The Research On Manipulator Trajectory Planning

The main issue in manipulator trajectory planning (MTP) is to deal with the problems of the displacement, angular velocity and angular acceleration of the trajectory, to make the trajectory end finish some work smoothly. MTP is the practical application of inverse kinematics, having been a very important robotics research field. The main contents of this paper are as follows:An overview of the background, the research situation both at home and abroad, and significance of the subject, is presented firstly. The characteristics and shortcomings of current manipulator planning methods are also analyzed. Then the kinematics analysis including mainly those of the kinematics and inverse kinematics, and the solving of Jacobian matrix of the manipulator, are conducted. A three-dimensional model of puma560, with the workspace based on Monte-Carlo method, is produced by ADAMS, which provides a foundation for the trajectory planning following.To meet the requirement of moving smoothly of the manipulator, the cubic triangular Bezier spline (CTBS) is utilized in MTP. Continuous displacement, and continuous angular velocity as well as acceleration, can be achieved due to nice features of the second and third derivative of CTBS. The sudden change of acceleration, at beginning and the end of the manipulator movement, is overcome with drawn into higher order curves at the end points. Therefore, smooth and continuous movements of the manipulator are achieved, which is tested and verified by the simulation experiments following.The improved adaptive genetic algorithm (IAGA) is used to solve the problem of time optimal MTP, due to the fact that CTBS can not ensure the shortest moving time though it meets the requirement of stability of the manipulator. After the fitness assignment based on the constraints of joints, other operations such as selection, crossover and mutation on the current population, are carried on in IAGA. A time optimal result is obtained at last, being justified by the simulation experiments.