FHQ-RRT: An Improved Path Planning Algorithm for Mobile Robots to Acquire High-Quality Paths Faster
The Rapidly-exploring Random Tree Star (RRT*) algorithm, widely utilized for path planning, faces challenges, such as slow acquisition of feasible paths and high path costs. To address this issue, this paper presents an improved algorithm based on RRT* that can obtain high-quality paths faster, term...
Uložené v:
| Vydané v: | Sensors (Basel, Switzerland) Ročník 25; číslo 7; s. 2189 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Switzerland
MDPI AG
30.03.2025
MDPI |
| Predmet: | |
| ISSN: | 1424-8220, 1424-8220 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | The Rapidly-exploring Random Tree Star (RRT*) algorithm, widely utilized for path planning, faces challenges, such as slow acquisition of feasible paths and high path costs. To address this issue, this paper presents an improved algorithm based on RRT* that can obtain high-quality paths faster, termed Faster High-Quality RRT*(FHQ-RRT*). The proposed algorithm enhances the exploration efficiency and path quality of mobile robots through three key innovations: First, a dynamic sparse sampling strategy that adaptively adjusts the sampling density according to the growth rate of the random tree, thereby increasing the algorithm’s growth speed while maintaining adaptability to complex environments. Second, a new node creation method that combines the bisection method, triangle inequality, and the concept of KeyPoints to reduce the cost of creating new nodes. Third, a focused rewiring strategy that restricts the rewiring operation to valuable regions, thereby improving rewiring efficiency. The performance of FHQ-RRT* was validated in four simulation maps and compared with other algorithms. In all validated maps, FHQ-RRT* consistently achieved the lowest path cost. Regarding time cost, FHQ-RRT* reduced the planning time by over 40% in the circular-obstacle map, 77% in the simple maze map, 56% in the complex maze map, and 50% in the narrow map. The simulation results show that FHQ-RRT* can rapidly generate high-quality paths faster than other algorithms. |
|---|---|
| AbstractList | The Rapidly-exploring Random Tree Star (RRT*) algorithm, widely utilized for path planning, faces challenges, such as slow acquisition of feasible paths and high path costs. To address this issue, this paper presents an improved algorithm based on RRT* that can obtain high-quality paths faster, termed Faster High-Quality RRT*(FHQ-RRT*). The proposed algorithm enhances the exploration efficiency and path quality of mobile robots through three key innovations: First, a dynamic sparse sampling strategy that adaptively adjusts the sampling density according to the growth rate of the random tree, thereby increasing the algorithm’s growth speed while maintaining adaptability to complex environments. Second, a new node creation method that combines the bisection method, triangle inequality, and the concept of KeyPoints to reduce the cost of creating new nodes. Third, a focused rewiring strategy that restricts the rewiring operation to valuable regions, thereby improving rewiring efficiency. The performance of FHQ-RRT* was validated in four simulation maps and compared with other algorithms. In all validated maps, FHQ-RRT* consistently achieved the lowest path cost. Regarding time cost, FHQ-RRT* reduced the planning time by over 40% in the circular-obstacle map, 77% in the simple maze map, 56% in the complex maze map, and 50% in the narrow map. The simulation results show that FHQ-RRT* can rapidly generate high-quality paths faster than other algorithms. The Rapidly-exploring Random Tree Star (RRT*) algorithm, widely utilized for path planning, faces challenges, such as slow acquisition of feasible paths and high path costs. To address this issue, this paper presents an improved algorithm based on RRT* that can obtain high-quality paths faster, termed Faster High-Quality RRT*(FHQ-RRT*). The proposed algorithm enhances the exploration efficiency and path quality of mobile robots through three key innovations: First, a dynamic sparse sampling strategy that adaptively adjusts the sampling density according to the growth rate of the random tree, thereby increasing the algorithm's growth speed while maintaining adaptability to complex environments. Second, a new node creation method that combines the bisection method, triangle inequality, and the concept of KeyPoints to reduce the cost of creating new nodes. Third, a focused rewiring strategy that restricts the rewiring operation to valuable regions, thereby improving rewiring efficiency. The performance of FHQ-RRT* was validated in four simulation maps and compared with other algorithms. In all validated maps, FHQ-RRT* consistently achieved the lowest path cost. Regarding time cost, FHQ-RRT* reduced the planning time by over 40% in the circular-obstacle map, 77% in the simple maze map, 56% in the complex maze map, and 50% in the narrow map. The simulation results show that FHQ-RRT* can rapidly generate high-quality paths faster than other algorithms.The Rapidly-exploring Random Tree Star (RRT*) algorithm, widely utilized for path planning, faces challenges, such as slow acquisition of feasible paths and high path costs. To address this issue, this paper presents an improved algorithm based on RRT* that can obtain high-quality paths faster, termed Faster High-Quality RRT*(FHQ-RRT*). The proposed algorithm enhances the exploration efficiency and path quality of mobile robots through three key innovations: First, a dynamic sparse sampling strategy that adaptively adjusts the sampling density according to the growth rate of the random tree, thereby increasing the algorithm's growth speed while maintaining adaptability to complex environments. Second, a new node creation method that combines the bisection method, triangle inequality, and the concept of KeyPoints to reduce the cost of creating new nodes. Third, a focused rewiring strategy that restricts the rewiring operation to valuable regions, thereby improving rewiring efficiency. The performance of FHQ-RRT* was validated in four simulation maps and compared with other algorithms. In all validated maps, FHQ-RRT* consistently achieved the lowest path cost. Regarding time cost, FHQ-RRT* reduced the planning time by over 40% in the circular-obstacle map, 77% in the simple maze map, 56% in the complex maze map, and 50% in the narrow map. The simulation results show that FHQ-RRT* can rapidly generate high-quality paths faster than other algorithms. |
| Audience | Academic |
| Author | Dong, Xingxiang Ran, Kemeng Liu, Guohui Fang, Can Wang, Yujun |
| AuthorAffiliation | College of Computer and Information Science, Southwest University, Chongqing 400715, China; d13538@email.swu.edu.cn (X.D.); wangyjun@swu.edu.cn (Y.W.); rkm666@email.swu.edu.cn (K.R.); liu914904@email.swu.edu.cn (G.L.) |
| AuthorAffiliation_xml | – name: College of Computer and Information Science, Southwest University, Chongqing 400715, China; d13538@email.swu.edu.cn (X.D.); wangyjun@swu.edu.cn (Y.W.); rkm666@email.swu.edu.cn (K.R.); liu914904@email.swu.edu.cn (G.L.) |
| Author_xml | – sequence: 1 givenname: Xingxiang orcidid: 0009-0003-9772-3431 surname: Dong fullname: Dong, Xingxiang – sequence: 2 givenname: Yujun surname: Wang fullname: Wang, Yujun – sequence: 3 givenname: Can surname: Fang fullname: Fang, Can – sequence: 4 givenname: Kemeng orcidid: 0009-0007-5687-593X surname: Ran fullname: Ran, Kemeng – sequence: 5 givenname: Guohui surname: Liu fullname: Liu, Guohui |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40218701$$D View this record in MEDLINE/PubMed |
| BookMark | eNplkstu3CAUQFGVqnm0i_5AhdRNu3DCa2zoJrKiTmekVE1G6RphwB5GtpkAjpS_L5NJoiQVCxCce-By7zE4GP1oAfiM0SmlAp1FMkMVwVy8A0eYEVZwQtDBi_UhOI5xgxChlPIP4JChTFcIHwE9X1wXq9XND1iPcDlsg7-zBl6ptIZXvRpHN3aw7jsfXFoPsPUB_vaN6y1c-canCJOHtb6dXLBw4bp1cT2p3qX7B0OEcxWTDR_B-1b10X56nE_A3_nPm4tFcfnn1_Kiviz0DONU6LYiqmqswIYhhpQ1lmDdmBI1RuOy5cTOBFaqwQ2pNKeMGsQbKpRWgpBS0xOw3HuNVxu5DW5Q4V565eTDhg-dVCE53VtZ8pIJTHSpG8K4YUoRViEtKoPJjCqUXed713ZqBmu0HVNQ_Svp65PRrWXn7yTGQmCGWTZ8ezQEfzvZmOTgorZ9_lXrpyhprhcraVnhjH59g278FMb8VzuKc8Ep31Gne6pTOQM3tj5frPMwdnA6d0Sb6yLrHYtyEiQHfHmZw_Pjn6qfgbM9oIOPMdhWapdUcn6XkuslRnLXX_K5v3LE9zcRT9L_2X87i8zs |
| CitedBy_id | crossref_primary_10_1007_s44443_025_00216_x |
| Cites_doi | 10.1109/TSMC.2021.3050960 10.1109/AICIT55386.2022.9930180 10.1109/SAUPEC/RobMech/PRASA52254.2021.9377014 10.1109/TCSI.2018.2882818 10.1109/CONIT55038.2022.9847869 10.1109/ICTech55460.2022.00037 10.1029/2020EA001498 10.1016/j.eswa.2022.119137 10.1109/TSSC.1968.300136 10.1109/NNICE61279.2024.10499018 10.1109/ITOEC53115.2022.9734323 10.1177/0278364911406761 10.3390/s24237812 10.1016/j.jksuci.2024.102146 10.1145/3478586.3478588 10.1117/12.3017521 10.1016/j.comcom.2019.10.014 10.1080/01691864.2020.1850349 10.1109/IRCE53649.2021.9570910 10.1016/j.engappai.2024.108246 10.1002/tee.23502 10.1080/0951192X.2017.1307526 10.1109/ICRA48506.2021.9561433 10.1016/j.eswa.2021.115457 10.1007/978-981-19-9195-0_25 10.1145/3477314.3507052 10.1109/ITAIC54216.2022.9836572 10.1109/CCDC52312.2021.9602203 10.1109/CVPR46437.2021.01350 10.1109/70.508439 10.1109/AICIT55386.2022.9930235 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2025 MDPI AG 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2025 by the authors. 2025 |
| Copyright_xml | – notice: COPYRIGHT 2025 MDPI AG – notice: 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2025 by the authors. 2025 |
| DBID | AAYXX CITATION NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.3390/s25072189 |
| DatabaseName | CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection (Proquest) ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | CrossRef MEDLINE - Academic Publicly Available Content Database PubMed |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_6864912c6cb248d4aa2470c97d1253a0 PMC11991414 A838102532 40218701 10_3390_s25072189 |
| Genre | Journal Article |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M ALIPV NPM 3V. 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c511t-cf72a7be91d4040aede21cbd60bdc16f82e591aab1b27c8343d08b39aca9226c3 |
| IEDL.DBID | 7X7 |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001465696600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1424-8220 |
| IngestDate | Mon Nov 10 04:32:54 EST 2025 Tue Nov 04 02:04:49 EST 2025 Fri Sep 05 17:39:05 EDT 2025 Tue Oct 07 07:27:42 EDT 2025 Tue Nov 04 18:15:16 EST 2025 Tue Apr 15 01:23:26 EDT 2025 Sat Nov 29 07:15:11 EST 2025 Tue Nov 18 22:24:16 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 7 |
| Keywords | optimal path planning sampling-based algorithms path planning rapidly explored random tree |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c511t-cf72a7be91d4040aede21cbd60bdc16f82e591aab1b27c8343d08b39aca9226c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0009-0003-9772-3431 0009-0007-5687-593X |
| OpenAccessLink | https://www.proquest.com/docview/3188898381?pq-origsite=%requestingapplication% |
| PMID | 40218701 |
| PQID | 3188898381 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_6864912c6cb248d4aa2470c97d1253a0 pubmedcentral_primary_oai_pubmedcentral_nih_gov_11991414 proquest_miscellaneous_3189463671 proquest_journals_3188898381 gale_infotracacademiconefile_A838102532 pubmed_primary_40218701 crossref_citationtrail_10_3390_s25072189 crossref_primary_10_3390_s25072189 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-03-30 |
| PublicationDateYYYYMMDD | 2025-03-30 |
| PublicationDate_xml | – month: 03 year: 2025 text: 2025-03-30 day: 30 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationTitleAlternate | Sensors (Basel) |
| PublicationYear | 2025 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Pan (ref_6) 2021; 69 Fan (ref_2) 2023; 213 Mohammed (ref_31) 2021; 35 He (ref_8) 2021; 52 ref_14 ref_33 ref_10 ref_30 Karaman (ref_22) 2011; 30 Aggarwal (ref_1) 2020; 149 ref_19 ref_18 ref_17 ref_16 ref_15 Liao (ref_32) 2021; 184 Darko (ref_11) 2022; 9 Kavraki (ref_13) 1996; 12 Koul (ref_5) 2019; 66 Li (ref_27) 2022; 17 ref_25 Wang (ref_24) 2024; 36 ref_23 Cui (ref_34) 2024; 133 ref_21 ref_20 Hart (ref_12) 1968; 4 Jin (ref_3) 2017; 30 ref_29 ref_28 ref_26 ref_9 ref_4 ref_7 |
| References_xml | – volume: 52 start-page: 2757 year: 2021 ident: ref_8 article-title: Asynchronous multithreading reinforcement-learning-based path planning and tracking for unmanned underwater vehicle publication-title: IEEE Trans. Syst. Man Cybern. Syst. doi: 10.1109/TSMC.2021.3050960 – ident: ref_20 doi: 10.1109/AICIT55386.2022.9930180 – ident: ref_10 doi: 10.1109/SAUPEC/RobMech/PRASA52254.2021.9377014 – volume: 66 start-page: 1544 year: 2019 ident: ref_5 article-title: Waypoint path planning with synaptic-dependent spike latency publication-title: IEEE Trans. Circuits Syst. I Regul. Pap. doi: 10.1109/TCSI.2018.2882818 – ident: ref_15 doi: 10.1109/CONIT55038.2022.9847869 – ident: ref_29 doi: 10.1109/ICTech55460.2022.00037 – volume: 9 start-page: e2020EA001498 year: 2022 ident: ref_11 article-title: A sampling-based path planning algorithm for improving observations in tropical cyclones publication-title: Earth Space Sci. doi: 10.1029/2020EA001498 – volume: 213 start-page: 119137 year: 2023 ident: ref_2 article-title: UAV trajectory planning based on bi-directional APF-RRT* algorithm with goal-biased publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.119137 – volume: 4 start-page: 100 year: 1968 ident: ref_12 article-title: A formal basis for the heuristic determination of minimum cost paths publication-title: IEEE Trans. Syst. Sci. Cybern. doi: 10.1109/TSSC.1968.300136 – ident: ref_17 doi: 10.1109/NNICE61279.2024.10499018 – ident: ref_14 doi: 10.1109/ITOEC53115.2022.9734323 – volume: 30 start-page: 846 year: 2011 ident: ref_22 article-title: Sampling-based algorithms for optimal motion planning publication-title: Int. J. Robot. Res. doi: 10.1177/0278364911406761 – ident: ref_23 doi: 10.3390/s24237812 – ident: ref_18 – ident: ref_21 – volume: 36 start-page: 102146 year: 2024 ident: ref_24 article-title: IBPF-RRT*: An improved path planning algorithm with Ultra-low number of iterations and stabilized optimal path quality publication-title: J. King Saud Univ. Comput. Inf. Sci. doi: 10.1016/j.jksuci.2024.102146 – ident: ref_25 doi: 10.1145/3478586.3478588 – ident: ref_33 doi: 10.1117/12.3017521 – volume: 149 start-page: 270 year: 2020 ident: ref_1 article-title: Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges publication-title: Comput. Commun. doi: 10.1016/j.comcom.2019.10.014 – volume: 35 start-page: 168 year: 2021 ident: ref_31 article-title: RRT* N: An efficient approach to path planning in 3D for Static and Dynamic Environments publication-title: Adv. Robot. doi: 10.1080/01691864.2020.1850349 – ident: ref_28 doi: 10.1109/IRCE53649.2021.9570910 – volume: 133 start-page: 108246 year: 2024 ident: ref_34 article-title: More Quickly-RRT*: Improved Quick Rapidly-exploring Random Tree Star algorithm based on optimized sampling point with better initial solution and convergence rate publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2024.108246 – volume: 17 start-page: 200 year: 2022 ident: ref_27 article-title: Fast-RRT*: An improved motion planner for mobile robot in two-dimensional space publication-title: IEEJ Trans. Electr. Electron. Eng. doi: 10.1002/tee.23502 – volume: 30 start-page: 1301 year: 2017 ident: ref_3 article-title: A novel path planning methodology for extrusion-based additive manufacturing of thin-walled parts publication-title: Int. J. Comput. Integr. Manuf. doi: 10.1080/0951192X.2017.1307526 – ident: ref_7 doi: 10.1109/ICRA48506.2021.9561433 – volume: 184 start-page: 115457 year: 2021 ident: ref_32 article-title: F-RRT*: An improved path planning algorithm with improved initial solution and convergence rate publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.115457 – ident: ref_26 doi: 10.1007/978-981-19-9195-0_25 – volume: 69 start-page: 1129 year: 2021 ident: ref_6 article-title: An improved artificial potential field method for path planning and formation control of the multi-UAV systems publication-title: IEEE Trans. Circuits Syst. II Express Briefs – ident: ref_9 doi: 10.1145/3477314.3507052 – ident: ref_19 doi: 10.1109/ITAIC54216.2022.9836572 – ident: ref_30 doi: 10.1109/CCDC52312.2021.9602203 – ident: ref_4 doi: 10.1109/CVPR46437.2021.01350 – volume: 12 start-page: 566 year: 1996 ident: ref_13 article-title: Probabilistic roadmaps for path planning in high-dimensional configuration spaces publication-title: IEEE Trans. Robot. Autom. doi: 10.1109/70.508439 – ident: ref_16 doi: 10.1109/AICIT55386.2022.9930235 |
| SSID | ssj0023338 |
| Score | 2.4573715 |
| Snippet | The Rapidly-exploring Random Tree Star (RRT*) algorithm, widely utilized for path planning, faces challenges, such as slow acquisition of feasible paths and... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 2189 |
| SubjectTerms | Autonomous vehicles Efficiency Genetic algorithms Methods optimal path planning Optimization path planning Planning rapidly explored random tree Robots sampling-based algorithms Sensors |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Pi9UwEA6yeNCD-NvqKlEEvZRt0myTeKviYw-6rI8V9laSSeo-WFt57Qr-9860faUPBS9em6EkmZnMN-3kG8ZeQ15TV8IiNQ50qkysU1cXIhV1dJnzmfXFcFH4kz49NRcX9mzR6otqwkZ64HHjjgpTKCskFOClMkE5J5XOwOqAoTl3Q7aeabtLpqZUK8fMa-QRyjGpP-ow0GOqQ73cF9FnIOn_8yhexKL9OslF4FndZXcmxMjLcab32I3Y3Ge3FzyCDxisTr6k6_X5O142fPxMEAM_Q3DHd02JeHn1rd1u-svvHGEq_9x6PA74uvVt3_G-5SVQSXDkVPeRjsQav4Y3dHzliEzhIfu6-nj-4SSduiekgCCqT6HW0mkfrQgKPdXFEKUAH4rMBxBFbWQ8tsI5L7zUYHKVh8z43DpwFjEZ5I_YQdM28QnjKqsRlnkEB4BBT0oj4DhEQaEMlIwmYW93u1rBRC1OHS6uKkwxSAHVrICEvZpFf4x8Gn8Tek-qmQWIAnt4gIZRTYZR_cswEvaGFFuRo-JkwE33DXBJRHlVlYbIzVBUJuxwp_tq8uCuwrPOoBGjTMJezsPoe_RDxTWxvR5kLBGuaZR5PJrKPGdF4ElnOGL2jGhvUfsjzeZy4PcWVI6mhHr6P7bhGbslqWUxXaPMDtlBv72Oz9lN-Nlvuu2LwWt-A-VdGq8 priority: 102 providerName: Directory of Open Access Journals |
| Title | FHQ-RRT: An Improved Path Planning Algorithm for Mobile Robots to Acquire High-Quality Paths Faster |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/40218701 https://www.proquest.com/docview/3188898381 https://www.proquest.com/docview/3189463671 https://pubmed.ncbi.nlm.nih.gov/PMC11991414 https://doaj.org/article/6864912c6cb248d4aa2470c97d1253a0 |
| Volume | 25 |
| WOSCitedRecordID | wos001465696600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central - New (Subscription) customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Health & Medical Collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELag5QAH3oVAWRmEBJeoseMmDheUol0ViV0tqyItp8ieOG2lkpRNisSF385Mkk13BeLCJYd4ZDma12dn_A1jryEsqCth5GsDsa-0K3xTRMIXhTOBsUFio_ai8Kd4NtPLZTLvD9zqvqxyHRPbQJ1XQGfkB2h7GifFBPP-8rtPXaPo72rfQuMm26W22WTn8fJ6wxXi_qtjEwpxa39QY7rHDQ91dN_IQS1V_58BeSMjbVdLbqSfyb3_Xfh9drcHnjztLOUBu-HKh-zOBh3hIwaT48_-YnHyjqcl704bXM7niBH5urcRTy9OcfLm7BtHtMunlcWowheVrZqaNxVPgSqLHafyEb_j5_jZzlDziSFOhsfsy2R88uHY75sw-IBYrPGhiKWJrUtErtDhjcudFGDzKLA5iKjQ0h0mwhgrrIxBhyrMA23DxIBJENpBuMd2yqp0TxlXQYHoziLGAMydUmoBh7kTlBFBSac99natlgx6hnJqlHGR4U6FNJgNGvTYq0H0sqPl-JvQEel2ECAm7fZFtTrNesfMIh2pREiIwEqlc2WMVHEASZwj9AtN4LE3ZBkZ-TsuBkx_bQE_iZizspQ0i8AxlB7bXxtA1geCOrvWvsdeDsPowvRfxpSuumplEuJti1HmSWdrw5oVYbA4wBG9ZYVbH7U9Up6ftTThgqralFDP_r2u5-y2pJ7GdM8y2Gc7zerKvWC34EdzXq9GrUO1Tz1iu0fj2Xwxas8t8Dn9NcZ384_T-dffwW4wSw |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELZKQQIOvB8LBQwCwSVq7Lixg4RQeKy26nZVVltpb8F2nLZSScomBfVP8RuZyau7AnHrget6ZNmbzzPfJONvCHlpgwy7Eoae0lZ6QrnM01nIPJY57WvjRyasLwqP5WSi5vNob4386u7CYFll5xNrR50WFt-RbwL2FEwKAeb9yXcPu0bh19WuhUYDix139hNStvLd9id4vq84H36efRx5bVcBzwK5qDybSa6lcRFLBSBYu9RxZk0a-ia1LMwUd1sR09oww6VVgQhSX5kg0lZHwFVsAPNeIpfBj0tM9uT8PMELIN9r1IuCIPI3S6AXkGBhB_mlmFe3BvgzACxFwNXqzKVwN7z5v_1Rt8iNlljTuDkJt8may--Q60tyi3eJHY6-eNPp7C2Nc9q8TXEp3QMOTLveTTQ-PoDNVIffKLB5ulsY8Jp0WpiiKmlV0Nhi5bSjWB7jNfojZ_UMJR1q1Jy4R_YvZJf3yXpe5O4hocLPgL0a4FAWuAHnitmt1DGM-FZwpwbkTQeDxLYK7NgI5DiBTAwRk_SIGZAXvelJIzvyN6MPiKXeAJXC6x-KxUHSOp4kVKGIGLehNVyoVGjNhfRtJFOgtoH2B-Q1IjFBfwaLsbq9lgFbQmWwJEYkATEO-IBsdIBLWkdXJudoG5Dn_TC4KPzupHNXnNY2EerSSbB50GC7X7NAjil9GFErqF_Z1OpIfnRYy6AzrNoTTDz697qekauj2e44GW9Pdh6Taxz7N-OdUn-DrFeLU_eEXLE_qqNy8bQ-zJR8vehD8Rv6jIj3 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLUJw4P1YKGAQCC7Rxo6bOEgILZRVV21XS9VK5RRsx2krlaRsUlD_Gr-OmTyWXYG49cB1bVn25puZb5LxNwAvbJBRV8LQU9pGnlQu83QWco9nTvva-LEJ64vC29Fkog4O4ukK_OzuwlBZZecTa0edFpbekQ8QewoXxQAzyNqyiOnG6N3pN486SNGX1q6dRgORLXf-A9O38u14A5_1SyFGH_c-bHpthwHPItGoPJtFQkfGxTyViGbtUie4NWnom9TyMFPCrcdca8ONiKwKZJD6ygSxtjpG3mIDXPcSrCIll6IHq9PxzvTzPN0LMPtrtIyCIPYHJZINTLeon_xCBKwbBfwZDhbi4XKt5kLwG934n_-2m3C9pdxs2NjILVhx-W24tiDEeAfsaPOTt7u794YNc9a8Z3EpmyI7Zl1XJzY8OcTDVEdfGfJ8tlMY9KdstzBFVbKqYENLNdWOUeGM1yiTnNcrlGykSY3iLuxfyCnvQS8vcvcAmPQz5LUG2ZVF1iCE4nY9dZy4gJXCqT687iCR2FabnVqEnCSYoxF6kjl6-vB8PvW0EST526T3hKv5BNIQr38oZodJ65KSUIUy5sKG1gipUqm1kJFv4yhF0htovw-vCJUJeTrcjNXthQ08EmmGJUNCFVLmQPRhrQNf0rrAMvmNvD48mw-j86IvUjp3xVk9JybFugjn3G9wPt-zJPYZ-Tiilixg6VDLI_nxUS2QzqmeT3L58N_7egpX0BaS7fFk6xFcFdTYmS6b-mvQq2Zn7jFctt-r43L2pLVsBl8u2ip-AX8Xk0Y |
| 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=FHQ-RRT%3A+An+Improved+Path+Planning+Algorithm+for+Mobile+Robots+to+Acquire+High-Quality+Paths+Faster&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Dong%2C+Xingxiang&rft.au=Wang%2C+Yujun&rft.au=Fang%2C+Can&rft.au=Kemeng+Ran&rft.date=2025-03-30&rft.pub=MDPI+AG&rft.eissn=1424-8220&rft.volume=25&rft.issue=7&rft.spage=2189&rft_id=info:doi/10.3390%2Fs25072189&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |