Research On Time-energy Optimal Trajectory Planning Algorithm For Industrial Robots

Trajectory planning is a basic problem in the design and control of industrial robots.To optimize the robot’s performance and improve its working efficiency,this paper takes the time and time-energy optimization as the optimization objectives respectively,and adopts the improved particle swarm optimization algorithm to optimize the trajectory under the given path and constraint conditions.(1)The current research status of single-objective and multi-objective trajectory optimization methods at home and abroad is reviewed,and the background and research significance of the subject are introduced.(2)The basic knowledge of the robot and the PUMA560 robot were introduced,and the forward and inverse kinematics of the robot were deduced.The mathematical derivation of the robot kinematics was verified by simulation through the MATLAB robot toolbox,which proved the correctness of the derivation and prepared for the next step of trajectory optimization.(3)Aiming at time optimization,an improved simplified particle swarm optimization algorithm is proposed.The particle swarm optimization algorithm is optimized by improving the weight and learning method of the algorithm.The performance of the algorithm is tested by eight sets of test functions,and the results show that the convergence speed and accuracy of the improved algorithm have been effectively improved.Then,a robot trajectory optimization model with given path and system constraints is established,and an improved simplified particle swarm optimization algorithm is introduced to verify the simulation.The results of Matlab simulation show that compared with other existing algorithms,the improved simplified particle swarm optimization algorithm can effectively reduce the running time of the robot and improve the running efficiency of the robot under the condition of meeting the kinematic constraints.(4)An improved multi-objective particle swarm optimization algorithm is proposed for time-energy synthesis optimization.In order to further balance the full development and local exploration ability of multi-objective particle swarm optimization,a random weight updating method based on normal distribution is proposed,and the learning method is improved.By comparing the results of eight test functions,it is shown that the convergence and comprehensive performance of the improved algorithm have been significantly improved.The algorithm is applied to the robot trajectory optimization model.The experimental results show that the improved algorithm can effectively reduce the time and energy consumption of passing through a given point.