Aggressive and robust low-level control and trajectory tracking for quadrotors with deep reinforcement learning
Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level cont...
Saved in:
| Published in: | Neural computing & applications Vol. 37; no. 3; pp. 1223 - 1240 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Springer London
01.01.2025
Springer Nature B.V |
| Subjects: | |
| ISSN: | 0941-0643, 1433-3058 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level controllers with traditional model-based control schemes is challenging. This paper presents an aggressive and robust reinforcement learning (RL)-based low-level control policy for quadrotors. The policy maps the observed quadrotor state directly to motor thrust commands, without requiring the quadrotor dynamics. Additionally, a trajectory generation pipeline is developed to improve the accuracy of trajectory tracking tasks based on differential flatness. With the learned low-level control policy, extensive simulations and real-world experiments are implemented to validate the performance of the policy. The results indicate that our RL-based low-level control policy outperforms traditional proportional–integral–derivative (PID) control methods and related learning-based policies in terms of accuracy and robustness, particularly in environments with uncertain disturbances. Furthermore, the proposed RL-based control policy exhibits an aggressive response in trajectory tracking, even when the speed of the desired trajectory is increased to 6 m/s. Moreover, the learned policy demonstrates strong vibration suppression capabilities and enables the quadrotor to recover to a hovering state from random initial conditions with shorter response time. |
|---|---|
| AbstractList | Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level controllers with traditional model-based control schemes is challenging. This paper presents an aggressive and robust reinforcement learning (RL)-based low-level control policy for quadrotors. The policy maps the observed quadrotor state directly to motor thrust commands, without requiring the quadrotor dynamics. Additionally, a trajectory generation pipeline is developed to improve the accuracy of trajectory tracking tasks based on differential flatness. With the learned low-level control policy, extensive simulations and real-world experiments are implemented to validate the performance of the policy. The results indicate that our RL-based low-level control policy outperforms traditional proportional–integral–derivative (PID) control methods and related learning-based policies in terms of accuracy and robustness, particularly in environments with uncertain disturbances. Furthermore, the proposed RL-based control policy exhibits an aggressive response in trajectory tracking, even when the speed of the desired trajectory is increased to 6 m/s. Moreover, the learned policy demonstrates strong vibration suppression capabilities and enables the quadrotor to recover to a hovering state from random initial conditions with shorter response time. Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level controllers with traditional model-based control schemes is challenging. This paper presents an aggressive and robust reinforcement learning (RL)-based low-level control policy for quadrotors. The policy maps the observed quadrotor state directly to motor thrust commands, without requiring the quadrotor dynamics. Additionally, a trajectory generation pipeline is developed to improve the accuracy of trajectory tracking tasks based on differential flatness. With the learned low-level control policy, extensive simulations and real-world experiments are implemented to validate the performance of the policy. The results indicate that our RL-based low-level control policy outperforms traditional proportional–integral–derivative (PID) control methods and related learning-based policies in terms of accuracy and robustness, particularly in environments with uncertain disturbances. Furthermore, the proposed RL-based control policy exhibits an aggressive response in trajectory tracking, even when the speed of the desired trajectory is increased to 6 m/s. Moreover, the learned policy demonstrates strong vibration suppression capabilities and enables the quadrotor to recover to a hovering state from random initial conditions with shorter response time. |
| Author | Lou, Yunjiang Li, Yanjie Lin, Ke Chen, Shiyu |
| Author_xml | – sequence: 1 givenname: Shiyu surname: Chen fullname: Chen, Shiyu organization: Department of Control Science and Engineering, Harbin Institute of Technology Shenzhen, Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics – sequence: 2 givenname: Yanjie surname: Li fullname: Li, Yanjie email: autolyj@hit.edu.cn organization: Department of Control Science and Engineering, Harbin Institute of Technology Shenzhen, Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics – sequence: 3 givenname: Yunjiang surname: Lou fullname: Lou, Yunjiang organization: Department of Control Science and Engineering, Harbin Institute of Technology Shenzhen, Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics – sequence: 4 givenname: Ke surname: Lin fullname: Lin, Ke organization: Department of Control Science and Engineering, Harbin Institute of Technology Shenzhen, Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics |
| BookMark | eNp9kEtLAzEQx4NUsK1-AU8Bz6uT1-72WIovELzoOewms3XrNmmTbUu_vWlX8OZphvk_Bn4TMnLeISG3DO4ZQPEQARRnGXCZMcgLlckLMmZSiEyAKkdkDDOZ5FyKKzKJcQUAMi_VmPj5chkwxnaPtHKWBl_vYk87f8g63GNHjXd98N1Z7EO1QtP7cDyt5rt1S9r4QLe7ygaf7pEe2v6LWsQNDdi6JBpco0uFWAWX_Nfksqm6iDe_c0o-nx4_Fi_Z2_vz62L-lhlWKplxVRVoZK3qYlbkADk3BasZWtEoVs-EkRYMR8NrxBqF5Y1lZVXWtsECLGvElNwNvZvgtzuMvV75XXDppRZMzZQCxlVy8cFlgo8xYKM3oV1X4agZ6BNYPYDVCaw-g9UyhcQQisnslhj-qv9J_QDpVoBE |
| Cites_doi | 10.1109/TSMC.2018.2884725 10.1109/IJCNN.1992.227257 10.1109/LRA.2021.3131690 10.48550/arXiv.2010.02293 10.48550/arXiv.1509.02971 10.1007/978-3-319-90893-9_43 10.1109/TSMC.2022.3222857 10.1016/j.conengprac.2019.104222 10.1109/SECon.2012.6196930 10.1109/LARS/SBR/WRE.2018.00094 10.48550/arXiv.1709.03339 10.1109/TIV.2022.3229723 10.1109/TASE.2017.2651109 10.3182/20110828-6-IT-1002.02453 10.1177/0278364913495721 10.3303/CET1544052 10.1109/RTCSA.2019.8864571 10.48550/arXiv.1711.02508 10.1109/TCST.2020.3001117 10.24963/ijcai.2018/685 10.1109/ICRA.2011.5980409 10.1016/j.inpa.2022.02.002 10.1145/3301273 10.5555/3104322.3104425 10.3390/rs9080824 10.23919/ECC.2013.6669644 10.1109/LRA.2017.2720851 10.1109/ICRA48891.2023.10160851 10.1109/IROS55552.2023.10341871 10.1109/LRA.2017.2776353 10.1007/s10846-021-01527-7 10.48550/arXiv.1707.06347 10.1002/9780470316887 10.1109/TNSE.2023.3325002 10.1007/s10514-012-9280-5 10.48550/arXiv.1506.02438 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Copyright Springer Nature B.V. Jan 2025 |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: Copyright Springer Nature B.V. Jan 2025 |
| DBID | AAYXX CITATION |
| DOI | 10.1007/s00521-024-10675-4 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1433-3058 |
| EndPage | 1240 |
| ExternalDocumentID | 10_1007_s00521_024_10675_4 |
| GrantInformation_xml | – fundername: Shenzhen Fundamental Research Program grantid: JCYJ20220818102415033; JSGG20201103093802006; KJZD20230923114222045 funderid: http://dx.doi.org/10.13039/501100017607 – fundername: National Natural Science Foundation of China grantid: 61977019 funderid: http://dx.doi.org/10.13039/501100001809 |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29N 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PT4 PT5 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z5O Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB |
| ID | FETCH-LOGICAL-c1854-25a7ec4b5b79760062c71b1ed3f51b93c4d0c2ec2beebe3d2fd18a8bdfe70d1f3 |
| IEDL.DBID | RSV |
| ISSN | 0941-0643 |
| IngestDate | Wed Nov 05 08:57:39 EST 2025 Sat Nov 29 04:30:44 EST 2025 Fri Feb 21 02:37:08 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | Robustness Trajectory tracking Low-level control Reinforcement learning Quadrotor |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c1854-25a7ec4b5b79760062c71b1ed3f51b93c4d0c2ec2beebe3d2fd18a8bdfe70d1f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 3159550125 |
| PQPubID | 2043988 |
| PageCount | 18 |
| ParticipantIDs | proquest_journals_3159550125 crossref_primary_10_1007_s00521_024_10675_4 springer_journals_10_1007_s00521_024_10675_4 |
| PublicationCentury | 2000 |
| PublicationDate | 20250100 2025-01-00 20250101 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – month: 1 year: 2025 text: 20250100 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: Heidelberg |
| PublicationTitle | Neural computing & applications |
| PublicationTitleAbbrev | Neural Comput & Applic |
| PublicationYear | 2025 |
| Publisher | Springer London Springer Nature B.V |
| Publisher_xml | – name: Springer London – name: Springer Nature B.V |
| References | J Kober (10675_CR22) 2013; 32 K Choutri (10675_CR3) 2020; 10 CH Pi (10675_CR35) 2020; 95 A Hafeez (10675_CR8) 2023; 10 RS Sutton (10675_CR19) 2018 JP Škrinjar (10675_CR6) 2019; 42 H Shan (10675_CR13) 2024; 11 10675_CR30 10675_CR31 M Faessler (10675_CR42) 2018; 3 W Koch (10675_CR25) 2019; 3 10675_CR14 10675_CR36 10675_CR37 10675_CR38 10675_CR39 10675_CR32 M Idrissi (10675_CR1) 2022; 104 10675_CR11 J Hwangbo (10675_CR29) 2017; 2 10675_CR34 F Santoso (10675_CR18) 2018; 15 AL Salih (10675_CR9) 2010; 5 10675_CR2 Y Zhang (10675_CR4) 2017; 9 E Tal (10675_CR16) 2021; 29 10675_CR5 PEI Pounds (10675_CR10) 2012; 33 10675_CR40 W Yang (10675_CR12) 2023; 53 10675_CR41 10675_CR20 D Hanover (10675_CR17) 2022; 7 S Chen (10675_CR23) 2023; 8 10675_CR26 10675_CR27 Y Wang (10675_CR33) 2020; 50 DK Giles (10675_CR7) 2015; 44 GV Raffo (10675_CR15) 2011 10675_CR28 10675_CR21 10675_CR43 10675_CR44 10675_CR45 10675_CR24 |
| References_xml | – volume: 50 start-page: 3713 issue: 10 year: 2020 ident: 10675_CR33 publication-title: IEEE Trans Syst Man Cybern Syst doi: 10.1109/TSMC.2018.2884725 – ident: 10675_CR38 doi: 10.1109/IJCNN.1992.227257 – ident: 10675_CR31 – volume: 7 start-page: 690 issue: 2 year: 2022 ident: 10675_CR17 publication-title: IEEE Robot Automat Lett doi: 10.1109/LRA.2021.3131690 – ident: 10675_CR32 doi: 10.48550/arXiv.2010.02293 – ident: 10675_CR26 doi: 10.48550/arXiv.1509.02971 – ident: 10675_CR27 – volume: 42 start-page: 359 year: 2019 ident: 10675_CR6 publication-title: Lecture Note Network and Syst doi: 10.1007/978-3-319-90893-9_43 – volume: 53 start-page: 3152 issue: 5 year: 2023 ident: 10675_CR12 publication-title: IEEE Trans Syst Man, Cyber: Syst doi: 10.1109/TSMC.2022.3222857 – volume: 95 year: 2020 ident: 10675_CR35 publication-title: Control Eng Pract doi: 10.1016/j.conengprac.2019.104222 – ident: 10675_CR2 doi: 10.1109/SECon.2012.6196930 – ident: 10675_CR30 doi: 10.1109/LARS/SBR/WRE.2018.00094 – ident: 10675_CR21 doi: 10.48550/arXiv.1709.03339 – volume: 5 start-page: 3660 issue: 23 year: 2010 ident: 10675_CR9 publication-title: Sci. Res Essays – ident: 10675_CR44 – volume: 8 start-page: 2271 issue: 3 year: 2023 ident: 10675_CR23 publication-title: IEEE Trans Intell Veh doi: 10.1109/TIV.2022.3229723 – volume: 15 start-page: 613 issue: 2 year: 2018 ident: 10675_CR18 publication-title: IEEE Trans Automat Sci Eng doi: 10.1109/TASE.2017.2651109 – year: 2011 ident: 10675_CR15 publication-title: IFAC doi: 10.3182/20110828-6-IT-1002.02453 – volume-title: Reinforcement learning: An introduction year: 2018 ident: 10675_CR19 – volume: 32 start-page: 1238 issue: 11 year: 2013 ident: 10675_CR22 publication-title: Int J Robot Res doi: 10.1177/0278364913495721 – ident: 10675_CR40 – volume: 44 start-page: 307 year: 2015 ident: 10675_CR7 publication-title: Chem Eng Trans doi: 10.3303/CET1544052 – ident: 10675_CR34 doi: 10.1109/RTCSA.2019.8864571 – ident: 10675_CR45 doi: 10.48550/arXiv.1711.02508 – volume: 29 start-page: 1203 issue: 3 year: 2021 ident: 10675_CR16 publication-title: IEEE Trans Control Syst Technol doi: 10.1109/TCST.2020.3001117 – ident: 10675_CR20 doi: 10.24963/ijcai.2018/685 – ident: 10675_CR41 doi: 10.1109/ICRA.2011.5980409 – volume: 10 start-page: 192 issue: 2 year: 2023 ident: 10675_CR8 publication-title: Inform Process Agri doi: 10.1016/j.inpa.2022.02.002 – volume: 3 start-page: 1 issue: 2 year: 2019 ident: 10675_CR25 publication-title: ACM Trans Cyber-Physical Syst doi: 10.1145/3301273 – ident: 10675_CR37 doi: 10.5555/3104322.3104425 – volume: 9 start-page: 824 issue: 8 year: 2017 ident: 10675_CR4 publication-title: Remote Sens doi: 10.3390/rs9080824 – ident: 10675_CR14 doi: 10.23919/ECC.2013.6669644 – ident: 10675_CR11 – volume: 2 start-page: 2096 issue: 4 year: 2017 ident: 10675_CR29 publication-title: IEEE Robot Automat Lett doi: 10.1109/LRA.2017.2720851 – ident: 10675_CR43 – ident: 10675_CR24 doi: 10.1109/ICRA48891.2023.10160851 – ident: 10675_CR5 doi: 10.1109/IROS55552.2023.10341871 – volume: 3 start-page: 620 issue: 2 year: 2018 ident: 10675_CR42 publication-title: IEEE Robot Automat Lett doi: 10.1109/LRA.2017.2776353 – volume: 104 start-page: 22 issue: 2 year: 2022 ident: 10675_CR1 publication-title: J Intell Robotic Syst doi: 10.1007/s10846-021-01527-7 – ident: 10675_CR28 doi: 10.48550/arXiv.1707.06347 – ident: 10675_CR36 doi: 10.1002/9780470316887 – volume: 11 start-page: 1510 issue: 2 year: 2024 ident: 10675_CR13 publication-title: IEEE Trans Netw Sci Eng doi: 10.1109/TNSE.2023.3325002 – volume: 33 start-page: 129 year: 2012 ident: 10675_CR10 publication-title: Auton Robot doi: 10.1007/s10514-012-9280-5 – volume: 10 start-page: 2 year: 2020 ident: 10675_CR3 publication-title: Int J Comput Digit Syst – ident: 10675_CR39 doi: 10.48550/arXiv.1506.02438 |
| SSID | ssj0004685 |
| Score | 2.369085 |
| Snippet | Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios,... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Index Database Publisher |
| StartPage | 1223 |
| SubjectTerms | Accuracy Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Control algorithms Control methods Controllers Data Mining and Knowledge Discovery Deep learning Disturbances Hovering Image Processing and Computer Vision Initial conditions Methods Original Article Probability and Statistics in Computer Science Proportional integral derivative Robotics Robust control Rotary wing aircraft Simulation Tracking Unmanned aerial vehicles Velocity Vibration control |
| Title | Aggressive and robust low-level control and trajectory tracking for quadrotors with deep reinforcement learning |
| URI | https://link.springer.com/article/10.1007/s00521-024-10675-4 https://www.proquest.com/docview/3159550125 |
| Volume | 37 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1JSwMxFA5aPXixrlitkoM3DXSyNJNjEYsHKeJGb0O2KUrp1Jmp4r83SWdaFT3obSDDI7y85O3vA-A0JtTGqaRIRYoiqgVGwg-8dJpIEyOZsDo0Cl_zwSAeDsVN1RRW1NXudUoyvNSLZjcfwXSuL6bIjz1jiK6CNafuYg_YcHv3-KkbMgBxOr_F1_RQUrXK_Ezjqzpa2pjf0qJB2_Sb_9vnFtisrEvYm4vDNlixkx3QrJEbYHWRd0HWGwU_2z11UE4MzDM1K0o4zt7Q2FcRwaqCPSyWuXwOsf13_6l9bB06Uxe-zKTJM4_WA300FxprpzC3YRSrDlFHWGFSjPbAQ__y_uIKVdALSDsFThFmkltNFVNc-NRdF2seqcgakrJICaKp6WhsNVbWSQExODVRLGNlUss7JkrJPmhMsok9ALAruJbOaGKWMmqUig3hKRW-carDJYta4Kw-gWQ6n7CRLGYpB14mjpdJ4GVCW6BdH1JS3bYiIZ48c6qWtcB5fSjL5d-pHf7t9yOwgT38b4jAtEGjzGf2GKzr1_KpyE-CFH4Aab_YzA |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS8MwED90Cvri_MTp1Dz4poG1Sdb2cYhj4hyiU_ZW8tWhjHV2m-J_b5K1m4o-6Fsh5QiXS-7ud18ApyGhOkw4xcITFFMZ-TiyDS-NJpJEcRZp6QqF20GnE_Z60W1eFDYust2LkKR7qefFbhbBNK6vT7Fte8YwXYYVajSW7Zh_d__4qRrSDeI0fovN6aEkL5X5mcZXdbSwMb-FRZ22aZb_t89N2MitS9SYicMWLOnhNpSLyQ0ov8g7kDb6zs82Tx3iQ4WyVEzHEzRI3_DAZhGhPIPdLU4y_uyw_Xf7KS22joypi16mXGWpndaDLJqLlNYjlGnXilU61BHlMyn6u_DQvOxetHA-egFLo8Ap9hkPtKSCiSCyobu6LwNPeFqRhHkiIpKqmvS19IU2UkCUnygv5KFQiQ5qykvIHpSG6VDvA6pHgeTGaGKaMqqECBUJEhrZwqlawJlXgbPiBOLRrMNGPO-l7HgZG17GjpcxrUC1OKQ4v23jmFjyzKhaVoHz4lAWy79TO_jb7yew1uretOP2Vef6ENZ9OwrYoTFVKE2yqT6CVfk6eRpnx04iPwCcstuw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEB60inixPrFadQ_edLHJ7prkWNSiKEXwgbdlXymKJDVNFf-9u9vEF3oQb4ENQ5iZzTy_GYDdmFATp4JiGUiKqUpCnLiBl9YSKaIFS4zyQOGLqN-P7-6Sy08oft_tXpckJ5gGN6UpKw-GOj14B765bKYNg0OK3Qg0huk0zFDXSO_i9avbT8hIv5TTxjCuv4eSCjbzM42vpunD3_xWIvWWp9f8_zcvwkLldaLuRE2WYMpky9CsNzqg6oKvQN4d-Pjb_gKRyDQqcjkelegxf8GPrrsIVZ3t_rAsxIPP-b-6R-Vy7si6wOhpLHSRuy0-yGV5kTZmiArjR7Qqn41E1a6KwSrc9E6uj05xtZIBK2vYKQ6ZiIyikskocSW9w1BFgQyMJikLZEIU1R0VGhVKY7WD6DDVQSxiqVMTdXSQkjVoZHlm1gEdJpES1plihjKqpYw1iVKaOEBVJxIsaMFeLQ0-nEze4O8zlj0vueUl97zktAXtWmC8uoUjThx5Zk0wa8F-LaCP49-pbfzt9R2Yuzzu8Yuz_vkmzIduQ7BP0rShURZjswWz6rm8HxXbXjnfAEfY5JQ |
| 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=Aggressive+and+robust+low-level+control+and+trajectory+tracking+for+quadrotors+with+deep+reinforcement+learning&rft.jtitle=Neural+computing+%26+applications&rft.au=Chen%2C+Shiyu&rft.au=Li%2C+Yanjie&rft.au=Lou%2C+Yunjiang&rft.au=Lin%2C+Ke&rft.date=2025-01-01&rft.pub=Springer+London&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=37&rft.issue=3&rft.spage=1223&rft.epage=1240&rft_id=info:doi/10.1007%2Fs00521-024-10675-4&rft.externalDocID=10_1007_s00521_024_10675_4 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0941-0643&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0941-0643&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0941-0643&client=summon |