Journal of Bionic Engineering最新文献

The Auxetic Structure (AS) exhibits significant densification strain due to its concave cell architecture, functioning as an effective energy-absorbing metamaterial. However, its limited plateau stress hampers further enhancement of energy absorption. The deep-sea Glass Sponge (GS) has high plateau stress due to its diagonal braces. Inspired by GS, the Glass-Sponge-Auxetic Structure (GSAS) is proposed, featuring concave cells reinforced by diagonal braces to achieve both high plateau stress and densification strain. Different structural configurations incorporating various brace arrangements and thicknesses for GSAS are designed and compared through finite element analysis. An optimal GSAS is achieved with a 0.5 mm strut thickness and an asymmetric arrangement of crossing and uncrossing braces. The GSAS is fabricated using Ti6Al4V through selective laser melting and compared with AS, GS, body-centered cube, and honeycomb in compression tests. The unique bending-stretching deformation and non-simultaneous fracturing pattern results in simultaneous high plateau stress and densification strain, and the highest energy absorption and specific energy absorption. Compared to AS, these values are enhanced by 156% and 75%, respectively. The exceptional energy absorption capability of GSAS presents promising prospects in fields such as automobile collision avoidance and vibration damping, with its customizable cell numbers offering the potential for more specific applications.

This paper systematically studies the movement behavior changes of Camponotus japonicus under one or two leg injuries. Firstly, a linear motion channel matching the size of the ants’ legs was designed, and the movements of ants with different leg injuries were captured using high-speed cameras, constructing a comprehensive video dataset of ants’ movements with missing legs. Secondly, stable and reliable motion position information for keypoints on the ants’ bodies and legs was obtained by utilizing low-annotation biometric keypoint detection technology. Finally, by analyzing the filtered gait data, information about the changes in the step locational points areas, phase differences, and duty factors of the injured ants’ remaining legs was obtained. Comparative analysis of the ants’ gait characteristics revealed some common adjustment patterns when the ants were in the injured states. Additionally, the study found that the loss of a foreleg had a significant impact on the ants’ movement. When two legs were missing, the loss of both legs on the same side had a greater effect on movement, whereas symmetric opposite-side leg loss conditions had a lesser impact. The research will provide an important reference for the subsequent design of gait adjustment algorithms for biomimetic multi-legged robots under damaged conditions.

As a kind of ionic artificial muscle material, Ionic Polymer–Metal Composites (IPMCs) have the advantages of a low drive current, light weight, and significant flexibility. IPMCs are widely used in the fields of biomedicine, soft robots, etc. However, the displacement and blocking force of the traditional sheet-type Nafion-IPMC need to be improved, and it has the limitation of unidirectional actuation. In this paper, a new type of short side chain Aquivion material is used as the polymer in the IPMC. The cylindrical IPMC is prepared by extrusion technology to improve its actuation performance and realize multi-degree-of-freedom motion. In comparison to the traditional Nafion-IPMC, the ion exchange capacity, specific capacitance, and conductivity of Aquivion-IPMC are improved by 28%, 27%, and 32%, respectively, and the displacement and blocking force are improved by 57% and 25%, respectively. The cylindrical actuators can be deflected in eight directions. This indicates that Aquivion, as a polymer membrane for IPMC, holds significant application potential. By designing a cylindrical IPMC electrode distribution, the multi-degree-of-freedom deflection of IPMC can be realized.

Quadruped robots which have flexibility and load-bearing capacity, are regarded as the best mobile platform for remote operation in unstructured and restricted environments. In the process of remote operation of quadruped robots, their status is inevitably influenced by complex environments. To monitor the robot's real-time operation status and make necessary adjustments, this paper focuses on the single-leg of a quadruped robot, proposes a single-leg virtual–real interaction system based on Digital Twin, and studies its virtual–real interaction characteristics. The virtual–reality interaction system contains single-leg physical entity, single-leg virtual model, control system, data service system and communication system, enabling interactive applications for single-leg visual state monitoring and real-time control optimization. This paper creates a high-fidelity model based on the physical entity; provides a system performance analysis method based on the system framework; analyzes virtual–real interaction delay based on communication scheme; conducts stand and jump test based on the single-leg mathematical model and analyzes the interaction characteristics under position/force control. This system provides new insights for real-time monitoring and control optimization of quadruped robots.

This paper designs a soft robot with a multi-chamber, multi-airbag mimicking soft biological structure, where the airbags of the same chamber are interconnected with each other. The upper and lower chambers are separated by an intermediate layer (thin plate), which is extended and widened to achieve robot movement and balance. By applying pressure to the different chambers of the soft robot, it is possible to produce a variety of bionic movements of the inchworm and caterpillar. Due to the strong nonlinearity and infinite number of degrees of freedom properties of the material, it is impossible to obtain the analytical solution of the bending morphology and pressure of the soft robot directly. Therefore, a method to establish a mathematical model of soft robot deformation based on the classical stacked plate theory is proposed, and a chain composite model of soft robot bending motion is established based on the large-deflection modeling method. This paper proposes a method to generate a multi-mode soft robot motion control based on the Central Pattern Generator (CPG) using a single controller, which achieves the switching of sine wave-like patterns, half-wave-like patterns, and dragging patterns by adjusting frequency, amplitude and period of parameters. Finally, a pneumatic control platform is built for the validation of the theoretical model and different experimental models of the motion of the robot. And comparation of the different motion modes of the soft robot under similar non-load and load conditions.

Immersion of scaffolds in Simulated Body Fluid (10SBF) is a standardized method for evaluating their bioactivity, simulating in vivo conditions where apatite deposits can be formed on the surface of scaffold, facilitating bone integration and ensuring their suitability for bone implant purposes, ultimately contributing to long-term implant success. The effect of apatite deposition on bioactivity and cell behavior of TiO₂ scaffolds was studied. Scaffolds were soaked in 10SBF for different durations to form HAP layer on their surface. The results proved the development of a hydroxyapatite film resembling the mineral composition of bone Extracellular Matrix (ECM) on the TiO₂ scaffolds. The XRD test findings showed the presence of hydroxyapatite layer similar to bone at the depth of 10 nm. A decrease in the specific surface area (18.913 m²g^− 1), the total pore volume (0.045172 cm³g^− 1 (at p/p₀ = 0.990)), and the mean pore diameter (9.5537 nm), were observed by BET analysis which confirmed the formation of the apatite layer. It was found that titania scaffolds with HAP coating promoted human osteosarcoma bone cell (MG63) cell attachment and growth. It seems that immersing the scaffolds in 10SBF to form HAP coating before utilizing them for bone tissue engineering applications might be a good strategy to promote bioactivity, cell attachment, and implant fixation.

As an effective therapy for treating unilateral neglect, Mirror Therapy (MT) is employed in the upper limb motor function rehabilitation of hemiplegic patients. However, traditional MT has a serious limitation—the Impaired Limb (IL) doesn’t actually move. In this study, a novel performance-based assistance strategy suitable for Robotic Mirror Therapy (RMT) based on MT is proposed. A guiding assistance based on the progress difference HL and IL is constructed in trajectory guidance, and a multi-stiffness region correction force field based on trajectory tracking error is designed to constrain IL’s deviation from the intended path in trajectory correction assistance. To validate the presented strategy, a series of experiments on a RMT system based on the end-effector upper limb rehabilitation robot are conducted. The results verify the performance and feasibility of this strategy.

Runge Kutta Optimization (RUN) is a widely utilized metaheuristic algorithm. However, it suffers from these issues: the imbalance between exploration and exploitation and the tendency to fall into local optima when it solves real-world optimization problems. To address these challenges, this study aims to endow each individual in the population with a certain level of intelligence, allowing them to make autonomous decisions about their next optimization behavior. By incorporating Reinforcement Learning (RL) and the Composite Mutation Strategy (CMS), each individual in the population goes through additional self-improvement steps after completing the original algorithmic phases, referred to as RLRUN. That is, each individual in the RUN population is trained intelligently using RL to independently choose three different differentiation strategies in CMS when solving different problems. To validate the competitiveness of RLRUN, comprehensive empirical tests were conducted using the IEEE CEC 2017 benchmark suite. Extensive comparative experiments with 13 conventional algorithms and 10 advanced algorithms were conducted. The experimental results demonstrated that RLRUN excels in convergence accuracy and speed, surpassing even some champion algorithms. Additionally, this study introduced a binary version of RLRUN, named bRLRUN, which was employed for the feature selection problem. Across 24 high-dimensional datasets encompassing UCI datasets and SBCB machine learning library microarray datasets, bRLRUN occupies the top position in classification accuracy and the number of selected feature subsets compared to some algorithms. In conclusion, the proposed algorithm demonstrated that it exhibits a strong competitive advantage in high-dimensional feature selection for complex datasets.

This paper investigates the motion control of the heavy-duty Bionic Caterpillar-like Robot (BCR) for the maintenance of the China Fusion Engineering Test Reactor (CFETR). Initially, a comprehensive nonlinear mathematical model for the BCR system is formulated using a physics-based approach. The nonlinear components of the model are compensated through nonlinear feedback linearization. Subsequently, a fuzzy-based regulator is employed to enhance the receding horizon optimization process for achieving optimal results. A Deep Neural Network (DNN) is trained to address disturbances. Consequently, a novel hybrid controller incorporating Nonlinear Model Predictive Control (NMPC), the Fuzzy Regulator (FR), and Deep Neural Network Feedforward (DNNF), named NMPC-FRDNNF is developed. Finally, the efficacy of the control system is validated through simulations and experiments. The results indicate that the Root Mean Square Error (RMSE) of the controller with FR and DNNF decreases by 33.2 and 48.9%, respectively, compared to the controller without these enhancements. This research provides a theoretical foundation and practical insights for ensuring the future highly stable, safe, and efficient maintenance of blankets.

Soft grippers are favored for handling delicate objects due to their compliance but often have lower load capacities compared to rigid ones. Variable Stiffness Module (VSM) offer a solution, balancing flexibility and load capacity, for which particle jamming is an effective technology for stiffness-tunable robots requiring safe interaction and load capacity. Specific applications, such as rescue scenarios, require quantitative analysis to optimize VSM design parameters, which previous analytical models cannot effectively handle. To address this, a Grey-box model is proposed to analyze the mechanical response of the particle-jamming-based VSM by combining a White-box approach based on the virtual work principle with a Black-box approach that uses a shallow neural network method. The Grey-box model demonstrates a high level of accuracy in predicting the VSM force-height mechanical response curves, with errors below 15% in almost 90% of the cases and a maximum error of less than 25%. The model is used to optimize VSM design parameters, particularly those unexplored combinations. Our results from the load capacity and force distribution comparison tests indicate that the VSM, optimized through our methods, quantitatively meets the practical engineering requirements.