Multi‐station multi‐robot task assignment method based on deep reinforcement learning
This paper focuses on the problem of multi‐station multi‐robot spot welding task assignment, and proposes a deep reinforcement learning (DRL) framework, which is made up of a public graph attention network and independent policy networks. The graph of welding spots distribution is encoded using the...
Uložené v:
| Vydané v: | CAAI Transactions on Intelligence Technology Ročník 10; číslo 1; s. 134 - 146 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Beijing
John Wiley & Sons, Inc
01.02.2025
Wiley |
| Predmet: | |
| ISSN: | 2468-2322, 2468-6557, 2468-2322 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | This paper focuses on the problem of multi‐station multi‐robot spot welding task assignment, and proposes a deep reinforcement learning (DRL) framework, which is made up of a public graph attention network and independent policy networks. The graph of welding spots distribution is encoded using the graph attention network. Independent policy networks with attention mechanism as a decoder can handle the encoded graph and decide to assign robots to different tasks. The policy network is used to convert the large scale welding spots allocation problem to multiple small scale single‐robot welding path planning problems, and the path planning problem is quickly solved through existing methods. Then, the model is trained through reinforcement learning. In addition, the task balancing method is used to allocate tasks to multiple stations. The proposed algorithm is compared with classical algorithms, and the results show that the algorithm based on DRL can produce higher quality solutions. |
|---|---|
| AbstractList | Abstract This paper focuses on the problem of multi‐station multi‐robot spot welding task assignment, and proposes a deep reinforcement learning (DRL) framework, which is made up of a public graph attention network and independent policy networks. The graph of welding spots distribution is encoded using the graph attention network. Independent policy networks with attention mechanism as a decoder can handle the encoded graph and decide to assign robots to different tasks. The policy network is used to convert the large scale welding spots allocation problem to multiple small scale single‐robot welding path planning problems, and the path planning problem is quickly solved through existing methods. Then, the model is trained through reinforcement learning. In addition, the task balancing method is used to allocate tasks to multiple stations. The proposed algorithm is compared with classical algorithms, and the results show that the algorithm based on DRL can produce higher quality solutions. This paper focuses on the problem of multi‐station multi‐robot spot welding task assignment, and proposes a deep reinforcement learning (DRL) framework, which is made up of a public graph attention network and independent policy networks. The graph of welding spots distribution is encoded using the graph attention network. Independent policy networks with attention mechanism as a decoder can handle the encoded graph and decide to assign robots to different tasks. The policy network is used to convert the large scale welding spots allocation problem to multiple small scale single‐robot welding path planning problems, and the path planning problem is quickly solved through existing methods. Then, the model is trained through reinforcement learning. In addition, the task balancing method is used to allocate tasks to multiple stations. The proposed algorithm is compared with classical algorithms, and the results show that the algorithm based on DRL can produce higher quality solutions. |
| Author | Zhang, Junnan Mu, Chaoxu Wang, Ke |
| Author_xml | – sequence: 1 givenname: Junnan surname: Zhang fullname: Zhang, Junnan organization: Tianjin University – sequence: 2 givenname: Ke orcidid: 0000-0002-8306-1663 surname: Wang fullname: Wang, Ke email: walker_wang@tju.edu.cn organization: Tianjin University – sequence: 3 givenname: Chaoxu orcidid: 0000-0003-1055-9513 surname: Mu fullname: Mu, Chaoxu organization: Tianjin University |
| BookMark | eNp9kc9qGzEQh0VJoY7rS59gIbeAXf3f1bGYJDU49JIeehKzktaVs5ZcSSb4lkfIM_ZJuvaGkFNPGg3ffDPwu0QXIQaH0BeCFwRz9dX4QheEMsU_oAnlsplTRunFu_oTmuW8xRgTpZRg9QT9uj_0xf99fskFio-h2r3-U2xjqQrkxwpy9puwc6FUO1d-R1u1kJ2tBto6t6-S86GLybgz0jtIwYfNZ_Sxgz672es7RT9vbx6W3-frH3er5bf13DBJ-Rwo540SCqRjQlCOsbC14R1XlllGmOw4o6ZzWGBmLLccN7Vgsm7bhgAzhk3RavTaCFu9T34H6agjeH1uxLTRkIo3vdMU17LhoiNUMA7EgmyhodKQwQtQn1xXo2uf4p-Dy0Vv4yGF4XzNSFMziYWqB-p6pEyKOSfXvW0lWJ-S0Kck9DmJASYj_OR7d_wPqZerBzrO_ANeCY2m |
| Cites_doi | 10.1109/tsmc.2021.3094190 10.1049/cit2.12103 10.1016/j.engappai.2021.104422 10.1038/nature24270 10.1016/j.rcim.2016.08.006 10.1016/j.jii.2018.08.001 10.1016/j.rcim.2020.101934 10.1109/tase.2017.2761180 10.1049/cit2.12066 10.1016/j.ins.2018.04.044 10.1016/j.cirp.2014.03.015 10.1080/0305215x.2015.1005084 10.1049/iet‐cta.2018.6125 10.1016/j.robot.2019.04.012 10.1109/jiot.2018.2815982 10.1016/j.rcim.2021.102197 10.1016/j.robot.2018.02.016 10.1038/nature14540 10.1016/j.ejor.2017.06.001 10.1016/j.rcim.2018.08.003 |
| ContentType | Journal Article |
| Copyright | 2024 The Author(s). published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology. 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2024 The Author(s). published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology. – notice: 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | 24P AAYXX CITATION 8FE 8FG ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS DOA |
| DOI | 10.1049/cit2.12394 |
| DatabaseName | Wiley Online Library Open Access CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology collection ProQuest One ProQuest Central ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) 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 Advanced Technologies & Aerospace Collection Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: 24P name: Wiley Online Library Open Access url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2468-2322 |
| EndPage | 146 |
| ExternalDocumentID | oai_doaj_org_article_2076845f12534a1da6ba826c1d4daa7c 10_1049_cit2_12394 CIT212394 |
| Genre | article |
| GrantInformation_xml | – fundername: National Key Research and Development Program of China funderid: 2021YFB1714700 – fundername: Postdoctoral Fellowship Program of CPSF funderid: GZB20240525 – fundername: Postdoctoral Research Foundation of China funderid: 2024M752364 |
| GroupedDBID | 0R~ 1OC 24P AACTN AAEDW AAHHS AAHJG AAJGR AALRI AAXUO ABMAC ABQXS ACCFJ ACCMX ACESK ACGFS ACXQS ADBBV ADMLS ADVLN ADZOD AEEZP AEQDE AEXQZ AFKRA AITUG AIWBW AJBDE AKRWK ALMA_UNASSIGNED_HOLDINGS ALUQN AMRAJ ARAPS ARCSS AVUZU BCNDV BENPR BGLVJ CCPQU EBS EJD FDB GROUPED_DOAJ HCIFZ IAO IDLOA ITC K7- M41 M43 O9- OK1 PHGZT PIMPY RIG ROL RUI SSZ AAMMB AAYWO AAYXX ACVFH ADCNI AEFGJ AEUPX AFFHD AFPUW AGXDD AIDQK AIDYY AIGII AKBMS AKYEP CITATION ICD PHGZM PQGLB WIN 8FE 8FG ABUWG AZQEC DWQXO GNUQQ JQ2 P62 PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c3624-a2448959a6e35524005d7c4f49d3d3136f432cfe0503cd4d40875367bb81a3cc3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001354257800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2468-2322 2468-6557 |
| IngestDate | Mon Nov 10 04:35:34 EST 2025 Wed Aug 13 02:51:25 EDT 2025 Wed Oct 29 21:18:41 EDT 2025 Tue Mar 04 09:30:11 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | Attribution-NonCommercial-NoDerivs |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3624-a2448959a6e35524005d7c4f49d3d3136f432cfe0503cd4d40875367bb81a3cc3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-1055-9513 0000-0002-8306-1663 |
| OpenAccessLink | https://doaj.org/article/2076845f12534a1da6ba826c1d4daa7c |
| PQID | 3187360597 |
| PQPubID | 6852857 |
| PageCount | 13 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_2076845f12534a1da6ba826c1d4daa7c proquest_journals_3187360597 crossref_primary_10_1049_cit2_12394 wiley_primary_10_1049_cit2_12394_CIT212394 |
| PublicationCentury | 2000 |
| PublicationDate | February 2025 2025-02-00 20250201 2025-02-01 |
| PublicationDateYYYYMMDD | 2025-02-01 |
| PublicationDate_xml | – month: 02 year: 2025 text: February 2025 |
| PublicationDecade | 2020 |
| PublicationPlace | Beijing |
| PublicationPlace_xml | – name: Beijing |
| PublicationTitle | CAAI Transactions on Intelligence Technology |
| PublicationYear | 2025 |
| Publisher | John Wiley & Sons, Inc Wiley |
| Publisher_xml | – name: John Wiley & Sons, Inc – name: Wiley |
| References | 2018; 5 2020; 64 2015; 521 2020 2022; 73 2017; 44 2022; 7 2019; 13 2019; 56 2018; 453 2021; 105 2019; 15 2018; 103 2019 2018 2017 2022; 52 2016 2017; 263 2019; 118 2017; 550 2014; 63 2016; 48 2018; 15 Mittal A. (e_1_2_9_29_1) 2019 e_1_2_9_31_1 e_1_2_9_10_1 Kartal B. (e_1_2_9_20_1) 2016 Bello I. (e_1_2_9_25_1) 2017 e_1_2_9_13_1 e_1_2_9_12_1 Vaswani A. (e_1_2_9_26_1) 2017 e_1_2_9_15_1 e_1_2_9_14_1 e_1_2_9_17_1 e_1_2_9_16_1 e_1_2_9_18_1 Drori I. (e_1_2_9_32_1) 2020 e_1_2_9_22_1 e_1_2_9_21_1 e_1_2_9_24_1 e_1_2_9_23_1 e_1_2_9_8_1 e_1_2_9_7_1 e_1_2_9_6_1 e_1_2_9_5_1 e_1_2_9_4_1 e_1_2_9_3_1 e_1_2_9_2_1 Dai H. (e_1_2_9_28_1) 2017 Kartal B. (e_1_2_9_19_1) 2016 e_1_2_9_9_1 Kool W. (e_1_2_9_27_1) 2019 Zhao G. (e_1_2_9_11_1) 2020 Velikovi P. (e_1_2_9_30_1) 2018 |
| References_xml | – start-page: 33 year: 2016 – start-page: 1 year: 2018 end-page: 12 – start-page: 1190 year: 2020 end-page: 1195 – volume: 263 start-page: 1033 issue: 3 year: 2017 end-page: 1048 article-title: Balancing a robotic spot welding manufacturing line: an industrial case study publication-title: Eur. J. Oper. Res. – start-page: 19 year: 2020 end-page: 24 – volume: 103 start-page: 151 year: 2018 end-page: 161 article-title: Resource‐based task allocation for multi‐robot systems publication-title: Robot. Autonom. Syst. – start-page: 6349 year: 2017 end-page: 6359 article-title: Learning combinatorial optimization algorithms over graphs publication-title: Neural Inf. Process. Syst. – start-page: 1 year: 2017 end-page: 15 – volume: 56 start-page: 12 year: 2019 end-page: 37 article-title: Advances in weld seam tracking techniques for robotic welding: a review publication-title: Robot. Comput. Integrated Manuf. – volume: 521 start-page: 445 issue: 7553 year: 2015 end-page: 451 article-title: Reinforcement learning improves behaviour from evaluative feedback publication-title: Nature – start-page: 1 year: 2019 end-page: 25 – start-page: 5999 year: 2017 end-page: 6009 article-title: Attention is all you need publication-title: Neural Inf. Process. Syst. – volume: 15 start-page: 842 issue: 2 year: 2018 end-page: 851 article-title: Intersection‐free geometrical partitioning of multirobot stations for cycle time optimization publication-title: IEEE Trans. Autom. Sci. Eng. – volume: 13 start-page: 2886 issue: 17 year: 2019 end-page: 2893 article-title: Distributed multi‐vehicle task assignment in a time‐invariant drift field with obstacles publication-title: IET Control Theory & Appl. – year: 2016 – volume: 118 start-page: 31 year: 2019 end-page: 46 article-title: A distributed method for dynamic multi‐robot task allocation problems with critical time constraints publication-title: Robot. Autonom. Syst. – volume: 63 start-page: 17 issue: 1 year: 2014 end-page: 20 article-title: Multi‐robot spot‐welding cells: an integrated approach to cell design and motion planning publication-title: CIRP Ann. ‐ Manuf. Technol. – volume: 550 start-page: 354 issue: 7676 year: 2017 end-page: 359 article-title: Mastering the game of go without human knowledge publication-title: Nature – volume: 52 start-page: 4259 issue: 7 year: 2022 end-page: 4271 article-title: Distributed task assignment for multiple robots under limited communication range publication-title: IEEE Trans. Syst. Man Cybern.‐syst. – volume: 73 year: 2022 article-title: Multi‐robot multi‐station cooperative spot welding task allocation based on stepwise optimization: an industrial case study publication-title: Robot. Comput. Integrated Manuf. – volume: 7 start-page: 671 issue: 4 year: 2022 end-page: 684 article-title: Adaptive composite frequency control of power systems using reinforcement learning publication-title: CAAI Trans. Intell. Technol. – volume: 453 start-page: 227 year: 2018 end-page: 238 article-title: An integrated multi‐population genetic algorithm for multi‐vehicle task assignment in a drift field publication-title: Inf. Sci. – start-page: 1 year: 2017 end-page: 19 – volume: 5 start-page: 1749 issue: 3 year: 2018 end-page: 1764 article-title: Task allocation in spatial crowdsourcing: current state and future directions publication-title: IEEE Internet Things J. – volume: 44 start-page: 97 year: 2017 end-page: 116 article-title: Multi‐robot spot‐welding cells for car‐body assembly: design and motion planning publication-title: Robot. Comput. Integrated Manuf. – volume: 48 start-page: 299 issue: 2 year: 2016 end-page: 316 article-title: Double global optimum genetic algorithm particle swarm optimization‐based welding robot path planning publication-title: Eng. Optim. – volume: 105 issue: 80‐ year: 2021 article-title: Learning to traverse over graphs with a Monte Carlo tree search‐based self‐play framework publication-title: Eng. Appl. Artif. Intell. – volume: 64 year: 2020 article-title: A welding task data model for intelligent process planning of robotic welding publication-title: Robot. Comput. Integrated Manuf. – volume: 15 start-page: 207 year: 2019 end-page: 218 article-title: Task allocation in manufacturing: a review publication-title: J. Ind. Inf. Integr. – volume: 7 start-page: 15 issue: 3 year: 2022 end-page: 536 article-title: Research on scheduling strategy for automated storage and retrieval system publication-title: CAAI Trans. Intell. Technol. – year: 2019 – ident: e_1_2_9_17_1 doi: 10.1109/tsmc.2021.3094190 – ident: e_1_2_9_22_1 doi: 10.1049/cit2.12103 – start-page: 5999 year: 2017 ident: e_1_2_9_26_1 article-title: Attention is all you need publication-title: Neural Inf. Process. Syst. – ident: e_1_2_9_31_1 doi: 10.1016/j.engappai.2021.104422 – ident: e_1_2_9_24_1 doi: 10.1038/nature24270 – ident: e_1_2_9_7_1 doi: 10.1016/j.rcim.2016.08.006 – start-page: 1 volume-title: International Conference on Learning Representations year: 2018 ident: e_1_2_9_30_1 – ident: e_1_2_9_2_1 doi: 10.1016/j.jii.2018.08.001 – volume-title: Learning Heuristics over Large Graphs via Deep Reinforcement Learning year: 2019 ident: e_1_2_9_29_1 – ident: e_1_2_9_6_1 doi: 10.1016/j.rcim.2020.101934 – ident: e_1_2_9_10_1 doi: 10.1109/tase.2017.2761180 – ident: e_1_2_9_14_1 doi: 10.1049/cit2.12066 – ident: e_1_2_9_15_1 doi: 10.1016/j.ins.2018.04.044 – start-page: 6349 year: 2017 ident: e_1_2_9_28_1 article-title: Learning combinatorial optimization algorithms over graphs publication-title: Neural Inf. Process. Syst. – start-page: 33 volume-title: The IJCAI‐16 Workshop on Autonomous Mobile Service Robots year: 2016 ident: e_1_2_9_20_1 – ident: e_1_2_9_8_1 doi: 10.1016/j.cirp.2014.03.015 – ident: e_1_2_9_12_1 doi: 10.1080/0305215x.2015.1005084 – start-page: 1 volume-title: International Conference on Learning Representations year: 2017 ident: e_1_2_9_25_1 – start-page: 1 volume-title: International Conference on Learning Representations year: 2019 ident: e_1_2_9_27_1 – start-page: 19 volume-title: International Conference on Machine Learning and Applications year: 2020 ident: e_1_2_9_32_1 – ident: e_1_2_9_16_1 doi: 10.1049/iet‐cta.2018.6125 – ident: e_1_2_9_5_1 doi: 10.1016/j.robot.2019.04.012 – ident: e_1_2_9_3_1 doi: 10.1109/jiot.2018.2815982 – ident: e_1_2_9_13_1 doi: 10.1016/j.rcim.2021.102197 – ident: e_1_2_9_18_1 doi: 10.1016/j.robot.2018.02.016 – ident: e_1_2_9_21_1 doi: 10.1038/nature14540 – ident: e_1_2_9_23_1 – ident: e_1_2_9_9_1 doi: 10.1016/j.ejor.2017.06.001 – start-page: 1190 volume-title: 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics year: 2020 ident: e_1_2_9_11_1 – volume-title: Thirtieth AAAI Conference on Artificial Intelligence year: 2016 ident: e_1_2_9_19_1 – ident: e_1_2_9_4_1 doi: 10.1016/j.rcim.2018.08.003 |
| SSID | ssj0001999537 ssib050169717 ssib050729737 ssib052855658 |
| Score | 2.2957876 |
| Snippet | This paper focuses on the problem of multi‐station multi‐robot spot welding task assignment, and proposes a deep reinforcement learning (DRL) framework, which... Abstract This paper focuses on the problem of multi‐station multi‐robot spot welding task assignment, and proposes a deep reinforcement learning (DRL)... |
| SourceID | doaj proquest crossref wiley |
| SourceType | Open Website Aggregation Database Index Database Publisher |
| StartPage | 134 |
| SubjectTerms | Algorithms Artificial intelligence Assignment problem Attention attention mechanism Automation Coding Deep learning deep reinforcement learning graph neural network Heuristic industrial robot Linear programming Methods Neural networks Path planning Process planning Robots Spot welding task allocation Vehicles Working hours |
| SummonAdditionalLinks | – databaseName: Computer Science Database dbid: K7- link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3PS8MwFA46PXjxBypWpwT0JNTZJm2ak-hwKMjwMGGeQpofY4jrbKtn_wT_Rv8SkzRz7rKLt7Y8SuhL8r6-9_J9AJxRTImtqIWXWKchxnEe8tysR5ImBo1EsUy4Y9d_IP1-NhzSR59wq3xb5WxPdBu1LITNkXfM3CPIYG9KrqZvoVWNstVVL6GxCtai2GzCtihLwnmOxaCfBJEZKymmHTGu44vIyoEvxCFH17-AMf8iVRdqelv_HeQ22PQgE143s2IHrKjJLnh2Z22_P7-qpvoOX_19WeRFDWtevUCDpMcj1x8AG2lpaKOchMZaKjWFpXJEq8LlFKFXnBjtgafe7aB7F3phhVCYeIVDbmJ6RhPKU2Xghu0iTSQRWGMqkUQRSjVGsdDKcsUIiSW2tPcoJXmeRRwJgfZBa1JM1AGAwlhkKtNaGxyJsMqkiHSuCBIkV5SgAJzOPjObNvwZzNW9MWXWGcw5IwA31gO_Fpbz2j0oyhHzS4jFrmiYaAPJEOaR5GnOzc-RiMwIOSciAO2ZU5hfiBWbeyQA586nS4bBuveD2F0dLn_XEdiIrQ6w695ug1ZdvqtjsC4-6nFVnrhJ-APqFuaw priority: 102 providerName: ProQuest – databaseName: Wiley Online Library Open Access dbid: 24P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LaxRBEC5i4sFLNKi4SZSG5CSM2o-ZngYvMSQoSMghQjw1_VwWyW6YneTsT8hv9JekumY2j4sg3maGGmiqq7q-7qr-CmDfKKNLRq36pHJTKSV85Tz6o25qRCNcxNoRu_53fXLSnp-b0zX4vLoLM_BD3B24Fc-g9bo4uPNDFxIEtTiJYdaLD7x09n4CG5xLXWxaqNP7ExbEPjWRZopyvQihg1jxkyrz8f73RxGJiPsfoc2HmJWCzvHz_xvuC9gcwSY7GKxjC9bS_CX8pDu3f37fLIcsPLsY37uFX_Ssd8tfDBH1bEp1AmxoMc1KtIsMpWNKl6xLRLga6GyRjZ0npq_gx_HR2eHXamywUAWMW6pyGNtbUxvXJIQdpZq0jjqorEyUUXLZZCVFyKlwxoSooir097LR3rfcyRDka1ifL-bpDbCAEm1qc86IJ6VKbQw8-6Rl0D4ZLSewt1KyvRx4NCzlv5WxRTWWVDOBL0X_dxKF-5o-LLqpHV3JCkoe1hmhmVSOR9d4h5ukwHGEzukwgd3V7NnRIZcWly4tcetm9ATe0zz9ZRj28NuZoKftfxHegWeidAemmu5dWO-7q_QWnobrfrbs3pFx3gJvjebh priority: 102 providerName: Wiley-Blackwell |
| Title | Multi‐station multi‐robot task assignment method based on deep reinforcement learning |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fcit2.12394 https://www.proquest.com/docview/3187360597 https://doaj.org/article/2076845f12534a1da6ba826c1d4daa7c |
| Volume | 10 |
| WOSCitedRecordID | wos001354257800001&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: 2468-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999537 issn: 2468-2322 databaseCode: DOA dateStart: 20180101 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: 2468-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssib050729737 issn: 2468-2322 databaseCode: M~E dateStart: 20160101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 2468-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999537 issn: 2468-2322 databaseCode: K7- dateStart: 20170601 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2468-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999537 issn: 2468-2322 databaseCode: BENPR dateStart: 20170601 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2468-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999537 issn: 2468-2322 databaseCode: PIMPY dateStart: 20170601 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVWIB databaseName: Wiley Online Library Free Content customDbUrl: eissn: 2468-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999537 issn: 2468-2322 databaseCode: WIN dateStart: 20170101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell – providerCode: PRVWIB databaseName: Wiley Online Library Open Access customDbUrl: eissn: 2468-2322 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001999537 issn: 2468-2322 databaseCode: 24P dateStart: 20170101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3PT9swFH5iwGEXNMQmukFlaZyQwprYiePjQK2oWKsIdQJOluMfVTWtRU3giPgT-Bv3l_DspFN7YZddrDjxwfpe7PfZz_4ewIlggvuIWtRjLosYS8pIlTgeeZYiG4kTk6qgrv-Dj8f57a0o1lJ9-TNhjTxwA5zfAclyljp0xJSp2KisVEiJdWyYUYprP_v2uFhbTIXdFeQ9KeUrPVImvulZnZzFPhH4hgcKQv0b7HKdowYnM_gAey07JN-bXu3Dlp0fwF24JPvn-aVqwubkd1tfLspFTWpV_SJIgWfTENgnTU5o4t2TIdjaWHtPljYopOqwGUjaVBHTj_Bz0J9cXEZtRoRIo6NhkUJnnItUqMwiT_DHP1PDNXNMGGpoTDPHaKKd9SIvGgFiXq-eZrws81hRrekn2J4v5vYQiMYWuc2dc0gAKbO50bErLaeal1Zw2oGvK5TkfSN8IUPAmgnpsZQByw6cewD_tvBi1eEFmlC2JpT_MmEHjlbwy3YEVRLnGk5xrSV4B06DSd7ohrwYTpLw9Pl_dOgLvE98mt9wOPsItuvlgz2GXf1Yz6plF94lrOjCznl_XFx3w3-H5RWPsBw99fFLMRwVd1i7GY5fAb1a4AE |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbtQwEB6VFgkuUASIhRYsARekUGI7cXxAVVtaddVlxWGRysk4_lmtEJslCSBuPAJPwkPxJIydhLaX3nrglkSW5XjGM5894_kAnkkuRYioJa-4zxPOaZnoEtejyDNEIym1mY7V9SdiOi1OT-W7Nfg93IUJaZWDTYyG2lYmnJHvoO4Jhthbit3VlySwRoXo6kCh0anFifvxHbdszevxG5Tvc0qPDmcHx0nPKpAYNNY80ejQCplJnTv0tSGFMrPCcM-lZZalLPecUeNdKJRiLLc81HxnuSjLItXMGIb9XoMNznA8IQgskrMzHURbGRNDFVQud8yipS_TQD9-we9FeoALmPY8Mo6u7ej2_zYpm3CrB9Fkr9P6O7DmlnfhQ7xL_Ofnr6bLLiCf-_e6KquWtLr5RHCnsJjH_AfSUWeT4MUtwdbWuRWpXSwka-KZKekZNeb34P2V_M19WF9WS_cAiMEWhSu894iTGXeFNakvnWBGlE4KNoKng1jVqqsPomJcn0sVhK-i8EewHyT-r0Wo6R0_VPVc9SZC0RgUzTxCTsZ1anVeatz8mRRHqLUwI9galED1hqZRZxowghdRhy4ZhjoYz2h8enh5X0_gxvHs7URNxtOTR3CTBs7jmKm-Bett_dVtw3XzrV009eO4AAh8vGrl-gtFuEBR |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5BQYgLDwFiSwFLcEIKEHsSx0corKioVnsoUjlZjh-rVcXuKht65ifwG_kljCfZll6QELckmkiW7fF88_A3AC8NGp0zasVbTHWBKNvCtaSPuq4IjZQyVI7Z9Y_1bNacnpr5WJuT78IM_BAXAbesGXxeZwWPm5AGhxMzSaZf9vJ1mVt7X4cbWNEhm4mdcX4ZYiHwUzFrpsz3iwg7yB1BKZo3l79fMUnM3H8Fbv4JWtnqTO_-53jvwZ0Rbop3w_64D9fi6gF85Vu3v3783A55ePFtfO_W7boXvdueCcLUywVXCoihybTI9i4Ikg4xbkQXmXLVc3RRjL0nFg_hy_TjyeGnYmyxUHiyXFg4su6NqYyrIwGPXE9aBe0xoQkqqFLVCZX0KWbWGB8wYCbAV7Vu26Z0ynv1CPZW61V8DMKTRBOblBIhSoWxCb5MbdTK6zYarSbwYjfLdjMwaVjOgKOxeWosT80E3ucFuJDI7Nf8Yd0t7KhMVnL6sEoEzhS6Mri6deQm-ZJG6Jz2EzjYLZ8dVXJr6fDSipw3oyfwihfqL8Owh0cnkp_2_0X4Odyaf5ja46PZ5ydwW-ZWwVzgfQB7ffc9PoWb_rxfbrtnvFF_A-O16kc |
| 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=Multi%E2%80%90station+multi%E2%80%90robot+task+assignment+method+based+on+deep+reinforcement+learning&rft.jtitle=CAAI+Transactions+on+Intelligence+Technology&rft.au=Junnan+Zhang&rft.au=Ke+Wang&rft.au=Chaoxu+Mu&rft.date=2025-02-01&rft.pub=Wiley&rft.eissn=2468-2322&rft.volume=10&rft.issue=1&rft.spage=134&rft.epage=146&rft_id=info:doi/10.1049%2Fcit2.12394&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_2076845f12534a1da6ba826c1d4daa7c |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2468-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2468-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2468-2322&client=summon |