Yong Liang, Xing Luo, Zhisong Xie, Qi Cui, Lifu Gan
{"title":"The realization and optimization of path planning algorithm for autonomous mobile robot","authors":"Yong Liang, Xing Luo, Zhisong Xie, Qi Cui, Lifu Gan","doi":"10.1145/3480571.3480589","DOIUrl":null,"url":null,"abstract":"∗Autonomous mobile robot navigation is a research hotspot in recent years, and path planning technology is the core part of navigation technology. Path planning is often realized by the combination of global and local path planning. Taking gazebo as the experimental platform and using TurtleBot as the experimental object, the navigation performance and resource occupancy rate of Dijkstra + DWA and Dijkstra + TEB are tested, and the performance difference between the two combinations is analyzed. The path planning of the latter combination performs better in navigation effect when performing the same task, and the average time consumption is 49.9% of the former combination, The CPU utilization is lower, only 24.016% on average, and the dependence on two-dimensional grid map is very low, and it can even navigate to unknown areas of the map.","PeriodicalId":113723,"journal":{"name":"Proceedings of the 6th International Conference on Intelligent Information Processing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th International Conference on Intelligent Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3480571.3480589","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
∗Autonomous mobile robot navigation is a research hotspot in recent years, and path planning technology is the core part of navigation technology. Path planning is often realized by the combination of global and local path planning. Taking gazebo as the experimental platform and using TurtleBot as the experimental object, the navigation performance and resource occupancy rate of Dijkstra + DWA and Dijkstra + TEB are tested, and the performance difference between the two combinations is analyzed. The path planning of the latter combination performs better in navigation effect when performing the same task, and the average time consumption is 49.9% of the former combination, The CPU utilization is lower, only 24.016% on average, and the dependence on two-dimensional grid map is very low, and it can even navigate to unknown areas of the map.
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