Reduction in the number of binary variables for inter-sample avoidance in trajectory optimizers using mixed-integer linear programming
Summary This note proposes the use of a tuple encoding scheme to reduce the number of binary variables involved in trajectory optimization problems with inter‐sample avoidance constraints. Copyright © 2016 John Wiley & Sons, Ltd.
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| Veröffentlicht in: | International journal of robust and nonlinear control 2016-11, Vol.26 (16), p.3662-3669 |
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| container_end_page | 3669 |
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| container_issue | 16 |
| container_start_page | 3662 |
| container_title | International journal of robust and nonlinear control |
| container_volume | 26 |
| creator | Afonso, Rubens J. M. Galvão, Roberto K. H. Kienitz, Karl H. |
| description | Summary
This note proposes the use of a tuple encoding scheme to reduce the number of binary variables involved in trajectory optimization problems with inter‐sample avoidance constraints. Copyright © 2016 John Wiley & Sons, Ltd. |
| doi_str_mv | 10.1002/rnc.3529 |
| format | Article |
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This note proposes the use of a tuple encoding scheme to reduce the number of binary variables involved in trajectory optimization problems with inter‐sample avoidance constraints. Copyright © 2016 John Wiley & Sons, Ltd.</description><subject>Avoidance</subject><subject>binary variables</subject><subject>Encoding</subject><subject>inter-sample avoidance</subject><subject>Linear programming</subject><subject>mixed-integer linear programming</subject><subject>Nonlinearity</subject><subject>Reduction</subject><subject>Robust control</subject><subject>Trajectories</subject><subject>Trajectory optimization</subject><issn>1049-8923</issn><issn>1099-1239</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNpdkdtKxDAQhosoeAQfIeCNN11zbJtLWXQVREFW9C6k7XTN2iZr0np6AJ_b1BUFr_6B-f6ZYf4kOSR4QjCmJ95WEyao3Eh2CJYyJZTJzbHmMi0kZdvJbghLjGOP8p3k8xbqoeqNs8hY1D8CskNXgkeuQaWx2r-jF-2NLlsIqHE-Uj34NOhu1QLSL87U2lbwbfZ6CVXvosWtetOZD_ABDcHYBerMG9Tp6F3E2a2xoD1aebfwuusisJ9sNboNcPCje8nd-dl8epFe3cwup6dXacUYkSmVjcSNFAVlglDd5EWOa66lYCBEbHDeYE5zQSpa61zqMisFzXgmGIeayYbtJcfruXH38wChV50JFbSttuCGoEghBMupLLKIHv1Dl27wNl4XKSo5ibtxpNI19WpaeFcrb7r4M0WwGtNQMQ01pqFur6ej_vEm9PD2y2v_pLKc5ULdX8_UA59nU3rBlWBfENyPCw</recordid><startdate>20161110</startdate><enddate>20161110</enddate><creator>Afonso, Rubens J. M.</creator><creator>Galvão, Roberto K. H.</creator><creator>Kienitz, Karl H.</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20161110</creationdate><title>Reduction in the number of binary variables for inter-sample avoidance in trajectory optimizers using mixed-integer linear programming</title><author>Afonso, Rubens J. M. ; Galvão, Roberto K. H. ; Kienitz, Karl H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3319-29f90f95823512af7870d4a953e550f944f042751c2da79ab6b52646534ed39f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Avoidance</topic><topic>binary variables</topic><topic>Encoding</topic><topic>inter-sample avoidance</topic><topic>Linear programming</topic><topic>mixed-integer linear programming</topic><topic>Nonlinearity</topic><topic>Reduction</topic><topic>Robust control</topic><topic>Trajectories</topic><topic>Trajectory optimization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Afonso, Rubens J. M.</creatorcontrib><creatorcontrib>Galvão, Roberto K. H.</creatorcontrib><creatorcontrib>Kienitz, Karl H.</creatorcontrib><collection>Istex</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International journal of robust and nonlinear control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Afonso, Rubens J. M.</au><au>Galvão, Roberto K. H.</au><au>Kienitz, Karl H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reduction in the number of binary variables for inter-sample avoidance in trajectory optimizers using mixed-integer linear programming</atitle><jtitle>International journal of robust and nonlinear control</jtitle><addtitle>Int. J. Robust. Nonlinear Control</addtitle><date>2016-11-10</date><risdate>2016</risdate><volume>26</volume><issue>16</issue><spage>3662</spage><epage>3669</epage><pages>3662-3669</pages><issn>1049-8923</issn><eissn>1099-1239</eissn><abstract>Summary
This note proposes the use of a tuple encoding scheme to reduce the number of binary variables involved in trajectory optimization problems with inter‐sample avoidance constraints. Copyright © 2016 John Wiley & Sons, Ltd.</abstract><cop>Bognor Regis</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/rnc.3529</doi><tpages>8</tpages></addata></record> |
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| ispartof | International journal of robust and nonlinear control, 2016-11, Vol.26 (16), p.3662-3669 |
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| language | eng |
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| source | Wiley Online Library All Journals |
| subjects | Avoidance binary variables Encoding inter-sample avoidance Linear programming mixed-integer linear programming Nonlinearity Reduction Robust control Trajectories Trajectory optimization |
| title | Reduction in the number of binary variables for inter-sample avoidance in trajectory optimizers using mixed-integer linear programming |
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