Path Planning Approach in Unknown Environment
This paper presents a new algorithm of path planning for mobile robots, which utilises the characteristics of the obstacle border and fuzzy logical reasoning. The environment topology or working space is described by the time-variable grid method that can be further described by the moving obstacles...
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| Veröffentlicht in: | International journal of automation and computing 2010-08, Vol.7 (3), p.310-316 |
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| container_title | International journal of automation and computing |
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| creator | Wang, Ting-Kai Dang, Quan Pan, Pei-Yuan |
| description | This paper presents a new algorithm of path planning for mobile robots, which utilises the characteristics of the obstacle border and fuzzy logical reasoning. The environment topology or working space is described by the time-variable grid method that can be further described by the moving obstacles and the variation of path safety. Based on the algorithm, a new path planning approach for mobile robots in an unknown environment has been developed. The path planning approach can let a mobile robot find a safe path from the current position to the goal based on a sensor system. The two types of machine learning： advancing learning and exploitation learning or trial learning are explored, and both are applied to the learning of mobile robot path planning algorithm. Comparison with A＊ path planning approach and various simulation results are given to demonstrate the efficiency of the algorithm. This path planning approach can also be applied to computer games. |
| doi_str_mv | 10.1007/s11633-010-0508-6 |
| format | Article |
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The environment topology or working space is described by the time-variable grid method that can be further described by the moving obstacles and the variation of path safety. Based on the algorithm, a new path planning approach for mobile robots in an unknown environment has been developed. The path planning approach can let a mobile robot find a safe path from the current position to the goal based on a sensor system. The two types of machine learning： advancing learning and exploitation learning or trial learning are explored, and both are applied to the learning of mobile robot path planning algorithm. Comparison with A＊ path planning approach and various simulation results are given to demonstrate the efficiency of the algorithm. 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All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c442t-7a325bc83d966c79360783e198abdfebedb2e9f759c137e44150a9073bad21ba3</citedby><cites>FETCH-LOGICAL-c442t-7a325bc83d966c79360783e198abdfebedb2e9f759c137e44150a9073bad21ba3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/88433X/88433X.jpg</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2918682820?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21388,27924,27925,33744,43805,64385,64389,72469</link.rule.ids></links><search><creatorcontrib>Wang, Ting-Kai</creatorcontrib><creatorcontrib>Dang, Quan</creatorcontrib><creatorcontrib>Pan, Pei-Yuan</creatorcontrib><title>Path Planning Approach in Unknown Environment</title><title>International journal of automation and computing</title><addtitle>Int. J. Autom. Comput</addtitle><addtitle>International Journal of Automation and computing</addtitle><description>This paper presents a new algorithm of path planning for mobile robots, which utilises the characteristics of the obstacle border and fuzzy logical reasoning. The environment topology or working space is described by the time-variable grid method that can be further described by the moving obstacles and the variation of path safety. Based on the algorithm, a new path planning approach for mobile robots in an unknown environment has been developed. The path planning approach can let a mobile robot find a safe path from the current position to the goal based on a sensor system. The two types of machine learning： advancing learning and exploitation learning or trial learning are explored, and both are applied to the learning of mobile robot path planning algorithm. Comparison with A＊ path planning approach and various simulation results are given to demonstrate the efficiency of the algorithm. This path planning approach can also be applied to computer games.</description><subject>Algorithms</subject><subject>CAE) and Design</subject><subject>Cognition & reasoning</subject><subject>Computer & video games</subject><subject>Computer Applications</subject><subject>Computer simulation</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Control</subject><subject>Engineering</subject><subject>Fuzzy</subject><subject>Fuzzy logic</subject><subject>Grid method</subject><subject>Learning</subject><subject>Machine learning</subject><subject>Mechatronics</subject><subject>Moving obstacles</subject><subject>Obstacles</subject><subject>Path planning</subject><subject>Robotics</subject><subject>Robots</subject><subject>Topology</subject><subject>Unknown environments</subject><subject>传感器系统</subject><subject>机器人路径规划</subject><subject>机器学习</subject><subject>模糊逻辑推理</subject><subject>环境</subject><subject>移动机器人</subject><subject>移动路径</subject><subject>路径规划算法</subject><issn>1476-8186</issn><issn>2153-182X</issn><issn>1751-8520</issn><issn>2153-1838</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kT1PwzAQhiMEEgj6A9giWFgMdz7Hdsaq4ktCogOdLSdx0pTWKXELor8eo1QIMTDdDc_73sebJOcI1wigbgKiJGKAwCADzeRBcoIqQ6YzDoexF0oyjVoeJ6MQ2gIIuaRMiJOETe1mnk6X1vvWN-l4ve47W87T1qcz_-q7D5_e-ve27_zK-c1ZclTbZXCjfT1NZne3L5MH9vR8_zgZP7FSCL5hyhLPilJTlUtZqpwkKE0Oc22LqnaFqwru8lpleYmknBCYgc1BUWErjoWl04QNvh_W19Y3ZtFtex8nmmaxq-afi7DbGcfjwUAAOvJXAx-3f9u6sDGrNpRuGc9y3TYYlAoF15wwopd_0B9vnscX6YjBfxSSjL_LlcoihQNV9l0IvavNum9Xtv80COY7GTMkY-Ki5jsZI6OGD5oQWd-4_pfzP6KL_aB555u3qDOFLV_rdukMCSWQhKAvfW-Ytw</recordid><startdate>20100801</startdate><enddate>20100801</enddate><creator>Wang, Ting-Kai</creator><creator>Dang, Quan</creator><creator>Pan, Pei-Yuan</creator><general>Institute of Automation, Chinese Academy of Sciences</general><general>Springer Nature B.V</general><general>Faculty of Computing,London Metropolitan University,London N7 8DB,UK</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W92</scope><scope>~WA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7SC</scope><scope>8FD</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20100801</creationdate><title>Path Planning Approach in Unknown Environment</title><author>Wang, Ting-Kai ; Dang, Quan ; Pan, Pei-Yuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c442t-7a325bc83d966c79360783e198abdfebedb2e9f759c137e44150a9073bad21ba3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Algorithms</topic><topic>CAE) and Design</topic><topic>Cognition & reasoning</topic><topic>Computer & video games</topic><topic>Computer Applications</topic><topic>Computer simulation</topic><topic>Computer-Aided Engineering (CAD</topic><topic>Control</topic><topic>Engineering</topic><topic>Fuzzy</topic><topic>Fuzzy logic</topic><topic>Grid method</topic><topic>Learning</topic><topic>Machine learning</topic><topic>Mechatronics</topic><topic>Moving obstacles</topic><topic>Obstacles</topic><topic>Path planning</topic><topic>Robotics</topic><topic>Robots</topic><topic>Topology</topic><topic>Unknown environments</topic><topic>传感器系统</topic><topic>机器人路径规划</topic><topic>机器学习</topic><topic>模糊逻辑推理</topic><topic>环境</topic><topic>移动机器人</topic><topic>移动路径</topic><topic>路径规划算法</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Ting-Kai</creatorcontrib><creatorcontrib>Dang, Quan</creatorcontrib><creatorcontrib>Pan, Pei-Yuan</creatorcontrib><collection>中文科技期刊数据库</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>中文科技期刊数据库-7.0平台</collection><collection>中文科技期刊数据库-工程技术</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>International journal of automation and computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Ting-Kai</au><au>Dang, Quan</au><au>Pan, Pei-Yuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Path Planning Approach in Unknown Environment</atitle><jtitle>International journal of automation and computing</jtitle><stitle>Int. J. Autom. Comput</stitle><addtitle>International Journal of Automation and computing</addtitle><date>2010-08-01</date><risdate>2010</risdate><volume>7</volume><issue>3</issue><spage>310</spage><epage>316</epage><pages>310-316</pages><issn>1476-8186</issn><issn>2153-182X</issn><eissn>1751-8520</eissn><eissn>2153-1838</eissn><abstract>This paper presents a new algorithm of path planning for mobile robots, which utilises the characteristics of the obstacle border and fuzzy logical reasoning. The environment topology or working space is described by the time-variable grid method that can be further described by the moving obstacles and the variation of path safety. Based on the algorithm, a new path planning approach for mobile robots in an unknown environment has been developed. The path planning approach can let a mobile robot find a safe path from the current position to the goal based on a sensor system. The two types of machine learning： advancing learning and exploitation learning or trial learning are explored, and both are applied to the learning of mobile robot path planning algorithm. Comparison with A＊ path planning approach and various simulation results are given to demonstrate the efficiency of the algorithm. This path planning approach can also be applied to computer games.</abstract><cop>Heidelberg</cop><pub>Institute of Automation, Chinese Academy of Sciences</pub><doi>10.1007/s11633-010-0508-6</doi><tpages>7</tpages></addata></record> |
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| ispartof | International journal of automation and computing, 2010-08, Vol.7 (3), p.310-316 |
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| subjects | Algorithms CAE) and Design Cognition & reasoning Computer & video games Computer Applications Computer simulation Computer-Aided Engineering (CAD Control Engineering Fuzzy Fuzzy logic Grid method Learning Machine learning Mechatronics Moving obstacles Obstacles Path planning Robotics Robots Topology Unknown environments 传感器系统 机器人路径规划 机器学习 模糊逻辑推理 环境 移动机器人 移动路径 路径规划算法 |
| title | Path Planning Approach in Unknown Environment |
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