Time-optimal trajectory planning based on improved genetic algorithm

A time-optimal trajectory planning method for industrial robots is proposed. The trajectory of robot joint space is regarded as the cubic spline curve fitting the key points. The mathematical model of optimal time trajectory planning was established with the objective of optimal time. At the same time, the constraints of joint trajectory velocity, acceleration and acceleration were considered. Combining the objective function value and constraint conditions, a sorting method for evolutionary algorithm was proposed, and the cubic spline trajectory of joint space was optimized with the improved genetic algorithm as an example. The Sheffield genetic algorithm toolbox was used to calculate the optimization results of genetic algorithm, and the D-H method was used to establish the robot kinematics model. Combined with the Robotics Toolbox, the 3D simulation model of the robot was built, and the simulation environment for cubic spline trajectory optimization of the robot was established. The simulation results of Stanford robot show that compared with the traditional pattern search method, the total trajectory time of the improved genetic algorithm is significantly reduced.