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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bashan Zuo Jiaxin Chen Wang, L. Ying Wang |
| Copyright Year | 2014 |
| Description | Author affiliation: Dept. of Electr. & Mechatron. Eng., Southern Polytech. State Univ., Marietta, GA, USA (Bashan Zuo; Jiaxin Chen; Ying Wang) || Dept. of Math., Southern Polytech. State Univ., Marietta, GA, USA (Wang, L.) |
| Abstract | It is a challenging task for an autonomous robot to navigate in an unknown environment. Machine learning could be useful to support the robot to adapt to the environment and learn the correct navigation skills quickly. In this paper, a reinforcement learning (Q-learning) based approach is proposed to help a robot to move out of an unknown maze. The definitions of the world states, actions and rewards of the algorithm are presented and some experiments are completed to validate the approach. The experimental results show that the proposed approach does have a good performance on mobile robot navigation. |
| Starting Page | 3452 |
| Ending Page | 3457 |
| File Size | 240957 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479938407 |
| DOI | 10.1109/SMC.2014.6974463 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-10-05 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Navigation Robot sensing systems Mobile robots Collision avoidance Sonar Q-learning Mobile Robots |
| Content Type | Text |
| Resource Type | Article |
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