and crosslink with other polymers, thereby improving the mechanical properties of sericin-based materials. This study explored the effects of different SS/polyvinyl alcohol (PVA) ratios on porous scaffolds fabricated via freeze-drying, focusing on the mechanical stability, water absorption, and protein release in phosphate-buffered saline (PBS). The scaffold morphology revealed reduced porosity with higher SS content, while increased PVA content led to material folding and layering. A greater PVA content enhanced water absorption, mechanical properties, and thermal stability, although SS release decreased. These results demonstrate that scaffold properties can be tailored by optimizing the SS/PVA ratio to suit specific biomedical applications.