Bionic Attitude Transformation Combined with Closed Motion for a Free Floating Space Robot

In order to realize the small error attitude transformation of a free floating space robot,a new method of three degrees of freedom（ DOF） attitude transformation was proposed for the space robot using a bionic joint. A general kinematic model of the space robot was established based on the law of linear and angular momentum conservation. A combinational joint model was established combined with bionic joint and closed motion. The attitude transformation of planar,two DOF and three DOF is analyzed and simulated by the model,and it is verified that the feasibility of attitude transformation in three DOF space. Finally,the specific scheme of disturbance elimination in attitude transformation is presented and simulation results are obtained.Therefore,the range of application field of the bionic joint model has been expanded.

Dynamic model and performance analysis of landing buffer for bionic locust mechanism

The landing buffer is an important problem in the research on bionic locust jumping robots, and the different modes of landing and buffering can affect the dynamic performance of the buffering process significantly. Based on an experimental observation, the different modes of landing and buffering are determined, which include the different numbers of landing legs and different motion modes of legs in the buffering process. Then a bionic locust mechanism is established, and the springs are used to replace the leg muscles to achieve a buffering effect. To reveal the dynamic performance in the buffering process of the bionic locust mechanism, a dynamic model is established with different modes of landing and buffering. In particular, to analyze the buffering process conveniently, an equivalent vibration dynamic model of the bionic locust mechanism is proposed.Given the support forces of the ground to the leg links, which can be obtained from the dynamic model, the spring forces of the legs and the impact resistance of each leg are the important parameters affecting buffering performance, and evaluation principles for buffering performance are proposed according to the aforementioned parameters. Based on the dynamic model and these evaluation principles, the buffering performances are analyzed and compared in different modes of landing and buffering on a horizontal plane and an inclined plane. The results show that the mechanism with the ends of the legs sliding can obtain a better dynamic performance. This study offers primary theories for buffering dynamics and an evaluation of landing buffer performance,and it establishes a theoretical basis for studies and engineering applications.

Preliminary studies on the basic factors of bionics

The bionic consciousness,idea,and practice opened a unique path for the progress of mankind,the development of the society,and the innovation of science and technology from the subconscious bionic activities of the ancient humans to the significant bionic designs in modern engineering.Nowadays,driven by the practical demand of human beings,bionics becomes an important factor for the sustainable development of technology.A lot of new and outstanding innovations have been produced through the effective interactions between bionics,technology,and demand.The stronger the interactions,the greater the innovation success would be.In this article,the basic factors such as the connotation,characteristics,and interactions of bionic demands,bionic models,bionic simulations,and bionic products were explained,which are the indispensable basic knowledge for improving the ability of innovation especially for the original one,realizing the design and innovation of new technology and manufacturing for better bionic products.

A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and intelligence.However,the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive enough.Hence,a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the paper.Firstly,four classical neural network models are illustrated:Back Propagation(BP)network,Deep Belief Network(DBN),LeNet5 network,and olfactory bionic model(KIII model),and the neuron transmission mode and equation,network structure,and weight updating principle of the models are analyzed qualitatively.The analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models,and the LeNet5 network simulates the nervous system in depth.Secondly,evaluation indexes of ANN are constructed from the perspective of bionics in this paper:small-world,synchronous,and chaotic characteristics.Finally,the network model is quantitatively analyzed by evaluation indexes from the perspective of bionics.The experimental results show that the DBN network,LeNet5 network,and BP network have synchronous characteristics.And the DBN network and LeNet5 network have certain chaotic characteristics,but there is still a certain distance between the three classical neural networks and actual biological neural networks.The KIII model has certain small-world characteristics in structure,and its network also exhibits synchronization characteristics and chaotic characteristics.Compared with the DBN network,LeNet5 network,and the BP network,the KIII model is closer to the real biological neural network.

A biologically inspired model for pattern recognition

In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent.

Differential Evolution-Boosted Sine Cosine Golden Eagle Optimizer with Lévy Flight

Golden eagle optimizer(GEO)is a recently introduced nature-inspired metaheuristic algorithm,which simulates the spiral hunting behavior of golden eagles in nature.Regrettably,the GEO suffers from the challenges of low diversity,slow iteration speed,and stagnation in local optimization when dealing with complicated optimization problems.To ameliorate these deficiencies,an improved hybrid GEO called IGEO,combined with Lévy flight,sine cosine algorithm and differential evolution(DE)strategy,is developed in this paper.The Lévy flight strategy is introduced into the initial stage to increase the diversity of the golden eagle population and make the initial population more abundant;meanwhile,the sine-cosine function can enhance the exploration ability of GEO and decrease the possibility of GEO falling into the local optima.Furthermore,the DE strategy is used in the exploration and exploitation stage to improve accuracy and convergence speed of GEO.Finally,the superiority of the presented IGEO are comprehensively verified by comparing GEO and several state-of-the-art algorithms using(1)the CEC 2017 and CEC 2019 benchmark functions and(2)5 real-world engineering problems respectively.The comparison results demonstrate that the proposed IGEO is a powerful and attractive alternative for solving engineering optimization problems.

Bioinspired Musculoskeletal Model-based Soft Wrist Exoskeleton for Stroke Rehabilitation

Exoskeleton robots have demonstrated the potential to rehabilitate stroke dyskinesia.Unfortunately,poor human-machine physiological coupling causes unexpected damage to human of muscles and joints.Moreover,inferior humanoid kinematics control would restrict human natural kinematics.Failing to deal with these problems results in bottlenecks and hinders its application.In this paper,the simplified muscle model and muscle-liked kinematics model were proposed,based on which a soft wrist exoskeleton was established to realize natural human interaction.Firstly,we simplified the redundant muscular system related to the wrist joint from ten muscles to four,so as to realize the human-robot physiological coupling.Then,according to the above human-like musculoskeletal model,the humanoid distributed kinematics control was established to achieve the two DOFs coupling kinematics of the wrist.The results show that the wearer of an exoskeleton could reduce muscle activation and joint force by 43.3%and 35.6%,respectively.Additionally,the humanoid motion trajectories similarity of the robot reached 91.5%.Stroke patients could recover 90.3%of natural motion ability to satisfy for most daily activities.This work provides a fundamental understanding on human-machine physiological coupling and humanoid kinematics control of the exoskeleton robots for reducing the post-stroke complications.