Optimal Motion Generation of a Flexible Macro-micro Manipulator System Using Genetic Algorithm and Neural Network
In this paper, a new approach to solve the inverse kinematics of a flexible macro-micro manipulator system is proposed. The macro-micro manipulator system consists of a macro flexible manipulator, and a micro rigid manipulator which is used to compensate for the errors at the tip of the system. Appa...
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| creator | Yu Zhang Zengqi Sun Tangwen Yang |
| description | In this paper, a new approach to solve the inverse kinematics of a flexible macro-micro manipulator system is proposed. The macro-micro manipulator system consists of a macro flexible manipulator, and a micro rigid manipulator which is used to compensate for the errors at the tip of the system. Apparently, such a macro-micro system is a redundant system, of which the inverse kinematics remains challenging, with no generic solution to date. Here, optimal joint motions, namely the manipulator system configuration, are generated using a genetic algorithm and a neural network. A fitness function is dedicated to the genetic algorithm to optimize the discrete solution of the inverse kinematics of the manipulator system. Then the discrete solution is further generalized by a forward neural network. A new compensability measure is defined in this paper. The proposed approach shows excellent performance on error compensation, as demonstrated by the simulation results |
| doi_str_mv | 10.1109/RAMECH.2006.252667 |
| format | Conference Proceeding |
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The proposed approach shows excellent performance on error compensation, as demonstrated by the simulation results</description><subject>Computer science</subject><subject>Constraint optimization</subject><subject>Error compensation</subject><subject>genetic algorithm</subject><subject>Genetic algorithms</subject><subject>Intelligent networks</subject><subject>Intelligent systems</subject><subject>Kinematics</subject><subject>Laboratories</subject><subject>macro-micro manipulator</subject><subject>motion planning</subject><subject>neural network</subject><subject>Neural networks</subject><subject>Sun</subject><issn>2158-2181</issn><isbn>1424400244</isbn><isbn>9781424400249</isbn><isbn>1424400252</isbn><isbn>9781424400256</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFj9lOwzAQRY0AiVL6A_DiH0iYcbwkj1XVBamLBOW5chynGLIUxxX074kKEi8z52p05-oSco8QI0L2-DxeTSeLmAHImAkmpbogt8gZ5wC9vvwXnF-RAUORRgxTvCGjrnsHgATTDAAH5HNzCK7WFV21wbUNndvGen3GtqSazir77fLK0pU2vo1q18-eG3c4Vjq0nr6cumBr-tq5Zn92B2fouNq33oW3muqmoGt79H3C2oav1n_cketSV50d_e0h2c6m28kiWm7mT5PxMnIZhAhFIpUsBEdRCKPy3BjDjWQ5AyELYBqVKlAluTaYJWlZKiEwz_prqlBLSIbk4fets9buDr4v6U87DpiqNEl-AFwlXIU</recordid><startdate>200612</startdate><enddate>200612</enddate><creator>Yu Zhang</creator><creator>Zengqi Sun</creator><creator>Tangwen Yang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200612</creationdate><title>Optimal Motion Generation of a Flexible Macro-micro Manipulator System Using Genetic Algorithm and Neural Network</title><author>Yu Zhang ; Zengqi Sun ; Tangwen Yang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-153676d5415d5c7bbccc4c62b2056d02a177d173bac1938ff7551b9205871a603</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Computer science</topic><topic>Constraint optimization</topic><topic>Error compensation</topic><topic>genetic algorithm</topic><topic>Genetic algorithms</topic><topic>Intelligent networks</topic><topic>Intelligent systems</topic><topic>Kinematics</topic><topic>Laboratories</topic><topic>macro-micro manipulator</topic><topic>motion planning</topic><topic>neural network</topic><topic>Neural networks</topic><topic>Sun</topic><toplevel>online_resources</toplevel><creatorcontrib>Yu Zhang</creatorcontrib><creatorcontrib>Zengqi Sun</creatorcontrib><creatorcontrib>Tangwen Yang</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 Electronic Library (IEL)</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>Yu Zhang</au><au>Zengqi Sun</au><au>Tangwen Yang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Optimal Motion Generation of a Flexible Macro-micro Manipulator System Using Genetic Algorithm and Neural Network</atitle><btitle>2006 IEEE Conference on Robotics, Automation and Mechatronics</btitle><stitle>RAMECH</stitle><date>2006-12</date><risdate>2006</risdate><spage>1</spage><epage>6</epage><pages>1-6</pages><issn>2158-2181</issn><isbn>1424400244</isbn><isbn>9781424400249</isbn><eisbn>1424400252</eisbn><eisbn>9781424400256</eisbn><abstract>In this paper, a new approach to solve the inverse kinematics of a flexible macro-micro manipulator system is proposed. The macro-micro manipulator system consists of a macro flexible manipulator, and a micro rigid manipulator which is used to compensate for the errors at the tip of the system. Apparently, such a macro-micro system is a redundant system, of which the inverse kinematics remains challenging, with no generic solution to date. Here, optimal joint motions, namely the manipulator system configuration, are generated using a genetic algorithm and a neural network. A fitness function is dedicated to the genetic algorithm to optimize the discrete solution of the inverse kinematics of the manipulator system. Then the discrete solution is further generalized by a forward neural network. A new compensability measure is defined in this paper. The proposed approach shows excellent performance on error compensation, as demonstrated by the simulation results</abstract><pub>IEEE</pub><doi>10.1109/RAMECH.2006.252667</doi><tpages>6</tpages></addata></record> |
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| ispartof | 2006 IEEE Conference on Robotics, Automation and Mechatronics, 2006, p.1-6 |
| issn | 2158-2181 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Computer science Constraint optimization Error compensation genetic algorithm Genetic algorithms Intelligent networks Intelligent systems Kinematics Laboratories macro-micro manipulator motion planning neural network Neural networks Sun |
| title | Optimal Motion Generation of a Flexible Macro-micro Manipulator System Using Genetic Algorithm and Neural Network |
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