Research On Path Planning Algorithm For Mobile Robots Based On ROS

With the rapid development of robotics industry,mobile robots are more and more widely used in many fields such as national defense,agriculture,industry,and service industry,where path planning technology plays a crucial role in the process of robots performing tasks through navigation.In this paper,the path planning algorithm for mobile robots is systematically studied and the research related to autonomous robot navigation is carried out based on the ROS platform,and the main work is as follows.First,the mobile robot system modeling and ROS platform are studied,and the characteristics of ROS,system architecture and topic communication mechanism are analyzed.And the mathematical modeling of the mobile robot and its important modules,including coordinate system model,kinematic model,odometry model,and radar ranging model,is carried out to provide support for the subsequent research.Then several map construction models are compared and analyzed,and raster maps are selected for subsequent related experiments according to the experimental requirements.Next,the global path planning algorithms for mobile robots are studied,including the study of several common global path planning algorithms,such as RRT,Dijkstra,and A~*.And the A~* algorithm is studied in depth,and the heuristic function of A~* algorithm is improved to improve its search efficiency for its large search range and many redundant nodes;meanwhile,the path node optimization algorithm is added to reduce the number of redundant nodes and improve the path smoothness.The comparison through simulation experiments shows that the improved A~* algorithm reduces the search time by 30.5%,the path length by 4.97% and the inflection points by a significant amount compared with the traditional A~* algorithm.Then,the mobile robot local path planning algorithm and the fusion algorithm are studied,including the study of D*,the artificial potential field method,and the dynamic window method,which focuses on the dynamic window method and improves its evaluation function.Through simulation experiment comparison,it shows that the improved dynamic window method shortens the path planning distance by 3.3% and the path planning time by 15.1% compared with the traditional dynamic window method The improved dynamic window method improves the search efficiency and determines a better set of weight parameters through experiments.Then,we propose a fusion algorithm strategy to fuse the improved A~* algorithm and the improved dynamic window method,and verify the feasibility and effectiveness of the fusion algorithm through experiments,and compare the experimental data of this fusion algorithm with several existing fusion algorithms,compared with this fusion algorithm,the average reduction of planning time and planning distance of this fusion algorithm is 5.1% and 3.7%,which verifies the superiority of this fusion algorithm.Finally,the mobile robot experimental platform construction and autonomous navigation experiments are studied.Firstly,the Xacro file of the mobile robot model is written in XML language,the robot model is imported into Gazebo and displayed visually using Rviz,and the experimental simulation environment is built using Gazebo.The Gmapping algorithm and AMCL algorithm are used to complete the map building and localization functions respectively,while the fusion algorithm in this paper is used for path planning.Three sets of experiments are conducted by continuously increasing the complexity of the experimental environment,and the experimental results show that the robot can successfully complete the autonomous navigation task,which verifies the effectiveness of the fusion algorithm in this paper.