Research On Trajectory Planning And Optimization Of Six-degree-of-freedom Industrial Robots

Trajectory planning and trajectory optimization have always been the focus of attention,and trajectory planning is the premise of trajectory optimization.Simply enabling industrial robots to complete the designated tasks can no longer meet the current needs.It is necessary to shorten the running time as possible to improve efficiency and reduce jerk to extend the service life.In this thesis the six-degree-of-freedom industrial robot is used as the research object.In the joint space,quintic non-uniform b-spline interpolation is used for interpolation.For the planned trajectory,an improved adaptive genetic simulated annealing algorithm is used as an optimization method,taking time-jerk as the optimization target,and time,jerk and the integrated optimal trajectory can be achieved by changing the weights.The main contents of this thesis are as follows:(1)The PUMA560 was used as the research object for D-H modelling,the kinematic equations were established according to the D-H parameter table,the positional matrix converted from joint angle to Cartesian space was derived,the joint angle corresponding to the positional matrix was solved in reverse,and the correctness of the derivation was verified by numerical simulation.The robot model is established through the robotic toolbox in MATLAB,and the pose of the end effector under the key points are given.(2)On the basis of kinematics,the polynomial and b-spline trajectory planning methods are deeply studied,and the detailed derivation process is given.The above-mentioned trajectory planning methods are realized by using MATLAB,and the simulation results are compared and analyzed.(3)In order to obtain the optimal trajectory under time-jerk index,it is necessary to optimize the planned trajectory.Trajectory optimization is a highly coupled nonlinear problem,which is generally solved by genetic algorithm.In order to improve the search ability of genetic algorithm,crossover operation and mutation operation are self-adaptively improved.The improved adaptive genetic simulated annealing algorithm is proposed by introducing a simulated annealing algorithm with stronger local search capability using genetic algorithm as a framework Use the test function and other algorithms to test the search ability,and prove the superiority of the improved algorithm in this thesis.(4)The improved algorithm in(3)is used to optimize the trajectory,taking time-jerk as the optimization goal and kinematics as the constraint conditions.The motion curves of each joint are obtained by changing the weights,and only the time is optimized under the same path points and constraints.Compared with other literatures,the optimization time of the improved algorithm in this thesis is shortened by 14%,13% and 1.3%,respectively.The curves of displacement,velocity,acceleration and jerk of each joint are smooth,so that the robot can complete tasks efficiently and smoothly.