Research On Fast Path Planning And High-precision Grasping Of Six-degree-of-freedom Manipulator

With the development of industrialization process,our country gradually enters the era of "industry 4.0",industrial robot arm,as a kind of anthropomoric robot with a higher degree of intelligence and automation,its position in the field of intelligent production becomes more and more important.It can replace manual operations to realize a variety of repetitive and tedious operations and improve the efficiency of production.It can replace manual operations to realize a variety of repetitive and tedious operations and improve the efficiency of production.However,most of the traditional robotic arms need manual path planning,and it is difficult to carry out independent operations in industrial production.Based on this,this paper designs a set of autonomous obstacle avoidance and grasping scheme of six-degree-of-freedom manipulator based on machine vision,and studies the hand-eye calibration method and path planning algorithm of the manipulator.Firstly,the kinematics model of the robotic arm is established using the D-H parameter method.In the inverse kinematics analysis,the analytical method and Euler angle solution method were used to find the six joint angles of the end pose of the manipulator,and the inverse solution of the manipulator was obtained.In MATLAB,the Monte Carlo method is used to analyze the working range of the manipulator,verify the accuracy of the mathematical model of the manipulator,and establish a mathematical foundation for the subsequent research of the manipulator path planning.Secondly,an improved RRT-connect algorithm is proposed to view the problems of the traditional RRT algorithm and RRT-connect algorithm being too random during sampling,the low search efficiency of sampling and the long planning time.Aiming at the problem that the planning efficiency of traditional algorithms is too low,the dependence on target points in the process of random tree sampling is realized by combining random sampling and greedy strategy,which improves the purpose of generating sampling points.Aiming at the problem that the planning efficiency of traditional algorithms is too low,this paper not only ensures the quality of path planning,but also proposes a sampling method combining the third node,setting the midpoint of the starting point and the target point as the third node for sampling before the sampling starts,and if the midpoint is in the obstacle,the point that is not in the obstacle is searched on the middle vertical line for the third node for sampling.Thirdly,the calibration accuracy of the robotic arm and the calibration accuracy of the camera can affect the calibration accuracy of the traditional nine-point calibration method,in view of this problem,an improved nine-point calibration method is proposed,aiming to improve the calibration accuracy of the robotic arm to achieve the purpose of improving the hand-eye calibration accuracy.Firstly,the four-point calibration method is used to calibrate the coordinate system of the manipulator and the tool coordinate system,and then the forward and reverse kinematics algorithm of the manipulator is used to calculate the length of the sixth axis of the manipulator,so that the end position obtained by the manipulator during hand-eye calibration is more accurate,so as to improve the accuracy of hand-eye calibration.Finally,in order to verify the effectiveness of the above method in robotic arm path planning and target grasping,this paper builds a robotic arm simulation experimental platform based on ROS(Robot Operating System),and inserts the RRT-connect algorithm with the improved RRT-connect algorithm into Move It! for comparative experiments,which preliminarily verifies the feasibility of the improved algorithm.The experimental results show that the improved RRT-connect algorithm and the improved nine-point calibration method proposed in this paper can make the robotic arm avoid obstacles faster to reach the designated position and grasp the target object more accurately.