Research On Motion Planning Of Stacking Robot Based On ROS

With the rapid development of e-commerce and freight logistics industry,warehousing,as an important part of the logistics industry,has higher and higher requirements for the stacking and sorting of goods.In the traditional warehousing industry,manual stacking and sorting are required.The items in the picking container have become the bottleneck of warehousing logistics,so it is necessary to realize warehousing automation to promote the industrial upgrading of intelligent warehousing.This topic focuses on the problem of warehouse container stacking,and studies the motion planning of ROS-based stacking robots,including the kinematic analysis of the robot during the stacking process,hand-eye calibration methods and path planning,etc.The details are as follows:(1)Perform kinematics analysis on the storage robot arm selected for this topic.First,establish the D-H coordinate system and D-H parameter table of the UR5 manipulator arm,and the homogeneous transformation matrix between adjacent rods.Establish forward kinematics equations to solve the position and attitude information of the end of the robot arm relative to the base coordinate system;then,establish the inverse kinematics equations on the basis of forward kinematics,and use algebraic methods to solve the angle change information of each joint of the robot;Finally,establish the UR5 kinematics model.(2)Aiming at the problem of robot grasping and positioning,an Eye-in-Hand calibration system was established.First,study the imaging principle of the calibration camera;secondly,obtain the internal and external parameters of the camera and the posture information of the captured target relative to the calibration camera through camera calibration;then,establish the eye-in-hand hand-eye calibration The system derives the relative positional relationship between the calibration camera and the robot base;finally,the Kinect2 camera is used for calibration experiments to verify the camera calibration and hand-eye calibration methods as the basis of the robotic arm motion planning.(3)Aiming at the problems of high randomness,low search efficiency,and path redundancy in the path planning of the robotic arm,the rapid expansion random tree algorithm(RRT)cannot obtain a relatively optimal smooth path in the container stacking scene.An improvement is proposed.RRT-artificial potential field method hybrid algorithm for path planning of container stacking movement.Firstly,the target search is introduced,the traditional RRT algorithm is improved,and the improved RRT algorithm is used for global path planning;secondly,the range of the repulsive force potential field is used as the threshold and the end effector of the robot arm is used to influence the correction gravitational function and The repulsion function,the improved artificial potential field method,and the improved artificial potential field method are used to optimize the local path;then,the path is smoothed using the cubic non-uniform B-spline curve and the final path is obtained.Finally,the algorithms are compared through Python Analysis and simulation verification under digital simulation scenarios.(4)Aiming at the planning problem of the UR5 collaborative robot arm in the warehouse stacking movement,a simulation platform for container stacking under the ROS environment was built.The hybrid algorithm combining the improved RRT-artificial potential field method is publicly inherited into the OMPL class through the Python interface,and the container stacking motion simulation is performed in Gazebo,which verifies that the robot can identify and locate moving objects and grab them The object moves to the designated position of the container,and a smooth,impact-free,collision-free path is planned for the robot.