Obstacle avoidance in mobile robot using Neural Network
Investigate mobile robot's history, obstacle avoidance is one of most important research area and also the foundation of building robot's successful behaviors. This paper proposes a Neural Network control system that is able to guide the mobile robots (AmigoBot and P3DX) traverse through a...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 5085 |
|---|---|
| container_issue | |
| container_start_page | 5082 |
| container_title | |
| container_volume | |
| creator | Kai-Hui Chi Lee, M R |
| description | Investigate mobile robot's history, obstacle avoidance is one of most important research area and also the foundation of building robot's successful behaviors. This paper proposes a Neural Network control system that is able to guide the mobile robots (AmigoBot and P3DX) traverse through a maze with arbitrary obstacles. The pattern is trained by using Matlab toolbox and Aria library for motion control. There are 256 specific patterns defined to help robot organize the situation. For input data, sonar and laser range finder are two main sensors for passing on information of environment. The empirical results show the effectiveness and the validity of the obstacle avoidance behavior of Neural Network control strategy. |
| doi_str_mv | 10.1109/CECNET.2011.5768815 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5768815</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5768815</ieee_id><sourcerecordid>5768815</sourcerecordid><originalsourceid>FETCH-LOGICAL-c140t-73d1580501bd023359a1672e3b50f8ded4989ef30ffe32728b8ce256206cae453</originalsourceid><addsrcrecordid>eNo1j81Kw0AUhUdEUGueoJu8QOK983uzlBB_oLSbui4zyY2MpokkqeLbG7Cezcf5FgeOEGuEHBGK-7Iqt9U-l4CYG2eJ0FyIpHCEFiVpbZy8FLf_hehaJNP0DkuskRrxRrhdmGZfd5z6ryE2vq85jX16HEJc3DiEYU5PU-zf0i2fRt8tmL-H8eNOXLW-mzg5cyVeH6t9-Zxtdk8v5cMmq1HDnDnVoCEwgKEBqZQpPFonWQUDLTXc6IIKbhW0LSvpJAWqWRorwdaetVErsf7bjcx8-Bzj0Y8_h_NX9QsApUbf</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Obstacle avoidance in mobile robot using Neural Network</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Kai-Hui Chi ; Lee, M R</creator><creatorcontrib>Kai-Hui Chi ; Lee, M R</creatorcontrib><description>Investigate mobile robot's history, obstacle avoidance is one of most important research area and also the foundation of building robot's successful behaviors. This paper proposes a Neural Network control system that is able to guide the mobile robots (AmigoBot and P3DX) traverse through a maze with arbitrary obstacles. The pattern is trained by using Matlab toolbox and Aria library for motion control. There are 256 specific patterns defined to help robot organize the situation. For input data, sonar and laser range finder are two main sensors for passing on information of environment. The empirical results show the effectiveness and the validity of the obstacle avoidance behavior of Neural Network control strategy.</description><identifier>ISBN: 1612844588</identifier><identifier>ISBN: 9781612844589</identifier><identifier>EISBN: 9781612844572</identifier><identifier>EISBN: 1612844596</identifier><identifier>EISBN: 9781612844596</identifier><identifier>EISBN: 161284457X</identifier><identifier>DOI: 10.1109/CECNET.2011.5768815</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Collision avoidance ; Intelligent Control ; Mobile Robot ; Mobile robots ; Neural Network ; Obstacle Avoidance ; Robot sensing systems ; Sonar ; Sonar navigation</subject><ispartof>2011 International Conference on Consumer Electronics, Communications and Networks (CECNet), 2011, p.5082-5085</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c140t-73d1580501bd023359a1672e3b50f8ded4989ef30ffe32728b8ce256206cae453</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5768815$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5768815$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Kai-Hui Chi</creatorcontrib><creatorcontrib>Lee, M R</creatorcontrib><title>Obstacle avoidance in mobile robot using Neural Network</title><title>2011 International Conference on Consumer Electronics, Communications and Networks (CECNet)</title><addtitle>CECNET</addtitle><description>Investigate mobile robot's history, obstacle avoidance is one of most important research area and also the foundation of building robot's successful behaviors. This paper proposes a Neural Network control system that is able to guide the mobile robots (AmigoBot and P3DX) traverse through a maze with arbitrary obstacles. The pattern is trained by using Matlab toolbox and Aria library for motion control. There are 256 specific patterns defined to help robot organize the situation. For input data, sonar and laser range finder are two main sensors for passing on information of environment. The empirical results show the effectiveness and the validity of the obstacle avoidance behavior of Neural Network control strategy.</description><subject>Artificial neural networks</subject><subject>Collision avoidance</subject><subject>Intelligent Control</subject><subject>Mobile Robot</subject><subject>Mobile robots</subject><subject>Neural Network</subject><subject>Obstacle Avoidance</subject><subject>Robot sensing systems</subject><subject>Sonar</subject><subject>Sonar navigation</subject><isbn>1612844588</isbn><isbn>9781612844589</isbn><isbn>9781612844572</isbn><isbn>1612844596</isbn><isbn>9781612844596</isbn><isbn>161284457X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1j81Kw0AUhUdEUGueoJu8QOK983uzlBB_oLSbui4zyY2MpokkqeLbG7Cezcf5FgeOEGuEHBGK-7Iqt9U-l4CYG2eJ0FyIpHCEFiVpbZy8FLf_hehaJNP0DkuskRrxRrhdmGZfd5z6ryE2vq85jX16HEJc3DiEYU5PU-zf0i2fRt8tmL-H8eNOXLW-mzg5cyVeH6t9-Zxtdk8v5cMmq1HDnDnVoCEwgKEBqZQpPFonWQUDLTXc6IIKbhW0LSvpJAWqWRorwdaetVErsf7bjcx8-Bzj0Y8_h_NX9QsApUbf</recordid><startdate>201104</startdate><enddate>201104</enddate><creator>Kai-Hui Chi</creator><creator>Lee, M R</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201104</creationdate><title>Obstacle avoidance in mobile robot using Neural Network</title><author>Kai-Hui Chi ; Lee, M R</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c140t-73d1580501bd023359a1672e3b50f8ded4989ef30ffe32728b8ce256206cae453</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Artificial neural networks</topic><topic>Collision avoidance</topic><topic>Intelligent Control</topic><topic>Mobile Robot</topic><topic>Mobile robots</topic><topic>Neural Network</topic><topic>Obstacle Avoidance</topic><topic>Robot sensing systems</topic><topic>Sonar</topic><topic>Sonar navigation</topic><toplevel>online_resources</toplevel><creatorcontrib>Kai-Hui Chi</creatorcontrib><creatorcontrib>Lee, M R</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kai-Hui Chi</au><au>Lee, M R</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Obstacle avoidance in mobile robot using Neural Network</atitle><btitle>2011 International Conference on Consumer Electronics, Communications and Networks (CECNet)</btitle><stitle>CECNET</stitle><date>2011-04</date><risdate>2011</risdate><spage>5082</spage><epage>5085</epage><pages>5082-5085</pages><isbn>1612844588</isbn><isbn>9781612844589</isbn><eisbn>9781612844572</eisbn><eisbn>1612844596</eisbn><eisbn>9781612844596</eisbn><eisbn>161284457X</eisbn><abstract>Investigate mobile robot's history, obstacle avoidance is one of most important research area and also the foundation of building robot's successful behaviors. This paper proposes a Neural Network control system that is able to guide the mobile robots (AmigoBot and P3DX) traverse through a maze with arbitrary obstacles. The pattern is trained by using Matlab toolbox and Aria library for motion control. There are 256 specific patterns defined to help robot organize the situation. For input data, sonar and laser range finder are two main sensors for passing on information of environment. The empirical results show the effectiveness and the validity of the obstacle avoidance behavior of Neural Network control strategy.</abstract><pub>IEEE</pub><doi>10.1109/CECNET.2011.5768815</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 1612844588 |
| ispartof | 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet), 2011, p.5082-5085 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_5768815 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Collision avoidance Intelligent Control Mobile Robot Mobile robots Neural Network Obstacle Avoidance Robot sensing systems Sonar Sonar navigation |
| title | Obstacle avoidance in mobile robot using Neural Network |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-24T20%3A16%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Obstacle%20avoidance%20in%20mobile%20robot%20using%20Neural%20Network&rft.btitle=2011%20International%20Conference%20on%20Consumer%20Electronics,%20Communications%20and%20Networks%20(CECNet)&rft.au=Kai-Hui%20Chi&rft.date=2011-04&rft.spage=5082&rft.epage=5085&rft.pages=5082-5085&rft.isbn=1612844588&rft.isbn_list=9781612844589&rft_id=info:doi/10.1109/CECNET.2011.5768815&rft_dat=%3Cieee_6IE%3E5768815%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781612844572&rft.eisbn_list=1612844596&rft.eisbn_list=9781612844596&rft.eisbn_list=161284457X&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5768815&rfr_iscdi=true |