Research On Path Planning Of Mobile Robots For Warehousing Environment

With the rapid development of the manufacturing and logistics industries,the shortage of human and material resources cannot effectively balance the rapidly increasing market demand.In order to improve efficiency,reduce manpower and material resources,and speed up the automation process of intelligent warehousing,how to control mobile robots to move materials to target locations autonomously and efficiently is a key problem that researchers need to solve.This topic is oriented to the field of intelligent storage.There are the following problems in the current mobile robot path planning process: the global positioning accuracy is low,and when facing the problem of robot kidnapping,that is,when the robot is transported to other unknown positions on the map,it cannot be located effectively;the path generated by global path planning algorithm has sharp points and is not suitable for dynamic environment;local path planning algorithm has the problems of poor trafficability and cannot generate the optimal path.Research on the positioning,global path planning and local path planning design of the mobile robot,and then control the mobile robot to achieve precise positioning,and can quickly plan a shortest path from the starting point to the target position,providing theoretical support for the material handling of the mobile robot.The main work of this paper is as follows:(1)Based on the four-wheel-drive Mecanum wheel mobile robot selected in this project,the control system modeling and map positioning are carried out.First,the kinematic model and odometry motion model of the mobile robot are built,and the pose information of the robot is continuously tracked to provide pose data for the subsequent positioning and path planning of the mobile robot.Secondly,in order to improve the accuracy of the global positioning of the mobile robot on the map and solve the problem of robot abduction,the adaptive Monte Carlo algorithm(AMCL)is selected,and the odometer motion model and lidar are combined for precise positioning.(2)Aiming at the problems existing in the global path planning algorithm,an improved IPSO-IDE algorithm is proposed.First,add adaptive parameters and introduce the concept of corporate governance to optimize the structure of the traditional particle swarm optimization(PSO)and improve the search accuracy of the algorithm.Secondly,the improved differential evolution algorithm is used to optimize the global optimal position of particle swarm optimization.Finally,the IPSO-IDE algorithm is compared and analyzed with other path planning algorithms,and the experiment verifies that the algorithm has superiority,higher iteration speed and optimization ability.(3)Because global path planning cannot avoid dynamic obstacles,local path planning algorithm is introduced.An improved dynamic window algorithm(IMDWA)is proposed to solve the problem of local path planning for mobile robots.First,for the dynamic window method,the passage rule of the mobile robot is proposed,and the adaptive weight is added to the speed constraint,so that the robot can adjust the speed according to the obstacle information.Secondly,in order to make the local path planning fit the global path planning as much as possible,the offset evaluation function is added.Finally,the IMDWA Algorithm is combined with IPSO-IDE Algorithm,and the simulation results are compared to verify the effectiveness and feasibility of the improved algorithm.(4)Build a path planning test platform.In this paper,in the Ubuntu16.04 environment,the ROS robot operating system is used to build the software platform and hardware platform of the path planning system,and the navigation system built in this paper and the proposed path planning algorithm are tested and verified in the real test scene.