Six Degrees Of Freedom Based On Virtual Prototype Technology 3 - Prps Parallel Robot Kinematics And Dynamics Research And Parameter Optimization

Compared with the series robot, parallel robot has the characteristics of higher rigidity, higher precision and stronger bearing capacity et al, widely used in the machine tools and other industrial fields. Based on the previous research, this paper makes further research on the6-DOE3-PRPS parallel robot in terms of the kinematics, dynamics and optimal design.The6-DOE3-PRPS parallel robot evolves from the Stewart platform. Compared with the Stewart platform, this robot has simple characteristics of kinematics which can reduce the calculation amount of the real-time control at large level and have the characteristics of predicting the motion of the moving platform and producing pure rotation. The structure characteristics of the robot is analyzed in this paper and the forward and inverse kinematics equations are established and solved by the Newton-Rapson iterative method and Matlab software respectively. Considering each constraint condition, the workplace figures and the cross section of the workplace are drawn on the base of the inverse kinematics. The velocity and acceleration equations of the robot are established by using vector method, and the Jacobian matrix of the robot is deduced, laying the foundation for the dynamic analysis. When analyzing the dynamics of the robot, the dynamic equation is established based on the characteristics of the robot structure and the applicable scope of the Lagrangian method. Based on the kinematics and dynamics analysis, the curves of the theoretical calculation and simulation, drawn by using Matlab and ADAMS software are prepared for the optimal design of the robot. To achieve the optimal design of the robot, the method of combing the parametric design, the single objective optimization and the multiple objective optimization is adopted. First, the virtual prototype model of the robot is parameterized and the design variables and constraints are established and the three sub-objective functions are confirmed on the base of the dynamics simulation. Then the three sub-objective functions are researched to determine which design variable exerts tremendous influence on the objective function. At last, the three sub-objective functions are unified into one general objective function by weighting coefficients, and the optimal value of design variables is obtained to balance the three sub-objective functions by the magnitude.