Automatic navigation of mobile robots in unknown environments
Online navigation with known target and unknown obstacles is an interesting problem in mobile robotics. This article presents a technique based on utilization of neural networks and reinforcement learning to enable a mobile robot to learn constructed environments on its own. The robot learns to gene...
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2014
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my.upm.eprints.366012015-10-06T03:26:50Z http://psasir.upm.edu.my/id/eprint/36601/ Automatic navigation of mobile robots in unknown environments Motlagh, Omid Reza Esmaeili Nakhaeinia, Danial Tang, Sai Hong Karasfi, Babak Khaksar, Weria Online navigation with known target and unknown obstacles is an interesting problem in mobile robotics. This article presents a technique based on utilization of neural networks and reinforcement learning to enable a mobile robot to learn constructed environments on its own. The robot learns to generate efficient navigation rules automatically without initial settings of rules by experts. This is regarded as the main contribution of this work compared to traditional fuzzy models based on notion of artificial potential fields. The ability for generalization of rules has also been examined. The initial results qualitatively confirmed the efficiency of the model. More experiments showed at least 32 % of improvement in path planning from the first till the third path planning trial in a sample environment. Analysis of the results, limitations, and recommendations is included for future work. Springer 2014-06 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/36601/1/Automatic%20navigation%20of%20mobile%20robots%20in%20unknown%20environments.pdf Motlagh, Omid Reza Esmaeili and Nakhaeinia, Danial and Tang, Sai Hong and Karasfi, Babak and Khaksar, Weria (2014) Automatic navigation of mobile robots in unknown environments. Neural Computing & Applications, 24 (7-8). pp. 1569-1581. ISSN 0941-0643; ESSN: 1433-3058 http://link.springer.com/article/10.1007%2Fs00521-013-1393-z 10.1007/s00521-013-1393-z |
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Online navigation with known target and unknown obstacles is an interesting problem in mobile robotics. This article presents a technique based on utilization of neural networks and reinforcement learning to enable a mobile robot to learn constructed environments on its own. The robot learns to generate efficient navigation rules automatically without initial settings of rules by experts. This is regarded as the main contribution of this work compared to traditional fuzzy models based on notion of artificial potential fields. The ability for generalization of rules has also been examined. The initial results qualitatively confirmed the efficiency of the model. More experiments showed at least 32 % of improvement in path planning from the first till the third path planning trial in a sample environment. Analysis of the results, limitations, and recommendations is included for future work. |
format |
Article |
author |
Motlagh, Omid Reza Esmaeili Nakhaeinia, Danial Tang, Sai Hong Karasfi, Babak Khaksar, Weria |
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Motlagh, Omid Reza Esmaeili Nakhaeinia, Danial Tang, Sai Hong Karasfi, Babak Khaksar, Weria Automatic navigation of mobile robots in unknown environments |
author_facet |
Motlagh, Omid Reza Esmaeili Nakhaeinia, Danial Tang, Sai Hong Karasfi, Babak Khaksar, Weria |
author_sort |
Motlagh, Omid Reza Esmaeili |
title |
Automatic navigation of mobile robots in unknown environments |
title_short |
Automatic navigation of mobile robots in unknown environments |
title_full |
Automatic navigation of mobile robots in unknown environments |
title_fullStr |
Automatic navigation of mobile robots in unknown environments |
title_full_unstemmed |
Automatic navigation of mobile robots in unknown environments |
title_sort |
automatic navigation of mobile robots in unknown environments |
publisher |
Springer |
publishDate |
2014 |
url |
http://psasir.upm.edu.my/id/eprint/36601/1/Automatic%20navigation%20of%20mobile%20robots%20in%20unknown%20environments.pdf http://psasir.upm.edu.my/id/eprint/36601/ http://link.springer.com/article/10.1007%2Fs00521-013-1393-z |
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