Biomimetics最新文献

Background: Cryopreservation is a key enabling technology for cell-based therapies and regenerative medicine; however, the toxicity associated with permeating cryoprotective agents such as dimethyl sulfoxide (DMSO) remains a major limitation, particularly for applications requiring repeated cell administration or long-term storage. Methods: In this study, silk-derived proteins, namely silk fibroin and silk sericin, were investigated as biomaterial-based cryoprotective additives to enable DMSO-sparing cryopreservation strategies. Mouse fibroblasts (3T3) were cryopreserved at -80 °C using conventional DMSO-based media, silk-only formulations, and hybrid formulations combining silk proteins with reduced DMSO concentrations. Post-thaw cell adhesion, metabolic activity, membrane integrity, and cytoskeletal organization were systematically evaluated over a 7-day culture period. Results: Complete replacement of DMSO with silk proteins was insufficient to ensure cell survival, confirming the essential role of permeating cryoprotectants for intracellular protection. In contrast, formulations combining silk fibroin or sericin with 5% (v/v) DMSO supported robust post-thaw viability, preserved cytoskeletal architecture, and promoted favorable recovery kinetics, with cell viability consistently exceeding established biocompatibility thresholds and higher than samples with DMSO alone. Conclusions: These findings support the integration of biomaterial-based components into hybrid cryopreservation formulations and provide design principles relevant to the preservation of more complex multicellular systems.

Exploring the lunar complex and extreme terrain presents formidable challenges for conventional lunar rovers. To address these limitations, this study proposes a novel hexapod jumping hybrid robot that incorporates a "figure-of-eight" (butterfly-shaped) six-branched wheel-legged mechanism and a jumping system that stores elastic energy via deformation of its elastic body. Inspired by the multimodal locomotion of grasshoppers, the robot dynamically switches between two operational modes: high-efficiency wheeled locomotion on relatively flat surfaces and agile jumping to traverse steep slopes and surmount large obstacles. A bio-inspired gait, inspired by the crawling patterns of a hexapod insect, is implemented using a Central Pattern Generator (CPG)-based controller to produce coordinated, rhythmic limb movements. Dynamic simulations of the jumping mechanism were conducted to optimize the critical parameters of the elastic structure and its associated control strategy. Experiments on a physical prototype were conducted to validate the robot's wheeled mobility and jumping performance. The results demonstrate that the robot exhibits excellent adaptability to rugged terrains and obstacle-dense environments. The integration of multimodal locomotion and adaptive gait control significantly enhances the robot's operational robustness and survivability in the harsh lunar environment, opening new possibilities for future lunar exploration missions.

To tackle the navigation challenge in dynamic and complex environments, this study designs a fusion planning framework that synergistically integrates enhanced A* algorithm with improved DWA, inspired by the biological dual-layer navigation mechanism of global path planning and local real-time obstacle avoidance. Firstly, the original global path from the conventional A* algorithm is smoothed and length-reduced through a three-stage optimization strategy involving redundant node removal and forward and reverse path relaxation, mimicking the behavioral logic of honeybees and desert ants that eliminate redundant routes to complete foraging and homing with minimal energy consumption. Secondly, an evaluation function integrating dynamic obstacle perception and adaptive weight adjustment is designed for the DWA to enhance the intelligence of local planning, drawing on the adaptive strategy of animals such as antelopes that adjust behavioral priorities according to environmental complexity to balance safety and efficiency. To comprehensively verify the performance of the proposed algorithm, simulation evaluations are performed in various scenarios, including 20 × 20 and 30 × 30 grid maps, with single and dual dynamic obstacles. Results demonstrate that our algorithm outperforms conventional methods in path length, smoothness, and safety. Further physical verification is carried out on a LiDAR-equipped mobile robot (Shenzhen Yuanchuangxing Technology Co., Ltd., Shenzhen, China) based on the ROS platform, confirming that the algorithm can stably achieve static path tracking and real-time obstacle avoidance in real indoor environments. Consequently, the developed hybrid algorithm delivers a viable and robust solution for autonomous mobile robots to navigate safely and efficiently in unpredictable and complex environments.

Many complex engineering problems can be formulated as mathematical optimization tasks, for which bio-inspired metaheuristic algorithms have demonstrated outstanding effectiveness. Drawing inspiration from snake behavior, the Snake Optimizer (SO) algorithm provides a promising framework but suffers from random population initialization, insufficient global search capability, and slow convergence. To address these drawbacks, the study proposes a Modified Snake Optimizer (MSO) that integrates three key strategies: a dual mapping strategy based on Latin hypercube sampling and logistic mapping for population initialization; an opposition-based learning mechanism with scaling factors for exploration; and integration of the soft-rime search strategy from RIME optimization during exploitation. The performance of MSO was benchmarked against nine representative algorithms using the CEC2017 and further validated on three engineering application problems-pressure vessel, tension/compression spring, and hydrostatic thrust bearing design, and two UAV path planning scenarios. Experimental results show that MSO achieves faster convergence speed, stronger robustness and greater stability, effectively extending the biomimetic principles of the original SO and confirming its superiority for solving optimization problems.

The efficacy of swarm intelligence algorithms in navigating high-dimensional, non-convex landscapes depends on the dynamic balance between global exploration and local exploitation. Drawing inspiration from the intricate social dynamics of Pyrrhura molinae, this study proposes a novel bio-inspired metaheuristic, the Comprehensive Learning Parrot Optimizer (CL-PO). While the original Parrot Optimizer (PO) simulates collective foraging and communication, it often suffers from population homogenization and entrapment in local optima due to its reliance on single-source social learning. To address these limitations, CL-PO incorporates a dimension-wise multi-exemplar social learning mechanism analogous to the cross-individual knowledge transfer observed in avian colonies. This strategy enables stagnant individuals to reconstruct their search trajectories by learning from multiple superior peers, thereby sustaining population diversity and facilitating adaptive exploration. Rigorous benchmarking on 29 test functions from the CEC 2017 suite reveals that CL-PO achieves statistically superior performance compared to nine state-of-the-art algorithms, securing a top-tier average Friedman rank of 1.28. Furthermore, the practical utility of CL-PO is substantiated through a complex reservoir production optimization task using the Egg benchmark model, where it consistently maximizes the net present value (NPV), reaching 9.625×108 USD. These findings demonstrate that CL-PO is a powerful and reliable solver for addressing large-scale engineering optimization problems under complex constraints.

To address the limitations of current prosthetic knees that lack personalized adaptability to users' gait characteristics and walking speeds, this study proposes a personalized gait parameter prediction-based speed-adaptive control method for a hybrid active-passive intelligent prosthetic knee (HAPK). The proposed system integrates a perceptron-based model to predict individualized gait parameters by mapping anthropometric data and walking speed to key points of the knee trajectory. A fuzzy logic-based damping control for the swing phase and a position-torque control for the stance extension phase are developed to achieve real-time adaptation to different walking speeds and user-specific biomechanics. The hybrid actuation system combines hydraulic damping and motor torque assistance to ensure both compliance and power delivery across gait phases. Experimental results from variable-speed walking tests demonstrate that the proposed control method improves gait symmetry indices-reducing stance and swing asymmetries by approximately 30-38%-and achieves smoother, more natural gait transitions compared to traditional fixed-gait control strategies. These findings validate the effectiveness of the proposed approach in achieving continuous, personalized, and speed-consistent gait control for intelligent prosthetic knees.

Hexapod walking robots are a subject of intense research in the existing literature. To move effectively in natural terrain, these robots must be able to adapt to surface irregularities. While most existing designs employ sophisticated technical solutions for the leg mechanisms, none of these projects allow for combined roll and pitch movements of the body segments. This paper addresses this gap, presenting the concept of a hexapod robot with a body formed of three segments connected by two active universal joints. This unique architecture allows the robot to locomote on both sides and autonomously recover from a rollover event. The robot's legs are underactuated, utilizing a passive spring element to simplify the mechanical design and control system while maintaining effective terrain adaptation capabilities. Experimental results are presented and discussed, validating the theoretical model and demonstrating the effectiveness of the proposed solution on varied terrains.

Legged robots inspired by animal locomotion require actuators with high power density, fast response, and robust force control, yet traditional valve-controlled hydraulic systems suffer from substantial energy losses and weak regeneration performance. Motivated by role allocation across gait phases in animal legs, where in-air positioning requires far less actuation effort than ground contact support and force modulation, this work proposes a novel gas-oil hybrid servo actuator, denoted GOhsa, for quadruped knee joints. GOhsa utilizes pre-charged high-pressure gas to pressurize hydraulic oil, converting the conventional dual-chamber pressure servo control into a single-chamber configuration while preserving the original piston stroke. This architecture enables bidirectional position-force control, enhances energy regeneration applicability, and improves operational efficiency. Theoretical modeling is conducted to analyze hydraulic stiffness and frequency-response characteristics, and a linearization-based force controller with dynamic compensation is developed to handle system nonlinearities. Experimental validation on a single-leg platform demonstrates significant energy-saving performance: under no-load conditions (simulating the swing phase), GOhsa achieves a maximum power reduction of 79.1%, with average reductions of 15.2% and 11.5% at inflation pressures of 3 MPa and 4 MPa, respectively. Under loaded conditions (simulating the stance phase), the maximum reduction reaches 28.0%, with average savings of 10.0% and 9.8%. Tracking accuracy is comparable to traditional actuators, with reduced maximum errors (13.7 mm/16.5 mm at 3 MPa; 15.0 mm/17.8 mm at 4 MPa) relative to the 16.6 mm and 18.1 mm errors of the conventional system, confirming improved motion stability under load. These results verify that GOhsa provides high control performance with markedly enhanced energy efficiency.

Humanoid mobile manipulators integrate a humanoid upper body with a mobile platform, forming a highly redundant system capable of performing complex manipulation tasks. To address the redundancy arising from the coordinated motion of the wheeled base, waist, and dual arms, this study proposes a human-inspired holistic control method based on multi-objective optimization. The degrees of freedom (DOF) of the upper limbs and the mobile base are unified within a single control framework, thereby enhancing overall motion coordination. Specifically, the controller is formulated as a strictly convex quadratic program (QP) that ensures accurate end-effector tracking while effectively handling joint position and velocity constraints. Inspired by human motor characteristics, the method incorporates a hierarchical weight assignment strategy and base DOF optimization to preserve arm manipulability while achieving effective coordination between the base and waist. Simulation studies of dual-arm handling tasks and real-world experiments involving mobile handling and peg-in-hole assembly demonstrate that the proposed method generates smooth, humanoid-like motions, thereby validating the effectiveness of the proposed control framework.

As a core functional component of the prosthetic system, the prosthetic socket's adaptability to the residual limb is directly correlated with the prosthetic's performance, comfort level, and safety profile. Although traditional sockets can satisfy basic suspension requirements, they commonly suffer from inherent drawbacks in practical applications, including uneven pressure distribution, poor air permeability, and inadequate adaptability to the morphological variations of individual residual limbs. To enhance socket adaptability across multi-task scenarios, this study proposes an intelligent physiological adaptation-based optimal design method for active upper-limb prosthetic sockets. Specifically, this method first employs a dynamic force optimization algorithm for multi-contact units oriented to prosthetic manipulation tasks, which real-timely optimizes the output force of each unit under varying external loads to achieve stable socket suspension with minimal interface pressure. Second, biomechanical experiments are conducted to obtain the pain threshold distribution characteristics of forearm soft tissues under compressive loads, thereby providing a physiological basis for the spatial layout of the contact units. Furthermore, the mechanical performance of different socket structures is evaluated under various representative task scenarios, with peak normal force, mean normal force, and force distribution variance adopted as the key comfort evaluation indices. The results demonstrate that the proposed active multi-unit socket, particularly the double-layered eight-unit symmetric radial staggered configuration, enables a robust balance between comfort and stability across diverse task scenarios, thereby establishing an effective and scalable design paradigm for long-term adaptive upper-limb prosthetic sockets.