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...
Saved in:
Published in: | International journal of systems science 2016-04, Vol.47 (6), p.1407-1420 |
---|---|
Main Authors: | , , , , |
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 & 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 & 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 & 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 & 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 |