Bio-inspired Mechanism Research Articles

Bio-Inspired Vibration Isolation: Methodology and Design

AbstractVarious bio-inspired vibration isolators have been emerged in recent decades and applied successfully in the protection of sensitive components, improvement of operating comfort, enhancement of control accuracy, etc. They are generally developed by exploiting favorable nonlinearities in biological structures. The main contribution of this work is to provide a comprehensive review of recent studies on the bio-inspired isolators. The methodology of bio-inspired vibration isolation is proposed from the perspective of mechanics based on the elemental theory and design principles. The key isolation mechanisms are classified into three categories according to different dominant forces: stiffness adjustment mechanism, auxiliary mass mechanism, and damping mechanism, respectively. Some representative designs, performance analyses, and practical applications of each type of bio-inspired isolators are also provided. In bio-inspired isolators with variable stiffness, the inherent structural performances can be adjusted to deal with variation in external load. The auxiliary mass mechanism utilizes nonlinear inertial effects to achieve ultralow frequency vibration isolation. Unique damping mechanism of bio-inspired structures is often studied to protect devices and equipment from impact loads. Bio-inspired vibration methods can also be applied in active/semi-active control systems with advantages of low energy consumption and high robustness. Finally, the review ends with conclusions, which highlight resolved and unresolved issues and provide a brief outlook on future perspectives. This review aims to give a comprehensive understanding of bio-inspired isolation mechanism. It also provides guidance on designing new bio-inspired isolators for improving their vibration isolation performance.

Spiky-joint: a bioinspired solution to combine mobility and support

Mobility and support are two structural properties that are often mutually exclusive. However, combining them could enhance the performance of mechanical components, and offer novel technical applications. Here through the implementation of a bioinspired interlocking mechanism in the design of a supportive, yet mobile, wrist splint, we tackled the conflicting combination of the two properties. We elaborated our design into a technology readiness level and, using 3D printing, directly converted it into a real-life application. In contrast to the existing splints, our bioinspired splint supports human wrist without impairing its movements. Hence, it can be used to prevent hyperextension injuries without hindering wrist function. By being interlocked at the maximum wrist extension, our splint could be an ideal wrist support for athletes, especially weightlifters. By restricting the wrist mobility, it could also be used as a support device to treat less severe medical issues, such as sprain, strain, or even for the recovery after cast removal, during which full immobilization may result in muscle atrophy. Our design strategy is purely structural; hence, it can be easily modified and implemented in other engineering applications. The simple, yet efficient, solution developed in this study offers a universal paradigm for developing engineering systems that pursuit both mobility and support.Graphic abstract

Modeling, comparison and evaluation of one-DOF six-bar bioinspired jumping leg mechanisms

One degree-of-freedom (DOF) jumping leg has the advantages of simple control and high stiffness, and it has been widely used in bioinspired jumping robots. Compared with four-bar jumping leg, six-bar jumping leg mechanism can make the robot achieve more abundant motion rules. However, the differences among different configurations have not been analyzed, and the choice of configurations lacks basis. In this study, five Watt-type six-bar jumping leg mechanisms were selected as research objects according to the different selection of equivalent tibia, femur and trunk link, and a method for determining the dimension of the jumping leg was proposed based on the movement law of jumping leg of locust in take-off phase. On this basis, kinematics indices (sensitivity of take-off direction angle and trunk attitude angle), dynamics indices (velocity loss, acceleration fluctuation, and mean and variance of total inertial moment) and structure index (distribution of center of mass) were established, and the differences of different configurations were compared and analyzed in detail. Finally, according to the principal component analysis method, the optimal selection method for different configurations was proposed. This study provides a reference for the design of one DOF bioinspired mechanism.

Prey Capturing Dynamics and Nanomechanically Graded Cutting Apparatus of Dragonfly Nymph.

Aquatic predatory insects, like the nymphs of a dragonfly, use rapid movements to catch their prey and it presents challenges in terms of movements due to drag forces. Dragonfly nymphs are known to be voracious predators with structures and movements that are yet to be fully understood. Thus, we examine two main mouthparts of the dragonfly nymph (Libellulidae: Insecta: Odonata) that are used in prey capturing and cutting the prey. To observe and analyze the preying mechanism under water, we used high-speed photography and, electron microscopy. The morphological details suggest that the prey-capturing labium is a complex grasping mechanism with additional sensory organs that serve some functionality. The time taken for the protraction and retraction of labium during prey capture was estimated to be 187 ± 54 ms, suggesting that these nymphs have a rapid prey mechanism. The Young’s modulus and hardness of the mandibles were estimated to be 9.1 ± 1.9 GPa and 0.85 ± 0.13 GPa, respectively. Such mechanical properties of the mandibles make them hard tools that can cut into the exoskeleton of the prey and also resistant to wear. Thus, studying such mechanisms with their sensory capabilities provides a unique opportunity to design and develop bioinspired underwater deployable mechanisms.

Dynamic structural colour increases photosynthetic performance in the alga Ericaria selaginoides

ABSTRACT Structural colour occurs when nanoscale structures interfere with incident light transmission and reflect particular wavelengths. The brown alga Ericaria selaginoides (Fucales, Phaeophyceae) has an opalescent photonic crystal anatomy that creates the blue colour of this seaweed but which responds to light, leading to speculation that the crystals have a photosynthetic role by modifying light transmission. Here, we characterize the colour response of E. selaginoides using time-lapse photography to capture responses to light treatments of different timing, duration, intensity and spectrum. The amount of light drove the colour response, with the most intense blue found in the dark which reduced rapidly in strong light and was not spectrum dependent. Chlorophyll a fluorometry showed that the maximum quantum efficiency of photosynthesis coincided with strong blue colouration but was reduced over two to three hours of illumination. This supports opalescent photonic crystals having a photosynthetic role by regulating light transmission to chloroplasts. Studies such as this could be used to improve solar cell efficiency and increase crop yield through the use of bio-inspired self-tuning mechanisms to optimize light transmission.

Classification of diversified web crawler accesses inspired by biological adaptation

To discover and prevent attacks, it is necessary to collect data about the attacks using honeypots and to identify malicious accesses from collected data. In this study, we focus on detecting a massive number of crawler accesses, which complicates the detection of malicious accesses. We adapt AntTree, a bio-inspired clustering scheme that is highly scalable and adaptable, for crawler detection. We also designed a feature vector for crawler detection and propose a cluster interpretation method of AntTree. Our results show that the proposed bio-inspired mechanism can detect crawlers with a low false-negative rate, which is an advantage over conventional schemes for detecting various types of crawler.

Integrative design and fabrication methodology for bio-inspired folding mechanisms for architectural applications

Adaptive shading devices are of special interest in the context of global need for reducing greenhouse gas emissions. To reduce mechanical complexity of kinetic architectural applications, the investigation of bio-inspired compliant mechanisms has proven to be a promising approach.Thus, a coherent integrative design framework for bio-inspired kinetic folding mechanisms has been developed. This includes the abstraction process of biological principles, such as kinematic behaviours, actuation, as well as materialisation principles. A computational design and simulation model is built to analyse the kinematic and kinetic behaviour of the abstracted biological principles under consideration of specific materialisation and fabrication parameters and constraints. The design and simulation model builds the basis for an automated fabrication process, as well as for interaction and active control of the physical application. The development of the ITECH Research Demonstrator 2018–19,​ a large-scale compliant folding mechanism, inspired by the folding behaviour ladybug wings (Coleptera coccinellidae), serves as a case study of the developed process. The folding motion of the demonstrator is facilitated by distinct elastic hinge-zones with integrated pneumatic actuators.

Implementing Rat-Like Motion for a Small-Sized Biomimetic Robot Based on Extraction of Key Movement Joints

For a small-sized biomimetic robot, it is challenging to mimic animal-like motion with high speed and high flexibility. To enable high flexibility, high stability, and high biomimicry degree for the robotic rat, we drew inspirations from three agile rat movements, namely, the pitch, yaw, and U-turn movements. First, we proposed key movement joints (KMJs) to capture a decent representation of the rat with a reduced-order model. By extracting the primary KMJs, we determined the number and distribution of robotic joints for the design of a bioinspired spine mechanism. Second, to meet the demand of high biomimicry degree, we generated an optimal compensation term to minimize the trajectory error introduced by simplifying the model. Moreover, we calculated the optimal minimum motion cycle based on the constraints of equilibrium under extreme conditions to ensure high flexibility without compromising the stability. Finally, the proposed method was successfully verified through simulation and experimental tests with a robotic rat endowed with the bioinspired spine mechanism.

Self-Administered Information Sharing Framework Using Bioinspired Mechanisms

The promising potential of distributed and interconnected lightweight devices that can jointly generate superior information-collecting and problem-solving abilities has long fostered various significant and ubiquitous techniques, from wireless sensor networks (WSNs) to Internet of Things (IoT). Although related applications have been widely used in different domains in attempting to collect and harness the ever-growing information flows, one major issue that impedes the further advancement of WSNs or IoT-based applications is the restricted battery power. Previous research mainly focuses on investigating novel protocols to save energy by reducing data traffic with the aid of optimal or heuristic algorithms. However, data packet behaviours and significant parameters involved are mostly preconfigured in a supervised-learning fashion rather than using an unsupervised learning paradigm and therefore may not adapt to uncertain or fast-changing environments. Hence, this paper concentrates on optimising the behaviours of data packets and significant parameters in a widely tested routing protocol, namely, Cognitive Packet Network (CPN), with the aid of several bio-inspired algorithms to increase the efficiency of energy usage and information acquisition. Two novel packet behaviours are introduced, and an on-line parameter calibration scheme is proposed to realise packet time-to-live (TTL) adjustment and rate adaptation. The simulation results show that the introduction of the bioinspired algorithms can improve the efficiency of information sharing and reduce the energy consumption.

A Multiplex Social Contagion Dynamics Model to Shape and Discriminate D2D Content Dissemination

5G network technology is growing fast, thus the number of devices and the traffic are likely to pose impressive challenges. A new paradigm called “Internet-of-People” (IoP) represents a valid approach to include the social aspect. Following an IoP perspective, we believe that the knowledge of social multiplex interactions and dynamics could drive more sustainable growth. By merging this with the Device-to-Device communication (D2D), we originate a new paradigm presented in this work. We propose a novel bio-inspired approach for quantifying the impact of the social multiplex structure on D2D contents' dissemination. Through rigorous mathematical modelling, we have shaped the D2D data dissemination process as a social contagion dynamics of two co-evolving spreading processes. We weigh the dynamic interactions by including the concepts of homophily and awareness. We have measured the effect of homophily, awareness and network heterogeneity on information diffusion. The bio-inspired mechanism is evaluated through a rigorous mathematical and algorithm analysis, and a meaningful simulation. We show that this mechanism is effective in tuning network awareness and alertness, breaking the “echo chambers” effect. Through our model, we have defined and proposed the guidelines to discriminate the nature of the contents based on contents' dissemination.

Crossmodal Language Grounding in an Embodied Neurocognitive Model.

Human infants are able to acquire natural language seemingly easily at an early age. Their language learning seems to occur simultaneously with learning other cognitive functions as well as with playful interactions with the environment and caregivers. From a neuroscientific perspective, natural language is embodied, grounded in most, if not all, sensory and sensorimotor modalities, and acquired by means of crossmodal integration. However, characterizing the underlying mechanisms in the brain is difficult and explaining the grounding of language in crossmodal perception and action remains challenging. In this paper, we present a neurocognitive model for language grounding which reflects bio-inspired mechanisms such as an implicit adaptation of timescales as well as end-to-end multimodal abstraction. It addresses developmental robotic interaction and extends its learning capabilities using larger-scale knowledge-based data. In our scenario, we utilize the humanoid robot NICO in obtaining the EMIL data collection, in which the cognitive robot interacts with objects in a children's playground environment while receiving linguistic labels from a caregiver. The model analysis shows that crossmodally integrated representations are sufficient for acquiring language merely from sensory input through interaction with objects in an environment. The representations self-organize hierarchically and embed temporal and spatial information through composition and decomposition. This model can also provide the basis for further crossmodal integration of perceptually grounded cognitive representations.

Kinematic Modeling and Analysis of a Novel Bio-Inspired and Cable-Driven Hybrid Shoulder Mechanism

Abstract The robotic shoulder rehabilitation exoskeletons that do not take into consideration all shoulder degrees-of-freedom (DOFs) lead to undesirable interaction forces and cause discomfort to the patient due to the joint axes misalignments between the exoskeleton and shoulder joints. In order to contribute to the solution of this human–robot compatibility issue, we present the kinematic modeling and analysis of a novel bio-inspired 5-DOFs hybrid human–robot mechanism (HRM). The human limbs are regarded as the inner passive restrained links in the proposed hybrid constrained anthropomorphic mechanism. The proposed hybrid mechanism combines serial and parallel manipulators with rigid and cable links enabling a match between human and exoskeleton joint axes. It is designed to cover the whole range of motion of the human shoulder with the workspace free of singularities. The numerical and simulation results from the computer-aided drawing model of the mechanism are presented to demonstrate the validity of the kinematic model, and the kinematic and singularity merits of the proposed mechanism. A three-dimensional printed prototype of the hybrid mechanism was fabricated to further validate the kinematic model and its overall advantages.

Computational Structure Design of a Bio-Inspired Armwing Mechanism

Bat membranous wings possess unique functions that make them a good example to take inspiration from and transform current aerial drones. In contrast with other flying vertebrates, bats have an extremely articulated musculoskeletal system which is key to their energetic efficiency with impressively adaptive and multimodal locomotion. Biomimicry of this flight apparatus is a significant engineering ordeal and we seek to achieve mechanical intelligence through sophisticated interactions of morphology. Such morphological computation or mechanical intelligence draws our attention to the obvious fact that there is a common interconnection between the boundaries of morphology and closed-loop feedback. In this work, we demonstrate that several biologically meaningful degrees of freedom can be interconnected to one another by mechanical intelligence and, as a result, the responsibility of feedback-driven components (e.g., actuated joints) is subsumed under computational morphology. The results reported in this work significantly contribute to the design of bio-inspired Micro Aerial Vehicles (MAVs) with articulated body and attributes such as efficiency, safety, and collision-tolerance.

Design method of one-DOF bio-inspired mechanism based on layered constraint conditions

Bio-inspired mechanism not only needs to be able to reproduce the movement laws of creature, but also has its shape. In order to simplify the design process and improve the design efficiency of one-degree-of-freedom (DOF) bio-inspired mechanism (especially six-bar or eight-bar mechanism), a design method based on layered constraint conditions is proposed in this paper. On the basis of the one-DOF kinematic chain, the frame is selected and the links are added in turn. At the same time, the constraint conditions are given in layers in the form of inequalities. When a closed loop is formed, the kinematic model needs to be established, and the positions of the target points or the attitudes of the target links can be represented by the driving angles or given angles. Thus, the feasible configurations and the ranges of the link lengths can be obtained. By cycling the above process, the target points can approach the reference points quickly and the design efficiency of the mechanism can be improved. The effect of the different basic kinematic chain, closed-loop forms and constraint conditions on configurations and design efficiency is analyzed in detail. It provides a reference for the design of other one-DOF mechanisms with multiple constraint conditions.

FEM-Based Mechanics Modeling of Bio-Inspired Compliant Mechanisms for Medical Applications

Compliant mechanisms are widely used in the design of medical robotics and devices because of their monolithic structure and high flexibility. Many compliant mechanisms derive their design ideas from nature, since the structure of biological organisms sometimes offers a better solution than the conventional mechanisms. However, the bio-inspired structures usually have very complex geometries which cannot be easily modeled and analyzed using traditional methods. In this paper, we present a novel finite element method (FEM) based modeling framework in MATLAB to analyze the mechanics of different bio-inspired compliant mechanisms. Since the basic linear FEM formulation can only be employed to model small displacements of compliant mechanisms, a non-linear FEM formulation that integrates the modeling of large displacements, tendon-driven mechanisms and contact problems was implemented in the proposed framework to overcome the limitations. Simulations and experiments were also conducted to evaluate the performance of the modeling framework. Results have demonstrated the accuracy and plausibility of the proposed non-linear FEM formulation. Furthermore, the proposed framework can also be used to achieve structural optimization of bio-inspired compliant mechanisms.

Lightweight, compression-resistant cellular structures inspired from the infructescence of Liquidambar formosana

In order to adapt to the environment, plants have evolved many structural designs to improve material utilization. The head infructescence can be described as the Fibonacci sequence, in consistent with plant developmental biology. The lignified framework inside the head infructescence possesses idiographic structural designs that optimize maximum energy efficiency, growing space, seed spreading probability, and enhance the mechanical behavior of the infructescences. In this study, the hierarchical structure and mechanical properties of the infructescence of Liquidambar formosana, commonly called Formosan gum, were investigated. Liquidambar formosana has maple-like leaves and burr-like infructescences. The buckyball-like framework inside infructescence consists of chambers (cells), which support the whole structure under compression. Inspired by the framework, we proposed three models: Thomson model based on the lowest potential energy state, Poisson disc model indicated random distribution, and spherical Fibonacci model represented plant development. Three-dimensional physical entities of these models were fabricated by additive manufacturing. We discovered that under compression testing, these models appear different mechanical properties and deformation mechanisms based on their structures. Spherical Fibonacci model provides superior mechanical properties compared to Thomson and Poisson disc models due to its unique structural design. It is the first time that spherical Fibonacci model brought into the bio-inspired mechanics models through structural analysis and finite element method. The unique construction of Liquidambar formosana has great potential in the designs of novel lightweight, anti-buckling composites, and bio-inspired architectures.

Bio-inspired robotic impedance adaptation for human-robot collaborative tasks

To improve the robotic flexibility and dexterity in a human-robot collaboration task, it is important to adapt the robot impedance in a real-time manner to its partners behavior. However, it is often quite challenging to achieve this goal and has not been well addressed yet. In this paper, we propose a bio-inspired approach as a possible solution, which enables the online adaptation of robotic impedance in the unknown and dynamic environment. Specifically, the bio-inspired mechanism is derived from the human motor learning, and it can automatically adapt the robotic impedance and feedforward torque along the motion trajectory. It can enable the learning of compliant robotic behaviors to meet the dynamic requirements of the interactions. In order to validate the proposed approach, an experiment containing an anti-disturbance test and a human-robot collaborative sawing task has been conducted.

A biologically inspired visual integrated model for image classification

In this paper, we propose a biologically inspired visual integrated model for image classification, called VMVI-CNN. Motivated in part by recent neuroscience progress in revealing integrated functions of human visual system, two bio-inspired visual mechanisms (the visual memory decay mechanism and the visual interaction mechanism) are proposed and built within the VMVI-CNN to (1) control the feature information passing through, and (2) increase the richness of feature information. The proposed method is tested on three benchmark datasets (MNIST, Cifar-10, and Mini-ImageNet) and a real-world industrial dataset. The results demonstrate that the new model can extract distinctive features and exhibit a better recognition performance than the current state-of-the-art approaches.

A Memristor-Based Spiking Neural Network With High Scalability and Learning Efficiency

Spike-timing dependent plasticity (STDP)-based spiking neural network (SNN) is a promising choice to realize unsupervised intelligent systems with a limited power budget. In addition to STDP, another two bio-inspired mechanisms of lateral inhibition and homeostasis are always implemented in the unsupervised training procedure of STDP-based SNNs. However, the existing methods to achieve lateral inhibition necessitate a great number of connections that are proportional to the square of the number of learning neurons, and the existing hardware solution of homeostasis demands complex circuits for each learning neuron, both of which challenge the hardware implementation of STDP-based SNNs. In this brief, we propose a novel SNN using memristor-based inhibitory synapses to realize the mechanisms of lateral inhibition and homeostasis with low hardware complexity. The proposed SNN can improve the network scalability by reducing the connection number for lateral inhibition from $N^{2}$ to $N$ and reduce the hardware overhead by leveraging the circuit of lateral inhibition to achieve homeostasis. Software simulations on the recognition task on MNIST dataset show that the proposed SNN achieves a ~ 2 times higher learning efficiency with comparable accuracy. In addition, the challenging properties of realistic memristor devices, including limited number of resistive states, intrinsic parameter variation, and permanent open device, are added in the simulation to evaluate the robustness of our proposed approach.

Nanopumping of water via rotation of graphene nanoribbons

In this paper, we perform molecular dynamics simulations to propose a novel bio-inspired nanopumping mechanism that is achieved through the rotation of graphene nanoribbons. Due to the rotation and interaction with water, the graphene nanoribbons undergo morphological transformation. It is shown that with appropriate geometrical and spatial parameters, the resulting morphology is twisted ribbon, which is efficient in pumping of water through a channel. This mimics the propulsive behavior of bacterial flagella through continual rotation at the base and causing morphology of the geometry into twisted ribbons, thus driving flow. It was observed that the maximum flux rate decreases upon reaching the optimal configuration even with increasing rotational speed and graphene width. This is due to the development of cavitation near the region of the nanoribbon with tip velocities approaching the speed of sound in water. The simulation shows promising results where the flux rate of the driven flow outperforms various nanopump configurations that have been reported in recent literature by more than one order.