Research on the Application of Intelligent Bionic Robot Horse in Juvenile Equestrian Teaching Case Study: Beijing Chaoyang Park Youth Equestrian Center

Research purposes:in this study,the intelligent bionic robotic horse is introduced into the equestrian teaching for teenagers,compared with the traditional teaching mode of using real horses.This research aims to explore the effectiveness of using intelligent bionic robotic horse in equestrian teaching for teenagers,as well as to promote the further development of equestrian teaching for teenagers in China,and to promote the introduction of new technology into the equestrian teaching area in the age of internet.Research methods:the methods used were literature method;mathematical statistics;interviewing the equestrian coaches who participated in the experiment;experimental method.The intelligent bionic robotic horse used in this research is the GETTAEN intelligent bionic robotic horse produced by Joy Game Technology Co.,Ltd.The bionic robotic horse is equipped with Internet technology,and the course is supervised and produced by senior coaches of China Equestrian Team.It also includes multiple operation modes.In this study,40 amateur students in Beijing Chaoyang Park Youth Equestrian Center were selected as the experimental subjects.Students will spend 40 h on studying how to ride a horse.Twenty(20)students in the experimental group,they are accommodated with 20 h of bionic robotic horse courses and 20 h of real horse course;20 students in the control group were taught in the traditional teaching mode with 40 h of real horse courses.Results:(1)in horseback physical fitness test,the average value of the control group was 101.9 s;325.6 s in the experimental group.Independent sample T test p<0.05 has significant difference,the horseback physical performance in experimental group is better than the control group.(2)in horseback physical balance test,the average value of the control group was 3.75,and the average value of the experimental group was 7.1.Independent sample T test p<0.05 has significant difference,the horseback physical balance test results in experimental group have significant difference,and the experimental group is better than the control group.(3)The interview method was used to interview the equestrian coaches who participated in the experiment,coaches think that the bionic robotic horse can speed up the learning progress and has a strong technical consolidation,especially for teaching amateurs;but for the time being,it cannot meet the training and improvement target of the actual horse control ability and the ability to grasp the route,and such experience is not real and good enough for senior students.Conclusion:using real horse and intelligent bionic robotic horse combined,one can improve the teaching effectiveness and promote students’adaptation to horseback and technical mastery.But for the time being,it is only suitable for students with weak foundation or zero foundation.The capability of intelligent bionic robotic horse needs to be strengthened,and technological innovation is needed to adapt to all kinds of students.

The Knee Joint Design and Control of Above-knee Intelligent Bionic Leg Based on Magneto-rheological Damper

The above-knee intelligent bionic leg is very helpful to amputees in the area of rehabilitation medicine. This paper first introduces the functional demand of the above-knee prosthesis design. Then, the advantages of the four-bar link mechanism and the magneto-rheological （MR） damper are analyzed in detail. The fixed position of the MR damper is optimized and a virtual prototype of knee joint is given. In the end, the system model of kinematics, dynamics, and controller are given and a control experiment is performed. The control experiment indicates that the intelligent bionic leg with multi-axis knee is able to realize gait tracking of the amputee＇s healthy leg based on semi-active control of the MR damper.

sEMG Pattern Recognition of Muscle Force of Upper Arm for Intelligent Bionic Limb Control

Two new feature extraction methods, window sample entropy and window kurtosis, were proposed, which mainly aims to complete the surface Elcctromyography （sEMG）-muscle force pattern recognition for intelligent bionic limb. The inspiration is drawn from physiological process of muscle force generation. Five hand movement tasks were implemented for sEMG-muscle force data record. With two classical features： Integrated Electromyography （IEMG） and Root Mean Square （RMS）, two new features were fed into the wavelet neural network to predict the muscle force. To solve the issues that amputates＇ residual limb couldn＇t provide full train data for pattern recognition, it is proposed that force was predicted by neural network which is trained by contralateral data in this paper. The feasibility of the proposed features extraction methods was demonstrated by both ipsi- lateral and contralateral experimental results. The ipsilateral experimental results give very promising pattern classification accuracy with normalized mean square 0.58 ± 0.05. In addition, unilateral transradial amputees will benefit from the proposed method in the contralateral experiment, which probably helps them to train the intelligent bionic limb by their own sEMG.

Intelligent PID controller based on ant system algorithm and fuzzy inference and its application to bionic artificial leg

A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters K*p , Ti* and T*d can be obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , Ti*and Td* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.

四足仿生巡检机器人在上湾选煤厂的应用

为解决生产系统核心设备依靠人工巡视和检查的问题,上湾选煤厂在智能化建设过程中研发了具备自主行走、自主定位、自主导航、故障诊断等功能的四足仿生巡检机器人,通过对该机器人的多层次巡检场景下路径规划、步态生成与步态转换等技术的研究,使其具备在选煤厂非结构化环境内精确定位和稳定行走的能力,且通过搭载多种传感器实现了对选煤厂生产现场有害气体、温度、湿度等环境因素的实时监测。实际应用结果表明,该机器人能够替代人工完成巡检任务,并具有环境检测、状态判断、异常分析及故障预警等功能。

Blackboard Mechanism Based Ant Colony Theory for Dynamic Deployment of Mobile Sensor Networks

A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard mechanism is introduced into the system for making pheromone and completing the algorithm. Every node, which can be looked as an ant, makes one information zone in its memory for communicating with other nodes and leaves pheromone, which is created by ant itself in naalre. Then ant colony theory is used to find the optimization scheme for path planning and deployment of mobile Wireless Sensor Network （WSN）. We test the algorithm in a dynamic and unconfigurable environment. The results indicate that the algorithm can reduce the power consumption by 13% averagely, enhance the efficiency of path planning and deployment of mobile WSN by 15% averagely.

Robot Navigation Strategy in Complex Environment Based on Episode Cognition

The hippocampal formation of the brain contains a series of nerve cells related to environmental cognition and navigation.These cells can integrate their moment information and external perceptual information and acquire episodic cognitive memory.Through episodic cognition and memory,organisms can achieve autonomous navigation in complex environments.This paper mainly studies the strategy of robot episode navigation in complex environments.After exploring the environment,the robot obtains subjective environmental cognition and forms a cognition map.The grid cells information contained in the cognitive map can obtain the direction and distance of the target through vector calculation,which can get a shortcut through the inexperienced area.The synaptic connection of place cells in the cognitive map can be used as the topological relationship between episode nodes.When the target-oriented vector navigation encounters obstacles,the obstacles can be realized by setting closer sub-targets.Based on the known obstacle information obtained from boundary cells in the cognitive map,topological paths can be divided into multi-segment vector navigation to avoid encountering obstacles.This paper combines vector and topological navigation to achieve goal-oriented and robust navigation capability in a complex environment.

Multi-Hop Routing Optimization Method Based on Improved Ant Algorithm for Vehicle to Roadside Network

This paper proposes a route optimization method to improve the performance of route selection in Vehicle Ad-hoc Network （VANET）. A novel bionic swarm intelligence algorithm, which is called ant colony algorithm, was introduced into a traditional ad-hoc route algorithm named AODV. Based on the analysis of movement characteristics of vehicles and according to the spatial relationship between the vehicles and the roadside units, the parameters in ant colony system were modified to enhance the performance of the route selection probability rules. When the vehicle moves into the range of several different roadsides, it could build the route by sending some route testing packets as ants, so that the route table can be built by the reply information of test ants, and then the node can establish the optimization path to send the application packets. The simulation results indicate that the proposed algorithm has better performance than the traditional AODV algorithm, especially when the vehicle is in higher speed or the number of nodes increases.