{"title":"基于模糊逻辑的移动机器人静态和动态障碍物有效路径规划","authors":"Amir Nasrinahar, Joon Huang Chuah","doi":"10.1109/ICCSCE.2016.7893541","DOIUrl":null,"url":null,"abstract":"Navigation of a mobile robot in cluttered environment while ensuring obstacle avoidance and maximum safety is indeed a challenging task. Route planning is an important issue in the field of autonomous mobile robots which makes them capable to travel from one position to another in various environments including both static and dynamic obstacles without any human intervention. This research is carried out with the purpose of designing and programming a mobile robot using two separated fuzzy logic controllers and developing an efficient algorithm in order to avoid both static and dynamic obstacles. In this work, four essential behavior controllers are designed and implemented onto the robot to assist its navigation towards the goal, i.e. goal reaching behavior, speed control behavior, goal searching behavior and obstacle avoidance behavior. For obstacle avoidance behavior, Sugeno fuzzy logic was applied. The simulation of this research was done by using MATLAB software where a mobile robot and some test environments with different complexity were created. Several navigation experiments were conducted and the robot's behavior were carefully observed. Analysis of the robot's performance validated the effectiveness of the proposed controllers and the robot could successfully navigate towards the goal in all experimental environments.","PeriodicalId":6540,"journal":{"name":"2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"471 1","pages":"34-38"},"PeriodicalIF":0.0000,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Effective route planning of a mobile robot for static and dynamic obstacles with Fuzzy Logic\",\"authors\":\"Amir Nasrinahar, Joon Huang Chuah\",\"doi\":\"10.1109/ICCSCE.2016.7893541\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Navigation of a mobile robot in cluttered environment while ensuring obstacle avoidance and maximum safety is indeed a challenging task. Route planning is an important issue in the field of autonomous mobile robots which makes them capable to travel from one position to another in various environments including both static and dynamic obstacles without any human intervention. This research is carried out with the purpose of designing and programming a mobile robot using two separated fuzzy logic controllers and developing an efficient algorithm in order to avoid both static and dynamic obstacles. In this work, four essential behavior controllers are designed and implemented onto the robot to assist its navigation towards the goal, i.e. goal reaching behavior, speed control behavior, goal searching behavior and obstacle avoidance behavior. For obstacle avoidance behavior, Sugeno fuzzy logic was applied. The simulation of this research was done by using MATLAB software where a mobile robot and some test environments with different complexity were created. Several navigation experiments were conducted and the robot's behavior were carefully observed. Analysis of the robot's performance validated the effectiveness of the proposed controllers and the robot could successfully navigate towards the goal in all experimental environments.\",\"PeriodicalId\":6540,\"journal\":{\"name\":\"2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)\",\"volume\":\"471 1\",\"pages\":\"34-38\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSCE.2016.7893541\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSCE.2016.7893541","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Effective route planning of a mobile robot for static and dynamic obstacles with Fuzzy Logic
Navigation of a mobile robot in cluttered environment while ensuring obstacle avoidance and maximum safety is indeed a challenging task. Route planning is an important issue in the field of autonomous mobile robots which makes them capable to travel from one position to another in various environments including both static and dynamic obstacles without any human intervention. This research is carried out with the purpose of designing and programming a mobile robot using two separated fuzzy logic controllers and developing an efficient algorithm in order to avoid both static and dynamic obstacles. In this work, four essential behavior controllers are designed and implemented onto the robot to assist its navigation towards the goal, i.e. goal reaching behavior, speed control behavior, goal searching behavior and obstacle avoidance behavior. For obstacle avoidance behavior, Sugeno fuzzy logic was applied. The simulation of this research was done by using MATLAB software where a mobile robot and some test environments with different complexity were created. Several navigation experiments were conducted and the robot's behavior were carefully observed. Analysis of the robot's performance validated the effectiveness of the proposed controllers and the robot could successfully navigate towards the goal in all experimental environments.