Research On Trajectory Planning And Trajectory Tracking Of 6R Manipulator

With the rapid development of robot technology,the demand for high-performance arm robots in China is increasing day by day.Among them,6R manipulator has become a hot spot in the application and research of manipulators because of its large working space,flexible movement,and non-redundancy.Therefore,based on the establishment of the kinematics and dynamics model of the manipulator,this thesis studies the high-quality trajectory planning and trajectory tracking of the 6R manipulator,to improve the accuracy,stability,and safety of the manipulator system and other control qualities.In this thesis,REBot-V-6R manipulator is taken as the research object,and its kinematics inverse solution,motion trajectory planning,and trajectory tracking control are studied.The main research contents are as follows:Firstly,an intelligent algorithm for solving inverse kinematics of the6 R manipulator is proposed.The forward kinematics model of REBot-V-6R manipulator was established by combining the actual physical parameters with the screw method.The improved PSO algorithm was used to solve the inverse kinematics optimization problem of the manipulator,and the position error and attitude error of the manipulator were simulated.The results verified the stability and effectiveness of the algorithm in the accuracy of the inverse solution,which laid a good foundation for the subsequent motion control.Secondly,the kinematic trajectory planning of the 6R manipulator is studied.The joint space trajectory planning experiments based on cubic polynomial,quintic polynomial,and "3-5-3" mixed polynomial interpolation methods were designed,respectively.The results show that the "3-5-3" composite polynomial interpolation function can better reflect the actual operation of the manipulator.Furthermore,an improved "3-5-3" hybrid polynomial interpolation algorithm for PSO time optimization trajectory planning was proposed.The simulation experiment proved that the proposed trajectory optimization algorithm could improve the system’s real-time performance and effectiveness and ensure the safe and stable operation of the manipulator.Finally,the precise trajectory tracking control of the manipulator is studied.Considering that there are uncertainties such as modeling error and external interference in the manipulator system,which will affect the motion control quality of the manipulator,a sliding mode control strategy based on the RBF neural network is proposed in this thesis to approximate the uncertain factors in the system by using RBF neural network algorithm.A sliding mode controller based on the new reaching law was designed to reduce the chattering phenomenon and the system jitter’s influence on manipulator control.Through simulation and comparison experiments,it is proved that the proposed control strategy has high precision and stability,noticeable effect,and effectiveness.