{"title":"Real time navigation strategies for webots using fuzzy controller","authors":"K. K. Pandey, P. K. Mohanty, D. Parhi","doi":"10.1109/ISCO.2014.7103910","DOIUrl":null,"url":null,"abstract":"Since last decade, designing of an autonomous mobile robot for complex systems involves the interaction between numerous heterogeneous components (software and hardware) and according to its application; they are heavily used in environments where human involvement is restricted, unmanageable, or hazardous. For researchers, the proper movement of robot inside working environment is the challenging tasks; accordingly, day by day research has been made on navigation system of mobile robot. As a result, we design mobile robot controller algorithms that helps mobile robot to navigate in environment according to given task and avoid obstacle. In order to avoid obstacles in efficient manner and to reach the goal position through complicated path (i.e. surrounded by various types of obstacles), we designed sensor integration based fuzzy logic controller that transform the direction of mobile robot according to obstacle position and create collision free path. To create a collision free path certain input parameter, output parameter, fuzzy membership functions and `If-Then rule' fuzzy interface system are executed in algorithm. All these information are combined together to map the environment. To attain the collision free path, obstacle avoidance is done through changing the steering angle at point to point with the help of sensor network. The controller covers the environment at which starting point, goal point and obstacle position is known. Priority is made to avoid the obstacle during goal seeking behavior by the robot. The efficiency of the recommended technique is confirmed by a succession of simulations. To check the simulation result for proposed controller, 3D Physics-based simulation software is used.","PeriodicalId":119329,"journal":{"name":"2014 IEEE 8th International Conference on Intelligent Systems and Control (ISCO)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 8th International Conference on Intelligent Systems and Control (ISCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCO.2014.7103910","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Since last decade, designing of an autonomous mobile robot for complex systems involves the interaction between numerous heterogeneous components (software and hardware) and according to its application; they are heavily used in environments where human involvement is restricted, unmanageable, or hazardous. For researchers, the proper movement of robot inside working environment is the challenging tasks; accordingly, day by day research has been made on navigation system of mobile robot. As a result, we design mobile robot controller algorithms that helps mobile robot to navigate in environment according to given task and avoid obstacle. In order to avoid obstacles in efficient manner and to reach the goal position through complicated path (i.e. surrounded by various types of obstacles), we designed sensor integration based fuzzy logic controller that transform the direction of mobile robot according to obstacle position and create collision free path. To create a collision free path certain input parameter, output parameter, fuzzy membership functions and `If-Then rule' fuzzy interface system are executed in algorithm. All these information are combined together to map the environment. To attain the collision free path, obstacle avoidance is done through changing the steering angle at point to point with the help of sensor network. The controller covers the environment at which starting point, goal point and obstacle position is known. Priority is made to avoid the obstacle during goal seeking behavior by the robot. The efficiency of the recommended technique is confirmed by a succession of simulations. To check the simulation result for proposed controller, 3D Physics-based simulation software is used.