Robot Trajectory Planning Based on the Energy Management Strategy
With the increasing demand for automated production technology in national defense, industry, agriculture, and other fields, the status and role of mobile robots are becoming more and more pivotal. The intelligent technology of robots is becoming more and more demanding in terms of reliability, stab...
Gespeichert in:
| Veröffentlicht in: | Mathematical problems in engineering 2022-09, Vol.2022, p.1-11 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 11 |
|---|---|
| container_issue | |
| container_start_page | 1 |
| container_title | Mathematical problems in engineering |
| container_volume | 2022 |
| creator | Li, Mingkai |
| description | With the increasing demand for automated production technology in national defense, industry, agriculture, and other fields, the status and role of mobile robots are becoming more and more pivotal. The intelligent technology of robots is becoming more and more demanding in terms of reliability, stability, efficiency, and adaptability, and the autonomous navigation technology of mobile robots is facing new challenges. This paper introduces the basic principle of traditional A∗ algorithm, points out the problems of this algorithm such as cumbersome calculation, large turning angle, and unsmooth derived trajectory planning, and proposes an improved A∗ algorithm. The improved algorithm introduces a two-way alternating search mechanism to improve the efficiency of the search path, and the improvement of the heuristic function solves the problem that the two-way alternating search A∗ algorithm takes more time when there are obstacles perpendicular to the path on the way to the encounter. Simulation experiments in different raster environments prove that compared with the traditional A∗ algorithm, genetic algorithm, and simulated annealing algorithm, the improved A∗ algorithm can significantly improve the search path speed, while overcoming the disadvantages of many path turning angles and large turning angles and is an efficient and feasible algorithm for raster map environments. |
| doi_str_mv | 10.1155/2022/9597075 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2715336017</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2715336017</sourcerecordid><originalsourceid>FETCH-LOGICAL-c224t-9da24009ef068ce5f5a4731d725abf98dc556c752071bc84b7ecd76d734f26373</originalsourceid><addsrcrecordid>eNp90E1LAzEQgOEgCtbqzR8Q8Khr8zWb3WMt9QMqilbwFrJJdrulzdYkRfbf29KePc0cHmbgReiakntKAUaMMDYqoZREwgkaUMh5BlTI091OmMgo49_n6CLGJSGMAi0GaPzRVV3C86CXzqQu9Ph9pb1vfYMfdHQWdx6nhcNT70LT41ftdePWzif8mYJOrukv0VmtV9FdHecQfT1O55PnbPb29DIZzzLDmEhZaTUThJSuJnlhHNSgheTUSga6qsvCGoDcSGBE0soUopLOWJlbyUXNci75EN0c7m5C97N1Malltw1-91IxSYHznNC9ujsoE7oYg6vVJrRrHXpFidpHUvtI6hhpx28PfNF6q3_b__UfKb1lGQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2715336017</pqid></control><display><type>article</type><title>Robot Trajectory Planning Based on the Energy Management Strategy</title><source>Wiley-Blackwell Open Access Titles</source><source>EZB-FREE-00999 freely available EZB journals</source><source>Alma/SFX Local Collection</source><creator>Li, Mingkai</creator><contributor>Ding, Baiyuan ; Baiyuan Ding</contributor><creatorcontrib>Li, Mingkai ; Ding, Baiyuan ; Baiyuan Ding</creatorcontrib><description>With the increasing demand for automated production technology in national defense, industry, agriculture, and other fields, the status and role of mobile robots are becoming more and more pivotal. The intelligent technology of robots is becoming more and more demanding in terms of reliability, stability, efficiency, and adaptability, and the autonomous navigation technology of mobile robots is facing new challenges. This paper introduces the basic principle of traditional A∗ algorithm, points out the problems of this algorithm such as cumbersome calculation, large turning angle, and unsmooth derived trajectory planning, and proposes an improved A∗ algorithm. The improved algorithm introduces a two-way alternating search mechanism to improve the efficiency of the search path, and the improvement of the heuristic function solves the problem that the two-way alternating search A∗ algorithm takes more time when there are obstacles perpendicular to the path on the way to the encounter. Simulation experiments in different raster environments prove that compared with the traditional A∗ algorithm, genetic algorithm, and simulated annealing algorithm, the improved A∗ algorithm can significantly improve the search path speed, while overcoming the disadvantages of many path turning angles and large turning angles and is an efficient and feasible algorithm for raster map environments.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2022/9597075</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Algorithms ; Artificial intelligence ; Autonomous navigation ; Autonomous vehicles ; Code reuse ; Communication ; Defense industry ; Energy management ; Genetic algorithms ; Localization ; R&D ; Raster ; Research & development ; Robotics ; Robots ; Searching ; Sensors ; Simulated annealing ; Software ; Technology ; Trajectory planning</subject><ispartof>Mathematical problems in engineering, 2022-09, Vol.2022, p.1-11</ispartof><rights>Copyright © 2022 Mingkai Li.</rights><rights>Copyright © 2022 Mingkai Li. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c224t-9da24009ef068ce5f5a4731d725abf98dc556c752071bc84b7ecd76d734f26373</cites><orcidid>0000-0002-3869-9166</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Ding, Baiyuan</contributor><contributor>Baiyuan Ding</contributor><creatorcontrib>Li, Mingkai</creatorcontrib><title>Robot Trajectory Planning Based on the Energy Management Strategy</title><title>Mathematical problems in engineering</title><description>With the increasing demand for automated production technology in national defense, industry, agriculture, and other fields, the status and role of mobile robots are becoming more and more pivotal. The intelligent technology of robots is becoming more and more demanding in terms of reliability, stability, efficiency, and adaptability, and the autonomous navigation technology of mobile robots is facing new challenges. This paper introduces the basic principle of traditional A∗ algorithm, points out the problems of this algorithm such as cumbersome calculation, large turning angle, and unsmooth derived trajectory planning, and proposes an improved A∗ algorithm. The improved algorithm introduces a two-way alternating search mechanism to improve the efficiency of the search path, and the improvement of the heuristic function solves the problem that the two-way alternating search A∗ algorithm takes more time when there are obstacles perpendicular to the path on the way to the encounter. Simulation experiments in different raster environments prove that compared with the traditional A∗ algorithm, genetic algorithm, and simulated annealing algorithm, the improved A∗ algorithm can significantly improve the search path speed, while overcoming the disadvantages of many path turning angles and large turning angles and is an efficient and feasible algorithm for raster map environments.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Autonomous navigation</subject><subject>Autonomous vehicles</subject><subject>Code reuse</subject><subject>Communication</subject><subject>Defense industry</subject><subject>Energy management</subject><subject>Genetic algorithms</subject><subject>Localization</subject><subject>R&D</subject><subject>Raster</subject><subject>Research & development</subject><subject>Robotics</subject><subject>Robots</subject><subject>Searching</subject><subject>Sensors</subject><subject>Simulated annealing</subject><subject>Software</subject><subject>Technology</subject><subject>Trajectory planning</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp90E1LAzEQgOEgCtbqzR8Q8Khr8zWb3WMt9QMqilbwFrJJdrulzdYkRfbf29KePc0cHmbgReiakntKAUaMMDYqoZREwgkaUMh5BlTI091OmMgo49_n6CLGJSGMAi0GaPzRVV3C86CXzqQu9Ph9pb1vfYMfdHQWdx6nhcNT70LT41ftdePWzif8mYJOrukv0VmtV9FdHecQfT1O55PnbPb29DIZzzLDmEhZaTUThJSuJnlhHNSgheTUSga6qsvCGoDcSGBE0soUopLOWJlbyUXNci75EN0c7m5C97N1Malltw1-91IxSYHznNC9ujsoE7oYg6vVJrRrHXpFidpHUvtI6hhpx28PfNF6q3_b__UfKb1lGQ</recordid><startdate>20220910</startdate><enddate>20220910</enddate><creator>Li, Mingkai</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-3869-9166</orcidid></search><sort><creationdate>20220910</creationdate><title>Robot Trajectory Planning Based on the Energy Management Strategy</title><author>Li, Mingkai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c224t-9da24009ef068ce5f5a4731d725abf98dc556c752071bc84b7ecd76d734f26373</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Autonomous navigation</topic><topic>Autonomous vehicles</topic><topic>Code reuse</topic><topic>Communication</topic><topic>Defense industry</topic><topic>Energy management</topic><topic>Genetic algorithms</topic><topic>Localization</topic><topic>R&D</topic><topic>Raster</topic><topic>Research & development</topic><topic>Robotics</topic><topic>Robots</topic><topic>Searching</topic><topic>Sensors</topic><topic>Simulated annealing</topic><topic>Software</topic><topic>Technology</topic><topic>Trajectory planning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Mingkai</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</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>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Mingkai</au><au>Ding, Baiyuan</au><au>Baiyuan Ding</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Robot Trajectory Planning Based on the Energy Management Strategy</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2022-09-10</date><risdate>2022</risdate><volume>2022</volume><spage>1</spage><epage>11</epage><pages>1-11</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>With the increasing demand for automated production technology in national defense, industry, agriculture, and other fields, the status and role of mobile robots are becoming more and more pivotal. The intelligent technology of robots is becoming more and more demanding in terms of reliability, stability, efficiency, and adaptability, and the autonomous navigation technology of mobile robots is facing new challenges. This paper introduces the basic principle of traditional A∗ algorithm, points out the problems of this algorithm such as cumbersome calculation, large turning angle, and unsmooth derived trajectory planning, and proposes an improved A∗ algorithm. The improved algorithm introduces a two-way alternating search mechanism to improve the efficiency of the search path, and the improvement of the heuristic function solves the problem that the two-way alternating search A∗ algorithm takes more time when there are obstacles perpendicular to the path on the way to the encounter. Simulation experiments in different raster environments prove that compared with the traditional A∗ algorithm, genetic algorithm, and simulated annealing algorithm, the improved A∗ algorithm can significantly improve the search path speed, while overcoming the disadvantages of many path turning angles and large turning angles and is an efficient and feasible algorithm for raster map environments.</abstract><cop>New York</cop><pub>Hindawi</pub><doi>10.1155/2022/9597075</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-3869-9166</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1024-123X |
| ispartof | Mathematical problems in engineering, 2022-09, Vol.2022, p.1-11 |
| issn | 1024-123X 1563-5147 |
| language | eng |
| recordid | cdi_proquest_journals_2715336017 |
| source | Wiley-Blackwell Open Access Titles; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Algorithms Artificial intelligence Autonomous navigation Autonomous vehicles Code reuse Communication Defense industry Energy management Genetic algorithms Localization R&D Raster Research & development Robotics Robots Searching Sensors Simulated annealing Software Technology Trajectory planning |
| title | Robot Trajectory Planning Based on the Energy Management Strategy |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T23%3A01%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Robot%20Trajectory%20Planning%20Based%20on%20the%20Energy%20Management%20Strategy&rft.jtitle=Mathematical%20problems%20in%20engineering&rft.au=Li,%20Mingkai&rft.date=2022-09-10&rft.volume=2022&rft.spage=1&rft.epage=11&rft.pages=1-11&rft.issn=1024-123X&rft.eissn=1563-5147&rft_id=info:doi/10.1155/2022/9597075&rft_dat=%3Cproquest_cross%3E2715336017%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2715336017&rft_id=info:pmid/&rfr_iscdi=true |