Research On Manipulator Motion Planning And Control Algorithm Based On P-RRT* Algorithm

With the introduction and development of the concept of intelligent manufacturing,robotic arms have been widely used in various industrial production such as palletizing,handling,welding,cutting,and assembly.In practical applications,when performing different tasks,a robotic arm needs to avoid different obstacles,and it is necessary to control the trajectory of the center point of the end effector.The level of motion planning and control directly affects the quality and efficiency of the operation.Aiming at the work requirements in actual production,this paper takes SNR-B6 robot arm as the research object,explores new motion planning algorithms,and then conducts research from the aspects of robot arm trajectory optimization and robot arm trajectory tracking,so as to ensure its stable operation while improving the operation accuracy and ensuring its application in various industrial tasks.The main research contents are as follows:Firstly,this paper analyzes the theoretical basis,kinematics,and modeling methods of the robot,and uses the improved DH parameter rule to establish its kinematics model.This study uses parameters to solve the kinematics equation of the robot arm,and uses the method of posture separation to quickly solve the inverse kinematics solution of the robot arm;The workspace of the robot arm is traversed using the Monte Carlo method,and the robot tool box in MATLAB is used to model and simulate the robot arm,thereby verifying it.Robot motion planning based on improved P-RRT * algorithm.Based on the RRT *algorithm,combined with a target bias strategy and obstacle avoidance measures based on target detection,it guides the random tree to grow towards the target point with a certain probability,and quickly grows towards the target position when the obstacle crossing is completed.Three RRT * algorithms are tested in a two-dimensional environment,and the results show that the improved algorithm can effectively avoid obstacles with high efficiency and short paths.In MATLAB,a workspace for experimental simulation of a robotic arm is built.The results show that the robotic arm can smoothly and stably avoid obstacles to reach the target point,proving the effectiveness of the improved algorithm applied to the robotic arm.The trajectory tracking control of robot arm based on feedforward compensation mainly focuses on the joint control of robot arm.Firstly,the force position control method is selected as the control method of the robot arm.The Newton Euler method is used to calculate the dynamics of the robot arm.The Lagrange method is used to derive the dynamics model of the6-degree of freedom robot arm,and the standard dynamics model of the robot arm in joint space is derived.The control torque is derived to achieve decoupling and linearization of the system.Then,based on the analysis of traditional PID control methods,in order to enhance the parameter tuning of the PID controller,based on the sparrow algorithm,a PID controller parameter self-tuning method is proposed to achieve parameter optimization of the PID controller.Secondly,the theory of feedforward control is introduced.The idea of the control algorithm is to calculate the torque under the desired trajectory in the joint space through the model of inverse kinematics of the robot,and use it as a compensation to compensate for the nonlinear error in the trajectory tracking control process.A joint simulation framework was built using Solidworks and Simulinks to track joints and trajectories of robotic arms without and with feedforward control,respectively.The corresponding simulation comparisons and verifications were conducted on the SNR-B6 simulation test platform.The simulation results show that the local planning compensation algorithm and the manipulator control algorithm proposed in this paper are effective,which can improve the trajectory tracking control accuracy and more stably reduce the trajectory tracking error.