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Conference Paper: An adaptive fuzzy approach to obstacle avoidance
Title | An adaptive fuzzy approach to obstacle avoidance |
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Authors | |
Keywords | Computers Cybernetics |
Issue Date | 1998 |
Publisher | IEEE. |
Citation | IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings, San Diego, California, USA, 11-14 October 1998, v. 4, p. 3418-3423 How to Cite? |
Abstract | Reinforcement learning based on a new training method previously reported guarantees convergence and an almost complete set of rules. However, there are two shortcomings remained: 1) the membership functions of the input sensor readings are determined manually and take the same form; and 2) there are still a small number of blank rules needed to be manually inserted. To address these two issues, this paper proposes an adaptive fuzzy approach using a supervised learning method based on backpropagation to determine the parameters for the membership functions for each sensor reading. By having different input fuzzy sets, each sensor reading contributes differently in avoiding obstacles. Our simulations show that the proposed system converges rapidly to a complete set of rules, and if there are no conflicting input-output data pairs in the training sets, the proposed system performs collision-free obstacle avoidance. |
Persistent Identifier | http://hdl.handle.net/10722/46151 |
ISSN | 2020 SCImago Journal Rankings: 0.168 |
DC Field | Value | Language |
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dc.contributor.author | Yung, NHC | en_HK |
dc.contributor.author | Ye, C | en_HK |
dc.date.accessioned | 2007-10-30T06:43:35Z | - |
dc.date.available | 2007-10-30T06:43:35Z | - |
dc.date.issued | 1998 | en_HK |
dc.identifier.citation | IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings, San Diego, California, USA, 11-14 October 1998, v. 4, p. 3418-3423 | en_HK |
dc.identifier.issn | 1062-922X | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/46151 | - |
dc.description.abstract | Reinforcement learning based on a new training method previously reported guarantees convergence and an almost complete set of rules. However, there are two shortcomings remained: 1) the membership functions of the input sensor readings are determined manually and take the same form; and 2) there are still a small number of blank rules needed to be manually inserted. To address these two issues, this paper proposes an adaptive fuzzy approach using a supervised learning method based on backpropagation to determine the parameters for the membership functions for each sensor reading. By having different input fuzzy sets, each sensor reading contributes differently in avoiding obstacles. Our simulations show that the proposed system converges rapidly to a complete set of rules, and if there are no conflicting input-output data pairs in the training sets, the proposed system performs collision-free obstacle avoidance. | en_HK |
dc.format.extent | 605339 bytes | - |
dc.format.extent | 10863 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.format.mimetype | text/plain | - |
dc.language | eng | en_HK |
dc.publisher | IEEE. | en_HK |
dc.rights | ©1998 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | - |
dc.subject | Computers | en_HK |
dc.subject | Cybernetics | en_HK |
dc.title | An adaptive fuzzy approach to obstacle avoidance | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.openurl | http://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1062-922X&volume=4&spage=3418&epage=3423&date=1998&atitle=An+adaptive+fuzzy+approach+to+obstacle+avoidance | en_HK |
dc.description.nature | published_or_final_version | en_HK |
dc.identifier.doi | 10.1109/ICSMC.1998.726539 | en_HK |
dc.identifier.hkuros | 45906 | - |
dc.identifier.issnl | 1062-922X | - |