基于IMOPSO的IRB6700机器人轨迹优化OA

To address the shortcomings of robotic systems,including low operational efficiency,high energy consumption,and excessive impact,as well as the limitations of the traditional multi-objective particle swarm optimization(MOPSO)algorithm such as slow convergence and susceptibility to local optima,a multi-objective particle swarm algorithm incorporating linear differential decreasing inertia weights and Gaussian variational operator was proposed to optimize the time-energy-impact multiobjective trajectory.First,taking the IRB6700 industrial robot as the research object,an improved D-H parameter method was used to establish the robot linkage coordinate system.And the forward and inverse kinematic equations of the robot were derived.The simulation model was then established with the help of MATLAB Robotics Toolbox to verify the correctness of the forward and inverse kinematics.Subsequently,the displacement,velocity and acceleration at different path nodes were trajectory planned by the 7-times B-spline interpolation.Finally,the improved multi-objective particle swarm optimization(IMOPSO)algorithm was used to optimize the planned trajectories by the 7-times B-spline interpolation method under the kinematic constraints.The results show that the running time of the robot was reduced from 25 seconds to 17 seconds while the acceleration and impact about time graphs became smoother.In addition,the robot starting point velocity and acceleration were both zero,thereby reducing energy consumption and joint impact.These findings prove that the IMOPSO algorithm in the paper can effectively realize the time-energy-impact multi-objective optimal trajectory planning.