Journal of Bionic Engineering最新文献

The primary objective of Cartilage Tissue Engineering (CTE) involves repairing or rebuilding impaired cartilage in an effort to restore joint functionality and enhance patients' quality of life. In this field, researchers are constantly exploring new materials and technologies to address the challenges posed by cartilage damage. Biomimetic hydrogels present several distinct advantages in articular cartilage repair when compared to conventional treatment methods like minimally invasive surgery, joint replacement, and drug therapies. These hydrogels effectively mimic the mechanical characteristics of natural cartilage while also promoting cell adhesion, proliferation, and differentiation through the inclusion of bioactive factors. This results in the creation of high-performance biomaterials, positioning them as a particularly promising therapeutic option. Recently, researchers have drawn inspiration from the intricate structures found in soft tissues to develop various types of biomimetic hydrogels. These innovative hydrogels find applications across various fields, such as biomedicine, tissue engineering, and flexible electronics. In tissue engineering, these materials serve as optimal scaffolds for cartilage regeneration and aid in restoring tissue function. Nevertheless, creating and manufacturing biomimetic hydrogels with complex designs, strong mechanical properties, and multifunctionality poses significant challenges. This paper reviews existing studies on natural and synthetic matrices for biomimetic hydrogels, explores the similarities between these hydrogels and natural cartilage, examines their biological and physical characteristics, discusses their advantages and limitations, and suggests future research avenues.

In this paper, a novel cooperative collision avoidance control strategy with relative velocity information for redundant robotic manipulators is derived to guarantee the behavioral safety of robots in the cooperative operational task. This strategy can generate the collision-free trajectory of the robotic links in real-time, which is to realize that the robot can avoid moving obstacles less conservatively and ensure tracking accuracy of terminal end-effector tasks in performing cooperative tasks. For the case where there is interference between the moving obstacle and the desired path of the robotic end-effector, the method inherits the null-space-based self-motion characteristics of the redundant manipulator, integrates the relative motion information, and uses the improved artificial potential field method to design the control items, which are used to generate the collision avoidance motion and carry out moving obstacles smoothly and less conservatively. At the same time, the strategy maintains the kinematic constraint relationship of dual-arm cooperatives, to meet the real-time collision avoidance task under collaborative tasks. Finally, the algorithm simulation indicates that the method can better ensure the tracking accuracy of the end-effector task and carry out moving obstacles smoothly. The experimental results show that the method can generate the real-time collision-free trajectory of the robot in the cooperative handling task, and the joint movement is continuous and stable.

In the visual ‘teach-and-repeat’ task, a mobile robot is expected to perform path following based on visual memory acquired along a route that it has traversed. Following a visually familiar route is also a critical navigation skill for foraging insects, which they accomplish robustly despite tiny brains. Inspired by the mushroom body structure in the insect brain and its well-understood associative learning ability, we develop an embodied model that can accomplish visual teach-and-repeat efficiently. Critical to the performance is steering the robot body reflexively based on the relative familiarity of left and right visual fields, eliminating the need for stopping and scanning regularly for optimal directions. The model is robust against noise in visual processing and motor control and can produce performance comparable to pure pursuit or visual localisation methods that rely heavily on the estimation of positions. The model is tested on a real robot and also shown to be able to correct for significant intrinsic steering bias.

Web-like obstacles, such as safety nets, represent a unique hazard for drones, and especially UAVs (Unmanned Aerial Vehicles). Fencing and netting are particularly difficult to distinguish from the background using either computer vision, lidar and sonar. In contrast, animals such as flying insects may detect these web-like obstacles using Optic Flow (OF), and more precisely motion parallax. A netting-avoidance solution was proposed using a OF-based detection method. The netting detection method was based on a signature defined by the shape of the OF magnitude across the visual field. We established that the OF shape depends on the orientation of the netting in relation to the hexarotor’s movement. This paper demonstrates netting detection in real-world experiments, according to any direction flight made by the UAV along the net. The proposed NOWA method (which stands for Netting Optical floW-based distinction Algorithm) separates the OF signatures belonging to these different surfaces -netting or background- whatever their orientations. By extracting the OF signatures of these different surfaces and separating them, the proposed visual method can estimate their relative locations and orientations. In a robotic simulations, the multirotor explores and navigates automatically using this netting detection method, using saccades to avoid obstacles. In the simulations, these saccades are also used to simplify netting detection by orienting itself systematically parallel to these planes, a behavior reminiscent of flying insects.

Accurate landing detection is crucial for humanoid robots performing high dynamic motions. Unlike common methods that rely on redundant force-torque sensors and low-precision observers to estimate landing states, this paper proposes a novel landing detection method characterized by high precision and low noise, synthesizing a learning-based Improved Momentum Observer (IMO-Net) for the ankles’ external torque estimation with a Gated Recurrent Unit (GRU)-based network for state judgment. Since the movement and external torque of the ankle undergo drastic changes during high dynamic motions, achieving accurate and real-time estimation presents a challenge. To address this problem, IMO-Net employs a new Improved Momentum Observer (IMO), which does not depend on acceleration data derived from second-order differentials or friction model, and significantly reduces noise effects from sensors data and robot foot wobble. Furthermore, an Elman network is utilized to accurately calculate the ankle output torque (IMO input), significantly reducing the estimation error. Finally, leveraging IMO-Net and extensive experimental data, we developed and optimized a GRU-based landing detection network through comprehensive ablation experiments. This refined network reliably determines the robot’s landing states in real-time. The effectiveness of our methods has been validated through experiments.

The application of transformer networks and feature fusion models in medical image segmentation has aroused considerable attention within the academic circle. Nevertheless, two main obstacles persist: (1) the restrictions of the Transformer network in dealing with locally detailed features, and (2) the considerable loss of feature information in current feature fusion modules. To solve these issues, this study initially presents a refined feature extraction approach, employing a double-branch feature extraction network to capture complex multi-scale local and global information from images. Subsequently, we proposed a low-loss feature fusion method-Multi-branch Feature Fusion Enhancement Module (MFFEM), which realizes effective feature fusion with minimal loss. Simultaneously, the cross-layer cross-attention fusion module (CLCA) is adopted to further achieve adequate feature fusion by enhancing the interaction between encoders and decoders of various scales. Finally, the feasibility of our method was verified using the Synapse and ACDC datasets, demonstrating its competitiveness. The average DSC (%) was 83.62 and 91.99 respectively, and the average HD95 (mm) was reduced to 19.55 and 1.15 respectively.

Soft robots capable of navigating complex environments hold promise for minimally invasive medical procedures and micromanipulation tasks. Here, we present a magnetically controlled multi-legged soft robot inspired by green sea turtle locomotion. Our designed robot, featuring six magnetized feet, demonstrates stable motion within a magnetic field strength range of 1.84–6.44 mT. Locomotion displacement scales linearly with field strength, while velocity correlates with frequency, reaching approximately 25 mm/s at 10 Hz. The robot navigates dry, semi-submerged, and fully submerged conditions, climbs slopes up to 30°, and maneuvers through U-shaped bends. Additionally, we demonstrate the robot's capability to smoothly transition between terrestrial and aquatic environments, demonstrating its amphibious locomotion performance. This adaptability to diverse environments, coupled with precise magnetic control, opens new possibilities for soft robotics in confined and complex spaces. Our findings provide a framework for designing highly maneuverable small-scale soft robots with potential applications ranging from targeted drug delivery to environmental sensing in challenging terrains.

The mental health issues of college students have become an increasingly prominent social problem, exerting severe impacts on their academic performance and overall well-being. Early identification of Interpersonal Sensitivity (IS) in students serves as an effective approach to detect psychological problems and provide timely intervention. In this study, 958 freshmen from higher education institutions in Zhejiang Province were selected as participants. We proposed a Multi-Strategy Artemisinin Optimization (MSAO) algorithm by enhancing the Artemisinin Optimization (AO) framework through the integration of a group-guided elimination strategy and a two-stage consolidation strategy. Subsequently, the MSAO was combined with the Fuzzy K-Nearest Neighbor (FKNN) classifier to develop the bMSAO-FKNN predictive model for assessing college students’ IS. The proposed algorithm’s efficacy was validated through the CEC 2017 benchmark test suite, while the model’s performance was evaluated on the IS dataset, achieving an accuracy rate of 97.81%. These findings demonstrate that the bMSAO-FKNN model not only ensures high predictive accuracy but also offers interpretability for IS prediction, making it a valuable tool for mental health monitoring in academic settings.

Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission.

Control of the wetting properties of biomimetic functional surfaces is a desired functionality in many applications. In this paper, the photoresist SU-8 was used as fabrication material. A silicon wafer was used as a substrate to prepare a biomimetic surface with different surface roughness and micro-pillars arranged in array morphology. The evaporation dynamics and interfacial heat transfer processes of deionised water droplets on the bioinspired microstructure surface were experimentally studied. The study not only proves the feasibility of preparing hydrophilic biomimetic functional surfaces directly through photoresist materials and photolithography technology but also shows that by adjusting the structural parameters and arrangement of the surface micro-pillar structure, the wettability of the biomimetic surface can be significantly linearly regulated, thereby effectively affecting the heat and mass transfer process at the droplet liquid-vapour interface. Analysis of the results shows that by controlling the biomimetic surface microstructure, the wettability can be enhanced by about 22% at most, the uniformity of the temperature distribution at the liquid-vapour interface can be improved by about 34%, and the average evaporation rate can be increased by about 28%. This study aims to provide some guidance for the research on bionic surface design based on photoresist materials.