A novel unmanned aerial vehicle path planning approach: sand cat optimization algorithm incorporating learned behaviour
Unmanned aerial vehicle (UAV) path planning plays an important role in UAV flight, and an effective algorithm is needed to realize UAV path planning. The sand cat algorithm is characterized by simple parameter setting and easy implementation. However, the convergence speed is slow, easy to fall into...
Saved in:
| Published in | Measurement science & technology Vol. 35; no. 4; p. 46203 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
01.04.2024
|
| Online Access | Get full text |
| ISSN | 0957-0233 1361-6501 1361-6501 |
| DOI | 10.1088/1361-6501/ad1977 |
Cover
| Abstract | Unmanned aerial vehicle (UAV) path planning plays an important role in UAV flight, and an effective algorithm is needed to realize UAV path planning. The sand cat algorithm is characterized by simple parameter setting and easy implementation. However, the convergence speed is slow, easy to fall into the local optimum. In order to solve these problems, a novel sand cat algorithm incorporating learning behaviors (LSCSO) is proposed. LSCSO is inspired by the life habits and learning ability of sand cats and incorporates a new position update strategy into the basic Sand Cat Optimization Algorithm, which maintains the diversity of the population and improves the convergence ability during the optimization process. Finally, LSCSO is applied to the challenging UAV 3D path planning with cubic B-spline interpolation to generate a smooth path, and the proposed algorithm is compared with a variety of other competing algorithms. The experimental results show that LSCSO has excellent optimization-seeking ability and plans a safe and feasible path with minimal cost consideration among all the compared algorithms. |
|---|---|
| AbstractList | Unmanned aerial vehicle (UAV) path planning plays an important role in UAV flight, and an effective algorithm is needed to realize UAV path planning. The sand cat algorithm is characterized by simple parameter setting and easy implementation. However, the convergence speed is slow, easy to fall into the local optimum. In order to solve these problems, a novel sand cat algorithm incorporating learning behaviors (LSCSO) is proposed. LSCSO is inspired by the life habits and learning ability of sand cats and incorporates a new position update strategy into the basic Sand Cat Optimization Algorithm, which maintains the diversity of the population and improves the convergence ability during the optimization process. Finally, LSCSO is applied to the challenging UAV 3D path planning with cubic B-spline interpolation to generate a smooth path, and the proposed algorithm is compared with a variety of other competing algorithms. The experimental results show that LSCSO has excellent optimization-seeking ability and plans a safe and feasible path with minimal cost consideration among all the compared algorithms. |
| Author | Hu, Kun Mo, Yuanbin |
| Author_xml | – sequence: 1 givenname: Kun orcidid: 0009-0007-2296-6681 surname: Hu fullname: Hu, Kun – sequence: 2 givenname: Yuanbin surname: Mo fullname: Mo, Yuanbin |
| BookMark | eNqNkMFLwzAchYNMcJvePeYfqEvatWm8jaFOGHjRc_k1SddImoQ025h_va0TD4Lg6cF7fO_wzdDEOqsQuqXkjpKyXNCsoEmRE7oASTljF2j6U03QlPCcJSTNsis06_t3QggjnE_RcYWtOyiD97YDa5XEoIIGgw-q1cIo7CG22Jth03aHwfvgQLT3uAcrsYCInY-60x8QtbMYzM4FHdsOaytc8C4M_cAZBWE8r1ULB-324RpdNmB6dfOdc_T2-PC63iTbl6fn9WqbiLTMYwI8BUI55E1dQ1lKCWmac7asmZKFJIVUosyLrFjSXBG2lLzJWM2bBkQmFElVNkf0_Lu3Hk5HMKbyQXcQThUl1WiuGjVVo6bqbG5gyJkRwfV9UM1_kOIXInT8UhIDaPM3-AmgfofB |
| CitedBy_id | crossref_primary_10_3390_drones8050205 crossref_primary_10_1109_ACCESS_2024_3483457 crossref_primary_10_3390_wevj16030157 crossref_primary_10_1007_s10489_024_06124_3 crossref_primary_10_3390_met15020105 crossref_primary_10_1088_1402_4896_ad551b crossref_primary_10_62762_CJIF_2024_596648 |
| Cites_doi | 10.1016/j.petrol.2021.109633 10.1007/s00366-022-01604-x 10.1109/ACCESS.2020.2990153 10.1016/j.cja.2023.07.030 10.1016/j.procs.2019.01.151 10.1016/j.comnet.2017.05.021 10.3390/math10224350 10.1109/ACCESS.2021.3049892 10.1016/j.eswa.2022.119327 10.3390/app10041494 10.1016/j.eswa.2023.120946 10.3390/drones4040070 10.1016/j.eswa.2020.114541 10.1016/j.advengsoft.2023.103411 10.3390/buildings11120602 10.3390/axioms12070702 10.1016/j.agrformet.2023.109646 10.1109/CCDC52312.2021.9602445) 10.1007/s12652-023-04540-w 10.1109/4235.585893 10.1016/j.eswa.2021.114854 10.23919/ChiCC.2018.8482622) 10.1016/j.asoc.2021.107376 10.1016/j.advengsoft.2023.103423 10.1016/j.eswa.2022.119243 |
| ContentType | Journal Article |
| DBID | AAYXX CITATION ADTOC UNPAY |
| DOI | 10.1088/1361-6501/ad1977 |
| DatabaseName | CrossRef Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| Database_xml | – sequence: 1 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) Physics |
| EISSN | 1361-6501 |
| ExternalDocumentID | 10.1088/1361-6501/ad1977 10_1088_1361_6501_ad1977 |
| GroupedDBID | -DZ -~X .DC 1JI 4.4 5B3 5GY 5PX 5VS 5ZH 7.M 7.Q AAGCD AAGID AAHTB AAJIO AAJKP AATNI AAYXX ABCXL ABHWH ABJNI ABPEJ ABQJV ABVAM ACAFW ACBEA ACGFO ACGFS ACHIP ADEQX AEFHF AEINN AENEX AFYNE AKPSB ALMA_UNASSIGNED_HOLDINGS AOAED ASPBG ATQHT AVWKF AZFZN CBCFC CEBXE CITATION CJUJL CRLBU CS3 DU5 EBS EDWGO EMSAF EPQRW EQZZN F5P IHE IJHAN IOP IZVLO KOT LAP N5L N9A P2P PJBAE R4D RIN RNS RO9 ROL RPA SY9 TAE TN5 TWZ W28 WH7 XPP YQT ZMT ~02 .GJ 02O 1WK 29M 5ZI 6TJ 6TU 9BW AAGCF AALHV ACARI ACWPO ADTOC AERVB AETNG AFFNX AGQPQ AHSEE ARNYC BBWZM EJD FEDTE HVGLF H~9 JCGBZ M45 MVM NT- NT. OHT Q02 RKQ S3P T37 UNPAY ZCG ZY4 |
| ID | FETCH-LOGICAL-c285t-a92a019a5fbba88dda225974b7ed6d06dec85636415e074d9f37b9ffac3ce02e3 |
| IEDL.DBID | UNPAY |
| ISSN | 0957-0233 1361-6501 |
| IngestDate | Sun Sep 07 11:27:35 EDT 2025 Wed Oct 01 05:32:03 EDT 2025 Thu Apr 24 22:56:42 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| License | cc-by-nc-nd |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c285t-a92a019a5fbba88dda225974b7ed6d06dec85636415e074d9f37b9ffac3ce02e3 |
| ORCID | 0009-0007-2296-6681 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://doi.org/10.1088/1361-6501/ad1977 |
| ParticipantIDs | unpaywall_primary_10_1088_1361_6501_ad1977 crossref_primary_10_1088_1361_6501_ad1977 crossref_citationtrail_10_1088_1361_6501_ad1977 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2024-04-01 |
| PublicationDateYYYYMMDD | 2024-04-01 |
| PublicationDate_xml | – month: 04 year: 2024 text: 2024-04-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Measurement science & technology |
| PublicationYear | 2024 |
| References | Wu (mstad1977bib9) 2020; 8 Estrada (mstad1977bib5) 2019; 149 Asadzadeh (mstad1977bib2) 2022; 208 Chen (mstad1977bib13) 2021 Phung (mstad1977bib11) 2021; 107 Wu (mstad1977bib28) 2022; 10 Lyu (mstad1977bib1) 2023; 341 Moshref-Javadi (mstad1977bib4) 2021; 177 Hammad (mstad1977bib3) 2021; 11 Liu (mstad1977bib14) 2023; 233 Pan (mstad1977bib12) 2021; 9 Tian (mstad1977bib16) 2018 Seyyedabbasi (mstad1977bib19) 2023; 39 Wang (mstad1977bib22) 2021; 170 Montes-Romero (mstad1977bib7) 2020; 10 Yang (mstad1977bib6) 2020; 4 Erdelj (mstad1977bib8) 2017; 124 Seyyedabbasi (mstad1977bib21) 2023; 178 Wolpert (mstad1977bib18) 1997; 1 Zhang (mstad1977bib15) 2023; 215 Rajmohan (mstad1977bib24) 2023; 14 Sheng (mstad1977bib10) 2023; 36 Yu (mstad1977bib17) 2023; 215 Kennedy (mstad1977bib26) 1995; vol 4 Chen (mstad1977bib23) 2023; 12 Dorigo (mstad1977bib27) 1999; vol 2 Kiani (mstad1977bib20) 2023; 178 Tizhoosh (mstad1977bib25) 2005; vol 1 |
| References_xml | – volume: 208 year: 2022 ident: mstad1977bib2 article-title: UAV-based remote sensing for the petroleum industry and environmental monitoring: state-of-the-art and perspectives publication-title: J. Pet. Sci. Eng. doi: 10.1016/j.petrol.2021.109633 – volume: 39 start-page: 2627 year: 2023 ident: mstad1977bib19 article-title: Sand cat swarm optimization: a nature-inspired algorithm to solve global optimization problems publication-title: Eng. Comput. doi: 10.1007/s00366-022-01604-x – volume: 8 start-page: 85431 year: 2020 ident: mstad1977bib9 article-title: Bi-directional adaptive A* algorithm toward optimal path planning for large-scale UAV under multi-constraints publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2990153 – volume: 36 start-page: 249 year: 2023 ident: mstad1977bib10 article-title: New multi-UAV formation keeping method based on improved artificial potential field publication-title: Chin. J. Aeronaut. doi: 10.1016/j.cja.2023.07.030 – volume: 149 start-page: 375 year: 2019 ident: mstad1977bib5 article-title: The uses of unmanned aerial vehicles –UAV’s- (or drones) in social logistic: natural disasters response and humanitarian relief aid publication-title: Proc. Comput. Sci. doi: 10.1016/j.procs.2019.01.151 – volume: 124 start-page: 72 year: 2017 ident: mstad1977bib8 article-title: Wireless sensor networks and multi-UAV systems for natural disaster management publication-title: Comput. Netw. doi: 10.1016/j.comnet.2017.05.021 – volume: 10 start-page: 4350 year: 2022 ident: mstad1977bib28 article-title: Modified sand cat swarm optimization algorithm for solving constrained engineering optimization problems publication-title: Mathematics doi: 10.3390/math10224350 – volume: 9 start-page: 7994 year: 2021 ident: mstad1977bib12 article-title: A deep learning trained by genetic algorithm to improve the efficiency of path planning for data collection with multi-UAV publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3049892 – volume: 215 year: 2023 ident: mstad1977bib17 article-title: A hybrid algorithm based on grey wolf optimizer and differential evolution for UAV path planning publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.119327 – volume: 10 start-page: 1494 year: 2020 ident: mstad1977bib7 article-title: Director tools for autonomous media production with a team of drones publication-title: Appl. Sci. doi: 10.3390/app10041494 – volume: 233 year: 2023 ident: mstad1977bib14 article-title: Agricultural UAV trajectory planning by incorporating multi-mechanism improved grey wolf optimization algorithm publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2023.120946 – volume: 4 start-page: 70 year: 2020 ident: mstad1977bib6 article-title: Developing an introductory UAV/drone mapping training program for seagrass monitoring and research publication-title: Drones doi: 10.3390/drones4040070 – volume: 170 year: 2021 ident: mstad1977bib22 article-title: Kinematic constrained bi-directional RRT with efficient branch pruning for robot path planning publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2020.114541 – volume: 178 year: 2023 ident: mstad1977bib21 article-title: A reinforcement learning-based metaheuristic algorithm for solving global optimization problems publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2023.103411 – volume: 11 start-page: 602 year: 2021 ident: mstad1977bib3 article-title: The use of unmanned aerial vehicles for dynamic site layout planning in large-scale construction projects publication-title: Buildings doi: 10.3390/buildings11120602 – volume: 12 start-page: 702 year: 2023 ident: mstad1977bib23 article-title: UAV path planning based on an improved chimp optimization algorithm publication-title: Axioms doi: 10.3390/axioms12070702 – volume: 341 year: 2023 ident: mstad1977bib1 article-title: UAV time-series imagery with novel machine learning to estimate heading dates of rice accessions for breeding publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2023.109646 – start-page: pp 535 year: 2021 ident: mstad1977bib13 article-title: Research on improved potential field ant colony algorithm for UAV path planning doi: 10.1109/CCDC52312.2021.9602445) – volume: 14 start-page: 4289 year: 2023 ident: mstad1977bib24 article-title: Improved symbiotic organisms search for path planning of unmanned combat aerial vehicles publication-title: J. Ambient Intell. Humaniz. Comput. doi: 10.1007/s12652-023-04540-w – volume: 1 start-page: 67 year: 1997 ident: mstad1977bib18 article-title: No free lunch theorems for optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.585893 – volume: 177 year: 2021 ident: mstad1977bib4 article-title: Applications and research avenues for drone-based models in logistics: a classification and review publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.114854 – volume: vol 2 start-page: pp 1470 year: 1999 ident: mstad1977bib27 article-title: Ant colony optimization: a new meta-heuristic – volume: vol 4 start-page: pp 1942 year: 1995 ident: mstad1977bib26 article-title: Particle swarm optimization – volume: vol 1 start-page: pp 695 year: 2005 ident: mstad1977bib25 article-title: Opposition-based learning: a new scheme for machine intelligence – start-page: pp 10055 year: 2018 ident: mstad1977bib16 article-title: Real-time dynamic track planning of multi-UAV formation based on improved artificial bee colony algorithm doi: 10.23919/ChiCC.2018.8482622) – volume: 107 year: 2021 ident: mstad1977bib11 article-title: Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2021.107376 – volume: 178 year: 2023 ident: mstad1977bib20 article-title: Pscso: enhanced sand cat swarm optimization inspired by the political system to solve complex problems publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2023.103423 – volume: 215 year: 2023 ident: mstad1977bib15 article-title: A novel UAV path planning approach: heuristic crossing search and rescue optimization algorithm publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.119243 |
| SSID | ssj0007099 |
| Score | 2.4999342 |
| Snippet | Unmanned aerial vehicle (UAV) path planning plays an important role in UAV flight, and an effective algorithm is needed to realize UAV path planning. The sand... |
| SourceID | unpaywall crossref |
| SourceType | Open Access Repository Enrichment Source Index Database |
| StartPage | 46203 |
| Title | A novel unmanned aerial vehicle path planning approach: sand cat optimization algorithm incorporating learned behaviour |
| URI | https://doi.org/10.1088/1361-6501/ad1977 |
| UnpaywallVersion | publishedVersion |
| Volume | 35 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIOP databaseName: IOP Electronic Journals customDbUrl: eissn: 1361-6501 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0007099 issn: 0957-0233 databaseCode: IOP dateStart: 19900101 isFulltext: true titleUrlDefault: https://iopscience.iop.org/ providerName: IOP Publishing |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEA66Ip58i4pKDh5UiPaZTb0toqjg4-CCnsrkURW73WW3VfTXO2mjqIiP-2Qok8nMN03mG0I2wygO0C0ipjxPsUhIzpIs8hgERvtCKF97tjn57Jwfd6PT6_ja_e-wvTCf7u-xOPND7jNEEf4eaB-xyjiZ4DGi7haZ6J5fdm4aKr02w9QTNi1Wjbi7kfxOxacMNFUVA3h-gjz_kFaOZhqOo1HNRmhfkzzsVqXcVS9fuBr_8sWzZNphS9ppnGGOjJlinkzWbzzVaJ7MuXM8oluObHp7gTx1aNF_NDmtih7YmEuh9kn6aO6sFmpHFtOBG21E3yjI9-kICk0VlLSPQafnujkp5Lf94X1516OW9aEhSbbr6uEUqNyxAlTDRdI9Orw6OGZuGANTgYhLBkkACAchzqQEIbQGjARYjMi20Vx7XBslYh5yBAQGYYlOsrAtkywDFSrjBSZcIq2iX5hlQmUAEPgqaltqHIQsAFiUxVkQG45BQCYrZO9tg1LlmMrtwIw8rW_MhUitjVNr47Sx8QrZfl8xaFg6fpDded_zX4VX_yO8RlrlsDLriFJKuUHGTy4uN5ybvgK3zOF8 |
| linkProvider | Unpaywall |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEA66Ip58i4pKDh5UiPaZTb0tooigeHBBT2XyqIrd7rLbuuivd9JmRUV83CdDmUxmvmky3xCyG0ZxgG4RMeV5ikVCcpZkkccgMNoXQvnas83Jl1f8vBtd3Ma37n-H7YX5dH-PxZkfcp8hivCPQPuIVabJDI8RdbfITPfqunPXUOm1GaaesGmxasTdjeR3Kj5loLmqGMDLGPL8Q1o5W2g4jkY1G6F9TfJ0WJXyUL1-4Wr8yxcvknmHLWmncYYlMmWKZTJbv_FUo2Wy5M7xiO45sun9FTLu0KL_bHJaFT2wMZdC7ZP02TxYLdSOLKYDN9qITijIj-kICk0VlLSPQafnujkp5Pf94WP50KOW9aEhSbbr6uEUqNyxAlTDVdI9O705OWduGANTgYhLBkkACAchzqQEIbQGjARYjMi20Vx7XBslYh5yBAQGYYlOsrAtkywDFSrjBSZcI62iX5h1QmUAEPgqaltqHIQsAFiUxVkQG45BQCYb5GiyQalyTOV2YEae1jfmQqTWxqm1cdrYeIPsv68YNCwdP8gevO_5r8Kb_xHeIq1yWJltRCml3HEO-gZ0HuBz |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+novel+unmanned+aerial+vehicle+path+planning+approach%3A+sand+cat+optimization+algorithm+incorporating+learned+behaviour&rft.jtitle=Measurement+science+%26+technology&rft.au=Hu%2C+Kun&rft.au=Mo%2C+Yuanbin&rft.date=2024-04-01&rft.issn=0957-0233&rft.eissn=1361-6501&rft.volume=35&rft.issue=4&rft.spage=46203&rft_id=info:doi/10.1088%2F1361-6501%2Fad1977&rft.externalDBID=n%2Fa&rft.externalDocID=10_1088_1361_6501_ad1977 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-0233&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-0233&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-0233&client=summon |