Loading…

UAV path planning using artificial potential field method updated by optimal control theory

The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constr...

Full description

Saved in:
Bibliographic Details
Published in:International journal of systems science 2016-04, Vol.47 (6), p.1407-1420
Main Authors: Chen, Yong-bo, Luo, Guan-chen, Mei, Yue-song, Yu, Jian-qiao, Su, Xiao-long
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c480t-1de79fc159d499999702ac12be202af8afb7af6b29945e52b021f0f113927c703
cites cdi_FETCH-LOGICAL-c480t-1de79fc159d499999702ac12be202af8afb7af6b29945e52b021f0f113927c703
container_end_page 1420
container_issue 6
container_start_page 1407
container_title International journal of systems science
container_volume 47
creator Chen, Yong-bo
Luo, Guan-chen
Mei, Yue-song
Yu, Jian-qiao
Su, Xiao-long
description The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.
doi_str_mv 10.1080/00207721.2014.929191
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1080_00207721_2014_929191</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1793243620</sourcerecordid><originalsourceid>FETCH-LOGICAL-c480t-1de79fc159d499999702ac12be202af8afb7af6b29945e52b021f0f113927c703</originalsourceid><addsrcrecordid>eNp9UD1PwzAQtRBIlMI_YPDIknJ2kqaeUFXxJVVioSwMluPY1MiJg-0I5d8TK7Byw92T7r2nu4fQNYEVgQ3cAlCoKkpWFEixYpQRRk7QghTrIitzwk7RIlGyxDlHFyF8AkBZUlig98P2DfciHnFvRdeZ7gMPIXXho9FGGmFx76LqYkLaKNvgVsWja_DQNyKqBtcjdn007bSXroveWRyPyvnxEp1pYYO6-p1LdHi4f909ZfuXx-fddp_JYgMxI42qmJakZE3BUlVAhSS0VnQCeiN0XQm9riljRalKWgMlGjQhOaOVrCBfopvZt_fua1Ah8tYEqez0kHJD4KRiOS3yNU3UYqZK70LwSvPeT5f7kRPgKUv-lyVPWfI5y0l2N8tMp51vxbfztuFRjNZ57UUnTeD5vw4_cnl7Dw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1793243620</pqid></control><display><type>article</type><title>UAV path planning using artificial potential field method updated by optimal control theory</title><source>Taylor and Francis:Jisc Collections:Taylor and Francis Read and Publish Agreement 2024-2025:Science and Technology Collection (Reading list)</source><creator>Chen, Yong-bo ; Luo, Guan-chen ; Mei, Yue-song ; Yu, Jian-qiao ; Su, Xiao-long</creator><creatorcontrib>Chen, Yong-bo ; Luo, Guan-chen ; Mei, Yue-song ; Yu, Jian-qiao ; Su, Xiao-long</creatorcontrib><description>The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.</description><identifier>ISSN: 0020-7721</identifier><identifier>EISSN: 1464-5319</identifier><identifier>DOI: 10.1080/00207721.2014.929191</identifier><language>eng</language><publisher>Taylor &amp; Francis</publisher><subject>additional control force ; artificial potential field (APF) ; Computer simulation ; Constraints ; functional optimisation problem ; Mathematical models ; Optimal control ; Optimization ; Path planning ; Potential fields ; Unmanned aerial vehicles</subject><ispartof>International journal of systems science, 2016-04, Vol.47 (6), p.1407-1420</ispartof><rights>2014 Taylor &amp; Francis 2014</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c480t-1de79fc159d499999702ac12be202af8afb7af6b29945e52b021f0f113927c703</citedby><cites>FETCH-LOGICAL-c480t-1de79fc159d499999702ac12be202af8afb7af6b29945e52b021f0f113927c703</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><creatorcontrib>Chen, Yong-bo</creatorcontrib><creatorcontrib>Luo, Guan-chen</creatorcontrib><creatorcontrib>Mei, Yue-song</creatorcontrib><creatorcontrib>Yu, Jian-qiao</creatorcontrib><creatorcontrib>Su, Xiao-long</creatorcontrib><title>UAV path planning using artificial potential field method updated by optimal control theory</title><title>International journal of systems science</title><description>The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.</description><subject>additional control force</subject><subject>artificial potential field (APF)</subject><subject>Computer simulation</subject><subject>Constraints</subject><subject>functional optimisation problem</subject><subject>Mathematical models</subject><subject>Optimal control</subject><subject>Optimization</subject><subject>Path planning</subject><subject>Potential fields</subject><subject>Unmanned aerial vehicles</subject><issn>0020-7721</issn><issn>1464-5319</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9UD1PwzAQtRBIlMI_YPDIknJ2kqaeUFXxJVVioSwMluPY1MiJg-0I5d8TK7Byw92T7r2nu4fQNYEVgQ3cAlCoKkpWFEixYpQRRk7QghTrIitzwk7RIlGyxDlHFyF8AkBZUlig98P2DfciHnFvRdeZ7gMPIXXho9FGGmFx76LqYkLaKNvgVsWja_DQNyKqBtcjdn007bSXroveWRyPyvnxEp1pYYO6-p1LdHi4f909ZfuXx-fddp_JYgMxI42qmJakZE3BUlVAhSS0VnQCeiN0XQm9riljRalKWgMlGjQhOaOVrCBfopvZt_fua1Ah8tYEqez0kHJD4KRiOS3yNU3UYqZK70LwSvPeT5f7kRPgKUv-lyVPWfI5y0l2N8tMp51vxbfztuFRjNZ57UUnTeD5vw4_cnl7Dw</recordid><startdate>20160425</startdate><enddate>20160425</enddate><creator>Chen, Yong-bo</creator><creator>Luo, Guan-chen</creator><creator>Mei, Yue-song</creator><creator>Yu, Jian-qiao</creator><creator>Su, Xiao-long</creator><general>Taylor &amp; Francis</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20160425</creationdate><title>UAV path planning using artificial potential field method updated by optimal control theory</title><author>Chen, Yong-bo ; Luo, Guan-chen ; Mei, Yue-song ; Yu, Jian-qiao ; Su, Xiao-long</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c480t-1de79fc159d499999702ac12be202af8afb7af6b29945e52b021f0f113927c703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>additional control force</topic><topic>artificial potential field (APF)</topic><topic>Computer simulation</topic><topic>Constraints</topic><topic>functional optimisation problem</topic><topic>Mathematical models</topic><topic>Optimal control</topic><topic>Optimization</topic><topic>Path planning</topic><topic>Potential fields</topic><topic>Unmanned aerial vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Yong-bo</creatorcontrib><creatorcontrib>Luo, Guan-chen</creatorcontrib><creatorcontrib>Mei, Yue-song</creatorcontrib><creatorcontrib>Yu, Jian-qiao</creatorcontrib><creatorcontrib>Su, Xiao-long</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology 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 systems science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Yong-bo</au><au>Luo, Guan-chen</au><au>Mei, Yue-song</au><au>Yu, Jian-qiao</au><au>Su, Xiao-long</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>UAV path planning using artificial potential field method updated by optimal control theory</atitle><jtitle>International journal of systems science</jtitle><date>2016-04-25</date><risdate>2016</risdate><volume>47</volume><issue>6</issue><spage>1407</spage><epage>1420</epage><pages>1407-1420</pages><issn>0020-7721</issn><eissn>1464-5319</eissn><abstract>The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.</abstract><pub>Taylor &amp; Francis</pub><doi>10.1080/00207721.2014.929191</doi><tpages>14</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0020-7721
ispartof International journal of systems science, 2016-04, Vol.47 (6), p.1407-1420
issn 0020-7721
1464-5319
language eng
recordid cdi_crossref_primary_10_1080_00207721_2014_929191
source Taylor and Francis:Jisc Collections:Taylor and Francis Read and Publish Agreement 2024-2025:Science and Technology Collection (Reading list)
subjects additional control force
artificial potential field (APF)
Computer simulation
Constraints
functional optimisation problem
Mathematical models
Optimal control
Optimization
Path planning
Potential fields
Unmanned aerial vehicles
title UAV path planning using artificial potential field method updated by optimal control theory
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T08%3A12%3A24IST&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=UAV%20path%20planning%20using%20artificial%20potential%20field%20method%20updated%20by%20optimal%20control%20theory&rft.jtitle=International%20journal%20of%20systems%20science&rft.au=Chen,%20Yong-bo&rft.date=2016-04-25&rft.volume=47&rft.issue=6&rft.spage=1407&rft.epage=1420&rft.pages=1407-1420&rft.issn=0020-7721&rft.eissn=1464-5319&rft_id=info:doi/10.1080/00207721.2014.929191&rft_dat=%3Cproquest_cross%3E1793243620%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c480t-1de79fc159d499999702ac12be202af8afb7af6b29945e52b021f0f113927c703%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1793243620&rft_id=info:pmid/&rfr_iscdi=true