Study On Control Algorithm And Trajectory Planning Of NAO Robot Arm

In recent years,with the continuous development of automation technology,computer technology and intelligent control theory,intelligent robot has been widely studied at home and abroad.The humanoid robot with the structure personification is more and more concerned,and becomes a new hot topic in the field of robot research,whose focus is on the flexible and easy to realize the moving tasks in the complex environment,such as handling,pickup,human-machine collaboration and so on.Therefore,it is of great practical significance to explore the motion control and trajectory planning of humanoid manipulator.In this dissertation,the humanoid robot NAO is taken as the research object.Aiming at the trajectory planning and tracking problem of NAO robot arm,an enhanced Q-Learning algorithm based on fuzzy PDC control structure is proposed.The specific research contents are as follows:Firstly,the NAO robotic arm mechanism is analyzed,the right arm's motion parameters are solved,and the dynamic model of the manipulator is constructed by using the Lagrangian function balance method.This provides a mathematical model for designing a closed-loop control system and realizing trajectory tracking.Secondly,aiming at the problem of large terminal error in trajectory tracking of NAO robotic arm,a parallel distributed compensation control algorithm based on T-S fuzzy model is proposed.The T-S fuzzy model of the arm is constructed by the Lagrange dynamics equation.In order to reduce the tracking error and improve the robustness,a closed-loop controller with state feedback is designed by using the parallel distributed compensation strategy.Compared with the fuzzy proportional differential control algorithm,the trajectory tracking of the manipulator under the condition of interference and interference is analyzed.The simulation results show the validity of the proposed algorithm.Finally,aiming at the obstacle avoidance problem of NAO robotic arm,the algorithm of obstacle avoidance for enhanced Q learning trajectory planning based on fuzzy PDC structure is proposed.The environment model of trajectory planning is established,and the optimal obstacle avoidance trajectory is determined by iterative exploration,which selects the action according to the current state selection,obtains the return value of the next time and updates the state.The simulation and experiments show that the proposed algorithm can enable the manipulator to complete the obstacle avoidance and target grabbing task,and verify the effectiveness of the proposed scheme.The research work in this paper can provide theoretical basis and method for the development of humanoid manipulator,which has important theoretical significance.