Journal of Bionic Engineering最新文献

To better understand the aerodynamic reasons for highly organized movements of flying organisms, the three-flapping wing system in tandem formation was studied numerically in this paper. Different from previous relevant studies on the multiple flapping wings that are equally spaced, this study emphasizes the impact of unequal spacing between individuals on the aerodynamics of each individual wing as well as the whole system. It is found that swapping the distance between the first and second wing with the distance between the second wing and the rearmost wing does not affect the overall aerodynamic performance, but significantly changes the distribution of aerodynamic benefits across each wing. During the whole flapping cycle, three effects are at play. The narrow channel effect and the downwash effect can promote and weaken the wing lift, respectively, while the wake capture effect can boost the thrust. It also shows that these effects could be manipulated by changing the spacing between adjacent wings. These findings provide a novel way for flow control in tandem formation flight and are also inspiring for designing the formation flight of bionic aircraft.

Flexible attachment actuators are popular in a wide range of applications, owing to their flexibility and highly reliable attachment. However, their reversible adhesion performance depends on the actual effective contact area and peel angle during operation. Therefore, a good actuator must ensure a uniform and reliable pre-pressure load on an adhesive surface, to increase the effective contact area of the attached surface, thereby maximizing adhesion. This study was inspired by fusion bionics for designing a hierarchical attachment structure with vacuum-adsorption and dry-adhesion mechanisms. The designed structure used the normal force under the negative pressure of a suction cup as a stable source of a pre-pressure load. By optimizing the rigid and flexible structural layers of the attachment structure, a load was applied uniformly to the adhesion area; thus, reliable attachment was achieved by self-preloading. The structure achieved detachment by exploiting the large deformation of a pneumatic structure under a positive pressure. The hierarchical attachment structure achieved up to 85% of the optimal performance of the adhesive surface. Owing to its self-preloading and reliable attachment characteristics, the designed structure can be used as an attachment unit in various complex scenarios, such as small, lightweight climbing platforms and the transport of objects in long, narrow pipelines.

When a human lands from a high drop, there is a high risk of serious injury to the lower limbs. On the other hand, cats can withstand jumps and falls from heights without being fatally wounded, largely due to their impact-resistant paw pads. The aim of the present study was to investigate the biomechanism of impact resistance in cat paw pads, propose an optimal hierarchical Voronoi structure inspired by the paw pads, and apply the structure to bionic cushioning shoes to reduce the impact force of landing for humans. The microstructure of cat paw pads was observed via tissue section staining, and a simulation model was reconstructed based on CT to verify and optimize the structural cushioning capacity. The distribution pattern, wall thickness of compartments, thickness ratio of epidermis and dermis, and number of compartments in the model were changed and simulated to achieve an optimal composed structure. A bionic sole was 3D-printed, and its performance was evaluated via compression test and a jumping-landing experiment. The results show that cat paw pads are a spherical cap structure, divided from the outside to the inside into the epidermis, dermis, and compartments, each with different cushioning capacities. A finite element simulation of different cushioning structures was conducted in a cylinder with a diameter of 20 mm and a height of 10 mm, featuring a three-layer structure. The optimal configuration of the three layers should have a uniform distribution with 0.3–0.5 mm wall thickness, a 1:1–2 thickness ratio of epidermis and dermis, and 100–150 compartments. A bionic sole with an optimized structure can reduce the peak impact force and delay the peak arrival time. Its energy absorption rate is about 4 times that of standard sole. When jumping 80, 100, and 120 cm, the normalized ground reaction force is also reduced by 8.7%, 12.6% and 15.1% compared with standard shoes. This study provides theoretical and technical support for effective protection against human lower limb landing injuries.

This paper presents a learning-based control framework for fast (< 1.5 s) and accurate manipulation of a flexible object, i.e., whip targeting. The framework consists of a motion planner learned or optimized by an algorithm, Online Impedance Adaptation Control (OIAC), a sim2real mechanism, and a visual feedback component. The experimental results show that a soft actor-critic algorithm outperforms three Deep Reinforcement Learning (DRL), a nonlinear optimization, and a genetic algorithm in learning generalization of motion planning. It can greatly reduce average learning trials (to < 20(%) of others) and maximize average rewards (to > 3 times of others). Besides, motion tracking errors are greatly reduced to 13.29(%) and 22.36(%) of constant impedance control by the OIAC of the proposed framework. In addition, the trajectory similarity between simulated and physical whips is 89.09(%). The presented framework provides a new method integrating data-driven and physics-based algorithms for controlling fast and accurate arm manipulation of a flexible object.

In order to reduce the labor intensity of high-altitude workers and realize the cleaning and maintenance of high-rise building exteriors, this paper proposes a design for a 4-DOF bipedal wall-climbing bionic robot inspired by the inchworm’s movement. The robot utilizes vacuum adsorption for vertical wall attachment and legged movement for locomotion. To enhance the robot’s movement efficiency and reduce wear on the adsorption device, a gait mimicking an inchworm’s movement is planned, and foot trajectory planning is performed using a quintic polynomial function. Under velocity constraints, foot trajectory optimization is achieved using an improved Particle Swarm Optimization (PSO) algorithm, determining the quintic polynomial function with the best fitness through simulation. Finally, through comparative experiments, the climbing time of the robot closely matches the simulation results, validating the trajectory planning method’s accuracy.

With an elemental composition similar to bone mineral, and the ability to release phosphorus and calcium that benefit bone regeneration, Calcium Phosphate Glass (CPG) serves as a promising component of bone tissue engineering scaffolds. However, the degradation of CPG composites typically results in increased acidity, and its impact on bone-forming activity is less studied. In this work, we prepared 3D-printed composite scaffolds comprising CPG, Poly-ε-caprolactone (PCL), and various Magnesium Oxide (MgO) contents. Increasing the MgO content effectively suppressed the degradation of CPG, maintaining a physiological pH of the degradation media. While the degradation of CPG/PCL scaffolds resulted in upregulated apoptosis of Rat Bone Marrow-derived Stem Cells (rBMSC), scaffolds containing MgO were free from these negative impacts, and an optimal MgO content of 1 wt% led to the most pronounced osteogenic differentiation of rBMSCs. This work demonstrated that the rapid degradation of CPG impaired the renewability of stem cells through the increased acidity of the surrounding media, and MgO effectively modulated the degradation rate of CPG, thus preventing the negative effects of rapid degradation and supporting the proliferation and osteogenic differentiation of the stem cells.

The enhancement of adhesive perception is crucial to maintaining a stable and comfortable grip of the skin-touch products. To study the tactile perception of adhesive surfaces, subjective evaluation, skin friction and vibrations, and neurophysiological response of the brain activity were investigated systematically. Silicone materials, which are commonly used for bionic materials and skin-touch products, were chosen for the tactile stimulus. The results showed that with the increasing of surface adhesion, the dominant friction transferred from a combination of adhesive friction and deformation friction to adhesive friction. The friction coefficient and vibration amplitude had strong correlations with the perceived adhesion of surfaces. The parietal lobe and occipital lobe were involved in adhesive perceptions, and the area and intensity of brain activation increased with the increasing surface adhesion. Surfaces with larger adhesion tended to excite a high P300 amplitude and short latency, indicating that the judgment was faster and that more attentional resources were involved in adhesive perception. Furthermore, the electroencephalograph signals of the adhesive perception were simulated by the neural mass model. It demonstrated that the excitability and intensity of brain activity, and the connectivity strength between two neural masses increased with the increasing surface adhesion. This study is meaningful to understand the role of surface adhesion in tactile friction and the cognitive mechanism in adhesive perception to improve the tactile experience of adhesive materials.

In order to strike a balance between achieving desired velocities and minimizing energy consumption, legged animals have the ability to adopt the appropriate gait pattern and seamlessly transition to another if needed. This ability makes them more versatile and efficient when traversing natural terrains, and more suitable for long treks. In the same way, it is meaningful and important for quadruped robots to master this ability. To achieve this goal, we propose an effective gait-heuristic reinforcement learning framework in which multiple gait locomotion and smooth gait transitions automatically emerge to reach target velocities while minimizing energy consumption. We incorporate a novel trajectory generator with explicit gait information as a memory mechanism into the deep reinforcement learning framework. This allows the quadruped robot to adopt reliable and distinct gait patterns while benefiting from a warm start provided by the trajectory generator. Furthermore, we investigate the key factors contributing to the emergence of multiple gait locomotion. We tested our framework on a closed-chain quadruped robot and demonstrated that the robot can change its gait patterns, such as standing, walking, and trotting, to adopt the most energy-efficient gait at a given speed. Lastly, we deploy our learned controller to a quadruped robot and demonstrate the energy efficiency and robustness of our method.

Surface electromyography (sEMG)-based gesture recognition is a key technology in the field of human–computer interaction. However, existing gesture recognition methods face challenges in effectively integrating discriminative temporal feature representations from sEMG signals. In this paper, we propose a deep learning framework named TFN-FICFM comprises a Temporal Fusion Network (TFN) and Fuzzy Integral-Based Classifier Fusion method (FICFM) to improve the accuracy and robustness of gesture recognition. Firstly, we design a TFN module, which utilizes an attention-based recurrent multi-scale convolutional module to acquire multi-level temporal feature representations and achieves deep fusion of temporal features through a feature pyramid module. Secondly, the deep-fused temporal features are utilized to generate multiple sets of gesture category prediction confidences through a feedback loop. Finally, we employ FICFM to perform fuzzy fusion on prediction confidences, resulting in the ultimate decision. This study conducts extensive comparisons and ablation studies using the publicly available datasets Ninapro DB2 and DB5. Results demonstrate that the TFN-FICFM model outperforms state-of-the-art methods in classification performance. This research can serve as a benchmark for sEMG-based gesture recognition and related deep learning modeling.

Feature Selection (FS) is an important data management technique that aims to minimize redundant information in a dataset. This work proposes DENGO, an improved version of the Northern Goshawk Optimization (NGO), to address the FS problem. The NGO is an efficient swarm-based algorithm that takes its inspiration from the predatory actions of the northern goshawk. In order to overcome the disadvantages that NGO is prone to local optimum trap, slow convergence speed and low convergence accuracy, two strategies are introduced in the original NGO to boost the effectiveness of NGO. Firstly, a learning strategy is proposed where search members learn by learning from the information gaps of other members of the population to enhance the algorithm's global search ability while improving the population diversity. Secondly, a hybrid differential strategy is proposed to improve the capability of the algorithm to escape from the trap of the local optimum by perturbing the individuals to improve convergence accuracy and speed. To prove the effectiveness of the suggested DENGO, it is measured against eleven advanced algorithms on the CEC2015 and CEC2017 benchmark functions, and the obtained results demonstrate that the DENGO has a stronger global exploration capability with higher convergence performance and stability. Subsequently, the proposed DENGO is used for FS, and the 29 benchmark datasets from the UCL database prove that the DENGO-based FS method equipped with higher classification accuracy and stability compared with eight other popular FS methods, and therefore, DENGO is considered to be one of the most prospective FS techniques. DENGO's code can be obtained at https://www.mathworks.com/matlabcentral/fileexchange/158811-project1.