Real-Time On-the-Fly Motion Planning for Urban Air Mobility via Updating Tree Data of Sampling-Based Algorithms Using Neural Network Inference

In this study, we consider the problem of motion planning for urban air mobility applications to generate a minimal snap trajectory and trajectory that cost minimal time to reach a goal location in the presence of dynamic geo-fences and uncertainties in the urban airspace. We have developed two sepa...

Full description

Saved in:

Bibliographic Details
Published in:	Aerospace Vol. 11; no. 1; p. 99
Main Authors:	Lou, Junlin, Yuksek, Burak, Inalhan, Gokhan, Tsourdos, Antonios
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.01.2024
Subjects:	Aircraft Algorithms Automation Data mining Efficiency Flexibility generative adversarial imitation learning Geofences Kinematics Machine learning Motion planning Neural networks Optimization Planning Real time Recurrent neural networks reinforcement learning Trajectory optimization Urban air Urban air mobility Vehicles
ISSN:	2226-4310, 2226-4310
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	In this study, we consider the problem of motion planning for urban air mobility applications to generate a minimal snap trajectory and trajectory that cost minimal time to reach a goal location in the presence of dynamic geo-fences and uncertainties in the urban airspace. We have developed two separate approaches for this problem because designing an algorithm individually for each objective yields better performance. The first approach that we propose is a decoupled method that includes designing a policy network based on a recurrent neural network for a reinforcement learning algorithm, and then combining an online trajectory generation algorithm to obtain the minimal snap trajectory for the vehicle. Additionally, in the second approach, we propose a coupled method using a generative adversarial imitation learning algorithm for training a recurrent-neural-network-based policy network and generating the time-optimized trajectory. The simulation results show that our approaches have a short computation time when compared to other algorithms with similar performance while guaranteeing sufficient exploration of the environment. In urban air mobility operations, our approaches are able to provide real-time on-the-fly motion re-planning for vehicles, and the re-planned trajectories maintain continuity for the executed trajectory. To the best of our knowledge, we propose one of the first approaches enabling one to perform an on-the-fly update of the final landing position and to optimize the path and trajectory in real-time while keeping explorations in the environment.
AbstractList	In this study, we consider the problem of motion planning for urban air mobility applications to generate a minimal snap trajectory and trajectory that cost minimal time to reach a goal location in the presence of dynamic geo-fences and uncertainties in the urban airspace. We have developed two separate approaches for this problem because designing an algorithm individually for each objective yields better performance. The first approach that we propose is a decoupled method that includes designing a policy network based on a recurrent neural network for a reinforcement learning algorithm, and then combining an online trajectory generation algorithm to obtain the minimal snap trajectory for the vehicle. Additionally, in the second approach, we propose a coupled method using a generative adversarial imitation learning algorithm for training a recurrent-neural-network-based policy network and generating the time-optimized trajectory. The simulation results show that our approaches have a short computation time when compared to other algorithms with similar performance while guaranteeing sufficient exploration of the environment. In urban air mobility operations, our approaches are able to provide real-time on-the-fly motion re-planning for vehicles, and the re-planned trajectories maintain continuity for the executed trajectory. To the best of our knowledge, we propose one of the first approaches enabling one to perform an on-the-fly update of the final landing position and to optimize the path and trajectory in real-time while keeping explorations in the environment.
Audience	Academic
Author	Inalhan, Gokhan Tsourdos, Antonios Lou, Junlin Yuksek, Burak
Author_xml	– sequence: 1 givenname: Junlin orcidid: 0000-0002-8596-7979 surname: Lou fullname: Lou, Junlin – sequence: 2 givenname: Burak orcidid: 0000-0001-9991-0618 surname: Yuksek fullname: Yuksek, Burak – sequence: 3 givenname: Gokhan orcidid: 0000-0002-4490-8358 surname: Inalhan fullname: Inalhan, Gokhan – sequence: 4 givenname: Antonios orcidid: 0000-0002-3966-7633 surname: Tsourdos fullname: Tsourdos, Antonios
BookMark	eNp9UV1vEzEQPKEiUUp_AG-WeL5in-_DfgyFQqRCESTPp7W9Th18dvA5RfkT_GYcghACCfthV-OZ8a7maXUWYsCqes7oFeeSvgRMcd6BRsYoo1TKR9V50zR93XJGz_7on1SX87yl5UjGBe3Oq--fEHy9chOSu1Dne6xv_IG8j9nFQD56CMGFDbExkXVSEMjCpfKqnHf5QB4ckPXOQD5yVgmRvIYMJFryGaadL2j9CmY0ZOE3Mbl8P81kPR_JH3CfwJeSv8X0hSyDxYRB47PqsQU_4-WvelGtb96srt_Vt3dvl9eL21q3Dcu1soIZZLKjnaadlVrKoew0yNb0wJQArlppBgChbK-t5LwTwPpOKS4M54JfVMuTr4mwHXfJTZAOYwQ3_gRi2oyQstMeR6tZUaPSstVtqwboDW-p6iRDxQdtiteLk9cuxa97nPO4jfsUyvhjI5noKO_6prCuTqwNFFMXbMwJdLkGJ6dLntYVfDEIKsoqAy8CdhLoEu6c0P4ek9HxGPv4T-xFM_yl0S7DMcvymfP_Uf4A_D-2qA
CitedBy_id	crossref_primary_10_3390_aerospace11070598
Cites_doi	10.1016/j.paerosci.2021.100726 10.1109/DASC.2016.7778009 10.1109/IROS.2014.6942976 10.1007/s12532-020-00179-2 10.1109/ICUAS51884.2021.9476861 10.1109/LRA.2019.2931199 10.1017/CBO9780511546877 10.1016/j.asoc.2022.109660 10.1016/j.paerosci.2015.01.001 10.1162/neco.1997.9.8.1735 10.1109/ROBIO.2015.7419013 10.1109/ICRA.2013.6631299 10.2514/6.2020-1483 10.2514/6.2023-0786 10.2514/6.2021-1754 10.2514/6.2016-1374 10.1177/0278364915614386 10.1109/ICRA.2011.5980409 10.1177/0278364911406761 10.3115/v1/D14-1179 10.1109/DASC52595.2021.9594424 10.1177/0278364919890396 10.1109/IROS45743.2020.9341794 10.2514/6.2020-2919 10.1177/0278364914558129
ContentType	Journal Article
Copyright	COPYRIGHT 2024 MDPI AG 2024 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.
Copyright_xml	– notice: COPYRIGHT 2024 MDPI AG – notice: 2024 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.
DBID	AAYXX CITATION 7TB 7TG 8FD 8FE 8FG ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ BHPHI BKSAR CCPQU DWQXO FR3 H8D HCIFZ KL. L7M P5Z P62 PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS DOA
DOI	10.3390/aerospace11010099
DatabaseName	CrossRef Mechanical & Transportation Engineering Abstracts Meteorological & Geoastrophysical Abstracts Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC ProQuest Central ProQuest Technology Collection Natural Science Collection Earth, Atmospheric & Aquatic Science Collection ProQuest One ProQuest Central Korea Engineering Research Database Aerospace Database SciTech Premium Collection Meteorological & Geoastrophysical Abstracts - Academic Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China Earth, Atmospheric & Aquatic Science Collection ProQuest Central ProQuest One Applied & Life Sciences Aerospace Database Meteorological & Geoastrophysical Abstracts Natural Science Collection ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Collection ProQuest One Academic Eastern Edition Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic Meteorological & Geoastrophysical Abstracts - Academic ProQuest One Academic (New)
DatabaseTitleList	CrossRef Publicly Available Content Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2226-4310
ExternalDocumentID	oai_doaj_org_article_fc1358ebc94c44b7a6d340b591eb37cd A780870073 10_3390_aerospace11010099
GroupedDBID	-~X 5VS 85S 8FE 8FG 8FH AADQD AAFWJ AAYXX ABPPZ ACIWK ADBBV AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ BHPHI BKSAR BQN CCPQU CITATION GROUPED_DOAJ HCIFZ IAO ITC KQ8 LK5 M7R MODMG M~E OK1 P62 PCBAR PHGZM PHGZT PIMPY PQGLB PROAC 7TB 7TG 8FD ABUWG AZQEC DWQXO FR3 H8D KL. L7M PKEHL PQEST PQQKQ PQUKI PRINS PUEGO
ID	FETCH-LOGICAL-c421t-bf81de19505c05f9c997009794d6a1b8a3b49d7aa8bf6cf93358a165bb38d3383
IEDL.DBID	DOA
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001149215800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2226-4310
IngestDate	Mon Nov 10 04:33:05 EST 2025 Thu Sep 11 05:41:52 EDT 2025 Tue Nov 04 18:39:31 EST 2025 Tue Nov 18 22:26:27 EST 2025 Sat Nov 29 07:10:30 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	1
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c421t-bf81de19505c05f9c997009794d6a1b8a3b49d7aa8bf6cf93358a165bb38d3383
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-4490-8358 0000-0001-9991-0618 0000-0002-3966-7633 0000-0002-8596-7979
OpenAccessLink	https://doaj.org/article/fc1358ebc94c44b7a6d340b591eb37cd
PQID	2918503562
PQPubID	2032442
ParticipantIDs	doaj_primary_oai_doaj_org_article_fc1358ebc94c44b7a6d340b591eb37cd proquest_journals_2918503562 gale_infotracacademiconefile_A780870073 crossref_primary_10_3390_aerospace11010099 crossref_citationtrail_10_3390_aerospace11010099
PublicationCentury	2000
PublicationDate	2024-01-01
PublicationDateYYYYMMDD	2024-01-01
PublicationDate_xml	– month: 01 year: 2024 text: 2024-01-01 day: 01
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	Aerospace
PublicationYear	2024
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	Gammell (ref_37) 2020; 39 ref_14 ref_36 Hochreiter (ref_32) 1997; 9 ref_35 ref_12 ref_34 ref_11 ref_33 ref_10 ref_30 Li (ref_42) 2016; 35 Zhang (ref_39) 2022; 130 ref_18 ref_16 Yu (ref_2) 2015; 74 ref_15 Wu (ref_38) 2019; 17 Stellato (ref_20) 2020; 12 Bry (ref_19) 2015; 34 Bauranov (ref_1) 2021; 125 Karaman (ref_13) 2011; 30 ref_25 ref_23 ref_22 ref_21 ref_43 Ng (ref_24) 2000; 1 ref_41 ref_40 Yuksek (ref_31) 2021; 18 ref_3 ref_29 ref_28 ref_27 ref_26 ref_8 Medsker (ref_9) 2001; 5 Chiang (ref_17) 2019; 4 ref_5 ref_4 ref_7 ref_6
References_xml	– ident: ref_28 – volume: 125 start-page: 100726 year: 2021 ident: ref_1 article-title: Designing airspace for urban air mobility: A review of concepts and approaches publication-title: Prog. Aerosp. Sci. doi: 10.1016/j.paerosci.2021.100726 – ident: ref_30 – ident: ref_3 – ident: ref_7 doi: 10.1109/DASC.2016.7778009 – ident: ref_34 – ident: ref_36 doi: 10.1109/IROS.2014.6942976 – ident: ref_11 – volume: 12 start-page: 637 year: 2020 ident: ref_20 article-title: OSQP: An operator splitting solver for quadratic programs publication-title: Math. Program. Comput. doi: 10.1007/s12532-020-00179-2 – ident: ref_25 doi: 10.1109/ICUAS51884.2021.9476861 – volume: 4 start-page: 4298 year: 2019 ident: ref_17 article-title: RL-RRT: Kinodynamic motion planning via learning reachability estimators from RL policies publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2019.2931199 – ident: ref_40 – ident: ref_8 doi: 10.1017/CBO9780511546877 – ident: ref_23 – volume: 130 start-page: 109660 year: 2022 ident: ref_39 article-title: A self-adaptive gradient-based particle swarm optimization algorithm with dynamic population topology publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2022.109660 – volume: 74 start-page: 152 year: 2015 ident: ref_2 article-title: Sense and avoid technologies with applications to unmanned aircraft systems: Review and prospects publication-title: Prog. Aerosp. Sci. doi: 10.1016/j.paerosci.2015.01.001 – volume: 9 start-page: 1735 year: 1997 ident: ref_32 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – ident: ref_33 doi: 10.1109/ROBIO.2015.7419013 – ident: ref_6 – ident: ref_29 – ident: ref_27 – ident: ref_16 doi: 10.1109/ICRA.2013.6631299 – volume: 17 start-page: 26 year: 2019 ident: ref_38 article-title: Hyperparameter optimization for machine learning models based on Bayesian optimization publication-title: J. Electron. Sci. Technol. – ident: ref_4 doi: 10.2514/6.2020-1483 – ident: ref_26 doi: 10.2514/6.2023-0786 – ident: ref_12 – ident: ref_10 – ident: ref_41 doi: 10.2514/6.2021-1754 – ident: ref_43 doi: 10.2514/6.2016-1374 – volume: 18 start-page: 739 year: 2021 ident: ref_31 article-title: Cooperative Planning for an Unmanned Combat Aerial Vehicle Fleet Using Reinforcement Learning publication-title: J. Aerosp. Inf. Syst. – volume: 35 start-page: 528 year: 2016 ident: ref_42 article-title: Asymptotically optimal sampling-based kinodynamic planning publication-title: Int. J. Robot. Res. doi: 10.1177/0278364915614386 – ident: ref_18 doi: 10.1109/ICRA.2011.5980409 – ident: ref_15 – volume: 30 start-page: 846 year: 2011 ident: ref_13 article-title: Sampling-based algorithms for optimal motion planning publication-title: Int. J. Robot. Res. doi: 10.1177/0278364911406761 – volume: 1 start-page: 2 year: 2000 ident: ref_24 article-title: Algorithms for inverse reinforcement learning publication-title: Icml – ident: ref_35 doi: 10.3115/v1/D14-1179 – ident: ref_14 doi: 10.1109/DASC52595.2021.9594424 – volume: 39 start-page: 543 year: 2020 ident: ref_37 article-title: Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search publication-title: Int. J. Robot. Res. doi: 10.1177/0278364919890396 – ident: ref_21 doi: 10.1109/IROS45743.2020.9341794 – ident: ref_22 – volume: 5 start-page: 64 year: 2001 ident: ref_9 article-title: Recurrent neural networks publication-title: Des. Appl. – ident: ref_5 doi: 10.2514/6.2020-2919 – volume: 34 start-page: 969 year: 2015 ident: ref_19 article-title: Aggressive flight of fixed-wing and quadrotor aircraft in dense indoor environments publication-title: Int. J. Robot. Res. doi: 10.1177/0278364914558129
SSID	ssj0000913805
Score	2.249965
Snippet	In this study, we consider the problem of motion planning for urban air mobility applications to generate a minimal snap trajectory and trajectory that cost...
SourceID	doaj proquest gale crossref
SourceType	Open Website Aggregation Database Enrichment Source Index Database
StartPage	99
SubjectTerms	Aircraft Algorithms Automation Data mining Efficiency Flexibility generative adversarial imitation learning Geofences Kinematics Machine learning Motion planning Neural networks Optimization Planning Real time Recurrent neural networks reinforcement learning Trajectory optimization Urban air Urban air mobility Vehicles
SummonAdditionalLinks	– databaseName: Publicly Available Content Database dbid: PIMPY link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3db9MwELeg44E98I1WGMgPSEhIVuM4ieMn1A0qJrFSwYrGU-SvlEolLWmYtH-Cv3l3iVNAiD3xGtvSWT7_7s65-x0hL6QUNvVpzDTnhiXWO2akiZnIVOkMmAzb8hR8fi-n0_z8XM1CefQ2pFX2mNgCdcf2jHnbAMIjt7b4Yj6KFdiZSIDxfr35zrCHFP5rDQ01bpI9JN6KBmRvdnI6-7J7c0EOzDxKu5-bAqL9kfZgiyA49WAFOXpLf5inlsX_X1jdGqDJ3f8r-j1yJziidNxpzn1yw1cPyP5v9IQPyc-P4EUyLBKhHyoGniKbrC7padv3h_bdjih4vXReG13R8bKG0Tbb9pJeLDWdb7B4Auac1d7TN7rRdF3STxqz2KsFOwIL6uh4tQDxmq_ftrTNX6DIFwKSTbsEdXrSlyQ-IvPJ27Pjdyz0b2A2iXnDTAnOsMc-s6mN0lJZpSTWjajEZZqbXAuTKCe1zk2Z2VIJkeaaZ6kxIncYOj8mg2pd-QNCFRLdy1KUBuI3k3OdS8OdcrA-kdrGQxL1x1bYQG6OPTZWBQQ5eNLFXyc9JK92SzYds8d1k49QF3YTkZS7_bCuF0W440VpOezAG6sSmyRG6syJJDKp4t4Iad2QvERNKhA6QDirQwUEbBFJuIqxzCOATwDdITnsNakImLItfinOk-uHn5LbMbhe3UPRIRk09Q__jNyyF81yWz8Pl-IKiLEfkQ priority: 102 providerName: ProQuest
Title	Real-Time On-the-Fly Motion Planning for Urban Air Mobility via Updating Tree Data of Sampling-Based Algorithms Using Neural Network Inference
URI	https://www.proquest.com/docview/2918503562 https://doaj.org/article/fc1358ebc94c44b7a6d340b591eb37cd
Volume	11
WOSCitedRecordID	wos001149215800001&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: 2226-4310 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913805 issn: 2226-4310 databaseCode: DOA dateStart: 20140101 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: 2226-4310 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913805 issn: 2226-4310 databaseCode: M~E dateStart: 20140101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2226-4310 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913805 issn: 2226-4310 databaseCode: P5Z dateStart: 20140301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 2226-4310 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913805 issn: 2226-4310 databaseCode: PCBAR dateStart: 20140301 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2226-4310 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913805 issn: 2226-4310 databaseCode: BENPR dateStart: 20140301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2226-4310 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913805 issn: 2226-4310 databaseCode: PIMPY dateStart: 20140301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQ4QCHivIQ25bKByQkJKtx7MT2cRd21UrsEpUuKlwiv1JWWrJVNq3UCz-B38zYyVaLEHDhkkNiS45nPPNNMvMNQq-EYDbzWUo0pYZw6x0xwqSE5apyBlyGjTwFn96L2UxeXKhiq9VXyAnr6IG7jTuuLGWZ9MYqbjk3QueO8cRkikIYKKwL1jcRaiuYijZYUSaTrPuNySCuP9YevA6EoR78HQ246BdHFPn6_2SVo6uZPEa7PUbEw25te-ier5-gR1vMgU_RjzMAeCTUb-APNQEQRybLWzyNLXnwphERBkCK543RNR4uGngaE2Fv8c1C4_lVqGuAMeeN9_idbjVeVfijDgnm9SUZgXNzeLi8XDWL9uu3NY6pBThQecDKZl3uOD7dVAs-Q_PJ-PztCelbKxDLU9oSUwFO9aEFbGaTrFJWKRFKOhR3uaZGama4ckJraarcVoqBDDTNM2OYdCGqfY526lXtXyCsAge9qFhlILQykmopDHXKwXwutE0HKNnsc2l73vHQ_mJZQvwRRFP-JpoBenM35aoj3fjb4FEQ3t3AwJcdb4AWlb0Wlf_SogF6HURfhlMNi7O6L06AVwz8WOVQyAQsG9jDATrcaEfZH_d1mSqAPQkDLLn_P1ZzgB6mgJ26Lz2HaKdtrv1L9MDetIt1c4Tuj8az4uwoajxcp9_HcC2yL_CkOJ0Wn38CKe8K6Q
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VFgk48EYECuyBh4S0qt_rPXBwWqJGTUNVEtTbsi-HSMEJjinKn-Cn8BuZcewAQvTWA1d7ba3tz_PN7M58Q8hzzkMTuzhgyvc1i4yzTHMdsDARudVAGabWKfgw4MNhenYmTrbIj7YWBtMqW5tYG2o7N7hGvhcIYBYvBLpuMiiP3OobxGfLN_0D-JgvgqD3drR_yJoWAsxEgV8xnYM_5rDVaWy8OBdGCI6lCyKyifJ1qkIdCcuVSnWemByi-zhVfhJrHaYWoze478vFF4ZdqnA3t2nZcYXspImI4L_aOdnvZqebVR1U2Uy9eL19GobC21MO2A7CXwc866M_9gcB1n0C_sUGNcX1bv1vL-c2udk40zRbo_8O2XLFXXLjN4nFe-T7KXjCDAtd6LuCgbfLerMVPa57F9G2YxMFz52OS60Kmk1LOFtnDK_o-VTR8QILQGDMqHSOHqhK0XlO3yvMxC8mrAtegKXZbALTqz59XtI6B4Oi5gnMbLhOsqf9tqzyPhlfyjt6QLaLeeEeEipQrJ_nYa4hBtWpr1KufSssXB9xZYIO8VpgSNMItGOfkJmEQA2xJP_CUoe83lyyWKuTXDS4i2jbDERh8frAvJzIxk7J3PjwBE4bEZko0lwlNow8HQvf6ZAb2yGvEKsSzR9MzqimigMeEYXEZMZTDygAiKNDdlusysYuLuUvoD66-PQzcu1wdDyQg_7w6DG5HoAruV742iXbVfnVPSFXzXk1XZZPm1-Qko-XjfWfbdxwJg
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFCE48EYNFNgDCAlpFdtrZ70HhBLSiKglRKVBvZl9OY0UnOCYovwJfhC_jlk_AgjRWw9c7bVlrz_PN7M78w3AM86ZjmwUUOn7iobaGqq4CijritQopAxd6hR8POLjcXx6KiY78KOphXFplY1NLA21WWq3Rt4JBDKLx5CuO2mdFjEZDF-vvlDXQcrttDbtNCqIHNrNNwzf1q9GA_zWz4NgeHDy5i2tOwxQHQZ-QVWK7pp1nVAj7UWp0EJwV9kgQtOVvoolU6EwXMpYpV2dYvAfxdLvRkqx2LjgDu97BXY5w6CnBbv9g_HkeLvC4xQ3Yy-qtlIZE15HWmQ-DIUtcq7vfLM_yLDsGfAvZijpbnjrf56o23CzdrJJr_or7sCOze7Cjd-kF-_B92P0kKkrgCHvM4peMB0uNuRd2dOINJ2cCHr0ZJormZHePMezZSbxhpzPJZmuXGEIjjnJrSUDWUiyTMkH6TL0sxnto3dgSG8xw-kozj6vSZmbQZwWCj7ZuEq-J6Om3PI-TC9lQh5AK1tmdg-IcCL-PGWpwthUxb6MufKNMHh9yKUO2uA1IEl0Ldzu-ocsEgzgHK6Sv3DVhpfbS1aVaslFg_sOeduBTnC8PLDMZ0ltv5JU-_gGVmkR6jBUXHYNCz0VCd8qxrVpwwuH28SZRXw4LevqDnxFJzCW9HjsITUgobRhv8FtUtvLdfILtA8vPv0UriGak6PR-PARXA_Qw6zWw_ahVeRf7WO4qs-L-Tp_Uv-NBD5dNq5_AmVXeP8
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=Real-Time+On-the-Fly+Motion+Planning+for+Urban+Air+Mobility+via+Updating+Tree+Data+of+Sampling-Based+Algorithms+Using+Neural+Network+Inference&rft.jtitle=Aerospace&rft.au=Junlin+Lou&rft.au=Burak+Yuksek&rft.au=Gokhan+Inalhan&rft.au=Antonios+Tsourdos&rft.date=2024-01-01&rft.pub=MDPI+AG&rft.eissn=2226-4310&rft.volume=11&rft.issue=1&rft.spage=99&rft_id=info:doi/10.3390%2Faerospace11010099&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_fc1358ebc94c44b7a6d340b591eb37cd
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2226-4310&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2226-4310&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2226-4310&client=summon