Y. Harish, R. K. Kumar, G. M. D. I. Feroz, C. Jada, V. A. Kumar, Mounika Mesa
{"title":"ROBOG: Robo guide with simple learning strategy","authors":"Y. Harish, R. K. Kumar, G. M. D. I. Feroz, C. Jada, V. A. Kumar, Mounika Mesa","doi":"10.1109/TECHSYM.2014.6808051","DOIUrl":null,"url":null,"abstract":"This paper presents ROBOG; an experimental effort in building an autonomous robot that can learn the navigation system of a known terrain and use it for guiding. It is equipped with Artificial Neural Network for the task of Decision making. ROBOG was trained to learn the geographical structure of a floor in an academic block of RGUKT and is tested successfully to guide a person from anywhere to any specific classroom in the trained region. The training of ANN is done with Error Back Propagation algorithm and Particle Swarm Optimization. Results are provided showing the superiority of PSO over conventional EBP in training the ANN. It can easily be trained for other type of structures as well. Some outlook of future work and extensions are suggested.","PeriodicalId":265072,"journal":{"name":"Proceedings of the 2014 IEEE Students' Technology Symposium","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2014 IEEE Students' Technology Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TECHSYM.2014.6808051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
This paper presents ROBOG; an experimental effort in building an autonomous robot that can learn the navigation system of a known terrain and use it for guiding. It is equipped with Artificial Neural Network for the task of Decision making. ROBOG was trained to learn the geographical structure of a floor in an academic block of RGUKT and is tested successfully to guide a person from anywhere to any specific classroom in the trained region. The training of ANN is done with Error Back Propagation algorithm and Particle Swarm Optimization. Results are provided showing the superiority of PSO over conventional EBP in training the ANN. It can easily be trained for other type of structures as well. Some outlook of future work and extensions are suggested.