Research On Time Optimal Trajectory Planning Of Manipulator Based On Forward And Inverse Kinematics Analysis

With the acceleration of industrial automation,a large number of robotic arms are used in human production and life.The manipulator mainly controls the end effector to complete various tasks through the continuous movement of the joints,so it is very necessary to carry out kinematics analysis and trajectory planning.Kinematic analysis is the basis of trajectory planning.Whether it can be solved accurately and quickly plays a vital role in real-time control of the manipulator.Trajectory planning is a prerequisite for the smooth operation of the manipulator.The angular acceleration planning can effectively reduce the vibration and sudden changes in the speed generated during the motion of the manipulator,and shorten the working time.In order to improve the working efficiency of the manipulator,this paper takes the SNR3-C30 six-degree-of-freedom manipulator as the research object,and conducts research on its spatial pose transformation,forward and inverse kinematics,trajectory planning and optimization.The main contents are as follows:According to the position and attitude representation of the robot arm in space coordinates and the principle of homogeneous transformation matrix,the robot arm linkage coordinate system and mathematical model are established by the DH method,and on this basis,the forward and inverse kinematics analysis is carried out,and then the traditional inverse kinematics There are a large number of matrix operations,complex mathematical models,and long program running time in the solution process.A geometric inversion optimization algorithm combining the idea of pose separation is proposed.The algorithm first solves the first three joint angles?₁,?₂,?₃ through the geometric position relationship of the joints of the robot arm relative to the wrist,and then uses the rotation sub-matrix to solve the remaining joint angles?₄,?₅,?₆ and finally uses MATLAB software to simulate and verify.The results show that the improved algorithm has superiority and effectiveness.The trajectory planning of this type of manipulator is carried out in Cartesian space and joint space respectively,and the advantages and disadvantages of different interpolation functions such as cubic,quintic and"3-5-3"mixed polynomials are analyzed for trajectory planning.Aiming at the shortcomings of the standard particle swarm optimization algorithm in the time-optimal trajectory planning of the manipulator,it is easy to fall into the local optimum and premature.An improved algorithm with fast convergence is proposed.The algorithm first uses a dynamic learning factor strategy to replace the traditional fixed learning factor,and then uses the"3-5-3"hybrid polynomial interpolation function for planning,and finally completes the fitting of the motion trajectory of each joint in the MATLAB simulation software.The results show that the improved algorithm has reached the expected time optimization goal and has certain application value.