"Dexterity" is an important index to measure the technical level of the robot dexterous hand, which demonstrates the ability to work in any position with the target shape. This study is based on the Shadow dexterous hand, and the mechanical structure characteristics of Shadow dexterous hand are studied. Then the kinematics model is established by using D-H parameter method, and the forward and inverse kinematics equations are derived. Finally the results are verified and simulated in MATLAB.

This study is based on the "Shadow" dexterous hand, whose mechanical structure are studied, and the displacement, velocity, acceleration and static force of dexterous hand joints are researched. Then the kinematics model is established by using D-H parameter method, and the forward and inverse kinematics equations and Jacobian matrix are derived. Finally the parameters of Differential kinematic and static force are analyzed and solved.

This paper presents a kind of power system short-term load prediction algorithm based on fuzzy control and RBF neural network, to solve the problems of th traditional RBF neural network in electric power system short-term load forecast errors. Through the example verification, this method can improve the prediction accuracy compared with the traditional RBF load forecasting method, which has a good application prospect.

The mobile interfaced robot arms are majorly being used nowadays in order to provide the remote-control applicability for various industrial and manufacturing applications. This article proposes a robotic arm platform for controlling the industrial application. The proposed system includes various modules like a robot arm, a controller module and a remote mobile operating application for visualizing the robot arm angles having real time applicability. Augmented reality (AR) is utilized for robot control WIFI communication and the robot angle information is obtained for varying real time environment. This novel approach incorporated the AR technology into mobile application which allow the real time virtual coordination with physical platform. The feasible trajectories are generated using the proposed methodology and a comparison is made between the desired and real trajectory paths. The simulation results are obtained for various assessment indicators and effectual outcomes are achieved with 98.03% accuracy value and 0.185, 0.180 of error and loss values for training phase. The accuracy value of 97.65% is achieved for testing phase with corresponding 0.209 and 0.190 minimum error and loss values. The proposed platform provides the feasible and reliable outcomes in the real time environment for real time manufacturing industry applications.