Ju Gao, Xiangrong Xu, Xingning Zhang, Shanshan Xu, Quancheng Pu
{"title":"基于改进A*算法的移动机器人路径规划","authors":"Ju Gao, Xiangrong Xu, Xingning Zhang, Shanshan Xu, Quancheng Pu","doi":"10.1109/WCMEIM56910.2022.10021353","DOIUrl":null,"url":null,"abstract":"Path planning technology is the core part of the independent navigation of mobile robots. The problem of unscrupulous road planning and many turning point nodes need to be resolved. In response to the problem of traditional A* algorithm expansion nodes, long search time, and excessive path bending, an improvement A* algorithm that uses an inspiration function optimization and cubic Bezier curve optimization. Based on the ROS open-source system, the Gazebo physical simulation environment is built, and the practical Gmapping builds a grid map. The improved algorithm before and after the improvement is used as a global path planner plugin and applied to ROS for simulation experiments. The simulation results show that in the same simulation experimental environment, the length of the improved A* algorithm path is reduced by 17.161%, and the number of redundant turning nodes is reduced by 71.429%; More reasonable, further meet the constraints of mobile robots.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Path Planning of Mobile Robot Based on Improved A* Algorithm\",\"authors\":\"Ju Gao, Xiangrong Xu, Xingning Zhang, Shanshan Xu, Quancheng Pu\",\"doi\":\"10.1109/WCMEIM56910.2022.10021353\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Path planning technology is the core part of the independent navigation of mobile robots. The problem of unscrupulous road planning and many turning point nodes need to be resolved. In response to the problem of traditional A* algorithm expansion nodes, long search time, and excessive path bending, an improvement A* algorithm that uses an inspiration function optimization and cubic Bezier curve optimization. Based on the ROS open-source system, the Gazebo physical simulation environment is built, and the practical Gmapping builds a grid map. The improved algorithm before and after the improvement is used as a global path planner plugin and applied to ROS for simulation experiments. The simulation results show that in the same simulation experimental environment, the length of the improved A* algorithm path is reduced by 17.161%, and the number of redundant turning nodes is reduced by 71.429%; More reasonable, further meet the constraints of mobile robots.\",\"PeriodicalId\":202270,\"journal\":{\"name\":\"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WCMEIM56910.2022.10021353\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCMEIM56910.2022.10021353","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Path Planning of Mobile Robot Based on Improved A* Algorithm
Path planning technology is the core part of the independent navigation of mobile robots. The problem of unscrupulous road planning and many turning point nodes need to be resolved. In response to the problem of traditional A* algorithm expansion nodes, long search time, and excessive path bending, an improvement A* algorithm that uses an inspiration function optimization and cubic Bezier curve optimization. Based on the ROS open-source system, the Gazebo physical simulation environment is built, and the practical Gmapping builds a grid map. The improved algorithm before and after the improvement is used as a global path planner plugin and applied to ROS for simulation experiments. The simulation results show that in the same simulation experimental environment, the length of the improved A* algorithm path is reduced by 17.161%, and the number of redundant turning nodes is reduced by 71.429%; More reasonable, further meet the constraints of mobile robots.