Trajectory Optimization And Control Of Grinding Robot Based On Improved Whale Optimization Algorithm

At present,grinding processing is mainly artificial.A large amount of dust is produced in the process of grinding,which has a negative impact on the health of workers and even endangers the personal safety of workers.Moreover,there are poor surface consistency and unstable of processing quality.Robot grinding is an effective method to rescue workers from the harsh grinding environment.Robot grinding can improve the working environment of workers and ensure the consistency and stability of grinding.The smoothness and accuracy of robot trajectory have direct influence on grinding quality in the fine grinding stage.Through the research on the key technologies of robot trajectory optimization and control,such as robot error compensation,trajectory planning and force/position control,the trajectory accuracy of grinding robot can improved,so that the robot can reach the desired position accurately and smoothly,so as to ensure the quality of grinding.Therefore,this dissertation conducts the exploration and analysis of robot trajectory optimization and contact force /position control from three aspects to improve the accuracy and ensure the smoothness of the trajectory for serial grinding robot with six degree of freedom: modeling of position and pose error,optimal trajectory planning and force/position control method.Improved whales optimization algorithm is proposed,it has high convergence speed and global optimization ability.Improved whales optimization algorithm is used in the inverse kinematics solution,robots optimal trajectory planning and proportional integral differential(PID)control parameter optimization,simplify the robot inverse kinematics solving process,improve the control accuracy and response speed,obtain more stable and smooth trajectory.From the perspective of robot position and pose error to improve the trajectory accuracy.Exploring the cause of trajectory error in robot grinding process,and an error model is established.By analyzing the basic principle of robot trajectory planning and aiming at obtaining the minimum joint vibration,the optimal trajectory planning of robot is carried out.From the perspective of planning algorithm to improve the smoothness of the trajectory.The grinding contact force control method of the robot is studied to realize the force/position control of the robot and ensure the precision and smoothness of the robot trajectory.The main work includes the following aspects:(1)The whale optimization algorithm has been used to solve many parameter optimization problems in engineering.It has simple structure and strong search ability,but its convergence speed is slow and it is easy to fall into local optimal.Differential evolution algorithm is a widely used optimization algorithm,which has the characteristics of fast convergence and strong robustness.Based on the whale optimization algorithm,an improved whale optimization algorithm is proposed in this dissertation.Each search agent is regarded as an individual in the population.Inspired by the variation and selection operation of the differential evolution algorithm,the updating method of the whale location is improved.The improved whale optimization algorithm is verified by 23 basic functions and compared with other algorithms.The results show that improved whale optimization algorithm is faster convergence and better global optimization.(2)A method for solving the inverse kinematics of the robot based on the improved whale optimization algorithm is proposed.This method is used in the simulation analysis of error compensation,trajectory planning and force/position control.By analyzing the deformation of the contact wheel of sand belt grinding and the displacement and deformation of the endeffector,combine with the structural error analysis of the robot,the position and pose error model is established.The basic principles of several interpolation methods for trajectory planning in Cartesian space and joint space are analyzed.The time and jerk minimization are taken as the objective functions of robot trajectory planning,and the improved whale optimization algorithm is used to obtain the optimal trajectory.Results show that jerk is significantly reduced by using improved whales optimization algorithm to achieve optimization trajectory planning of Serial Grinding Robot with six degree of freedom.(3)Robot grinding processing is a contact operation,and its motion trajectory is inevitably affected by the grinding contact force.Take in to account the grinding force,the deformation force of the robot and the pendulum force caused by the uncertainty of the contact surface,the grinding contact force model of robot is established.The PID control method based on the improved whale optimization algorithm and IMC-PID control method based on the internal model control principle are proposed,and the grinding contact force control simulation analysis is carried out.By using a six-dimensional force sensor,the force control and position control are decoupled,and the hybrid force/position control in the orthogonal direction of robot is realized.The simulation analysis of the robot force/position control method based on impedance control is carried out.(4)The tool type grinding experiment and workpiece type grinding experiment are designed respectively,and the theoretical research on trajectory optimization and control of grinding robot based on the improved whale optimization algorithm is verified experimentally.The robot grinding experiment is carried out with the robot grinding and polishing workstation of North University of China.Considering the influence of abrasive belt granularity,robot speed and belt wheel speed on the contact force,surface roughness and material removal rate.Orthogonal experiment is designed to achieve the experiment of robotic grinding cast iron.Range analysis and variance analysis are carried out based on the experimental results.An evaluation index of grinding effect is proposed,and its prediction model is established through multiple linear regression analysis.