{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:07:01Z","timestamp":1776085621392,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172443"],"award-info":[{"award-number":["62172443"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2022JJ30760"],"award-info":[{"award-number":["2022JJ30760"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Changsha","award":["kq2202107"],"award-info":[{"award-number":["kq2202107"]}]},{"name":"Natural Science Foundation of Changsha","award":["kq2202108"],"award-info":[{"award-number":["kq2202108"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Autonomous exploration is a critical technology to realize robotic intelligence as it allows unsupervised preparation for future tasks and facilitates flexible deployment. In this paper, a novel Deep Reinforcement  Learning (DRL) based autonomous exploration strategy is proposed to efficiently reduce the unknown area of the workspace and provide accurate 2D map construction for mobile robots. Different from existing human-designed exploration techniques that usually make strong assumptions about the scenarios and the tasks, we utilize a model-free method to directly learn an exploration strategy through trial-and-error interactions with complex environments. To be specific, the Generalized Voronoi Diagram (GVD) is first utilized for domain conversion to obtain a high-dimensional Topological Environmental Representation (TER). Then, the Generalized Voronoi Networks (GVN) with spatial awareness and episodic memory is designed to learn autonomous exploration policies interactively online. For complete and efficient exploration, Invalid Action Masking (IAM) is employed to reshape the configuration space of exploration tasks to cope with the explosion of action space and observation space caused by the expansion of the exploration range. Furthermore, a well-designed reward function is leveraged to guide the learning of policies. Extensive baseline tests and comparative simulations show that our strategy outperforms the state-of-the-art strategies in terms of map quality and exploration speed. Sufficient ablation studies and mobile robot experiments demonstrate the effectiveness and superiority of our strategy.<\/jats:p>","DOI":"10.1007\/s40747-023-01144-x","type":"journal-article","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T12:02:16Z","timestamp":1688040136000},"page":"7365-7379","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["EMExplorer: an episodic memory enhanced autonomous exploration strategy with Voronoi domain conversion and invalid action masking"],"prefix":"10.1007","volume":"9","author":[{"given":"Bolei","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3393-8874","authenticated-orcid":false,"given":"Ping","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Yongzheng","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Siyi","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Yixiong","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Sheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"key":"1144_CR1","first-page":"1","volume":"71","author":"S Zhang","year":"2022","unstructured":"Zhang S, Zhang X, Li T et al (2022) Fast active aerial exploration for traversable path finding of ground robots in unknown environments. IEEE Trans Instrum Meas 71:1\u201313","journal-title":"IEEE Trans Instrum Meas"},{"issue":"3","key":"1144_CR2","first-page":"1","volume":"12","author":"Y Wang","year":"2016","unstructured":"Wang Y, Tan R, Xing G et al (2016) Energy-efficient aquatic environment monitoring using smartphone-based robots. ACM Trans Sens Netw TOSN 12(3):1\u201328","journal-title":"ACM Trans Sens Netw TOSN"},{"issue":"4","key":"1144_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2505767","volume":"10","author":"Y Wang","year":"2014","unstructured":"Wang Y, Tan R, Xing G et al (2014) Spatiotemporal aquatic field reconstruction using cyber-physical robotic sensor systems. ACM Trans Sens Netw TOSN 10(4):1\u201327","journal-title":"ACM Trans Sens Netw TOSN"},{"issue":"2","key":"1144_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2422966.2422979","volume":"9","author":"D Wang","year":"2013","unstructured":"Wang D, Liu J, Zhang Q (2013) On mobile sensor assisted field coverage. ACM Trans Sens Netw TOSN 9(2):1\u201327","journal-title":"ACM Trans Sens Netw TOSN"},{"key":"1144_CR5","doi-asserted-by":"crossref","unstructured":"Ropero F, Mu\u00f1oz P, R-Moreno MD (2019) TERRA: a path planning algorithm for cooperative UGV-UAV exploration. Eng Appl Artif Intelli 78:260\u2013272","DOI":"10.1016\/j.engappai.2018.11.008"},{"key":"1144_CR6","unstructured":"Yamauchi BA (1997) frontier-based approach for autonomous exploration. In: Proceedings, IEEE international symposium on computational intelligence in robotics and automation CIRA\u201997.\u2019 Towards new computational principles for robotics and automation\u2019. IEEE, pp 146\u2013151 (1997)"},{"key":"1144_CR7","doi-asserted-by":"crossref","unstructured":"Yu J, Tong J, Xu Y et al (2021) Smmr-explore: Submap-based multi-robot exploration system with multi-robot multi-target potential field exploration method. In: 2021 IEEE international conference on robotics and automation (ICRA). IEEE, pp 8779\u20138785","DOI":"10.1109\/ICRA48506.2021.9561328"},{"key":"1144_CR8","unstructured":"Garaffa LC, Basso M, Konzen AA et al (2021) Reinforcement learning for mobile robotics exploration: a survey. IEEE Trans Neural Netw Learn Syst"},{"key":"1144_CR9","doi-asserted-by":"crossref","unstructured":"Lodel M, Brito B, Serra-G\u00f3mez A et al (2022) Where to look next: learning viewpoint recommendations for informative trajectory planning. In: 2022 IEEE international conference on robotics and automation (ICRA). IEEE","DOI":"10.1109\/ICRA46639.2022.9812190"},{"issue":"6","key":"1144_CR10","doi-asserted-by":"publisher","first-page":"2064","DOI":"10.1109\/TNNLS.2019.2927869","volume":"31","author":"H Li","year":"2019","unstructured":"Li H, Zhang Q, Zhao D (2019) Deep reinforcement learning-based automatic exploration for navigation in unknown environment[J]. IEEE Trans Neural Netw Learn Syst 31(6):2064\u20132076","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"1144_CR11","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1109\/TRO.2004.835454","volume":"21","author":"JY Lee","year":"2005","unstructured":"Lee JY, Choset H (2005) Sensor-based exploration for convex bodies: a new roadmap for a convex-shaped robot. IEEE Trans Robot 21(2):240\u2013247","journal-title":"IEEE Trans Robot"},{"key":"1144_CR12","doi-asserted-by":"crossref","unstructured":"Fang K, Toshev A, Fei-Fei L et al (2019) Scene memory transformer for embodied agents in long-horizon tasks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 538\u2013547","DOI":"10.1109\/CVPR.2019.00063"},{"key":"1144_CR13","unstructured":"Fortunato M, Tan M, Faulkner R et al (2019) Generalization of reinforcement learners with working and episodic memory. Adv Neural Inf Process Syst 32"},{"key":"1144_CR14","doi-asserted-by":"crossref","unstructured":"Gupta S, Davidson J, Levine S et al (2017) Cognitive mapping and planning for visual navigation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2616\u20132625","DOI":"10.1109\/CVPR.2017.769"},{"key":"1144_CR15","unstructured":"Chaplot DS, Gandhi D, Gupta S, Gupta A, Salakhutdinov R (2020) Learning to explore using active neural slam. In: International conference on learning representations (ICLR)"},{"key":"1144_CR16","unstructured":"Huang S, Onta\u00f1\u00f3n S (2020) A closer look at invalid action masking in policy gradient algorithms. arXiv preprint arXiv:2006.14171"},{"key":"1144_CR17","doi-asserted-by":"crossref","unstructured":"Umari H, Mukhopadhyay S (2017) Autonomous robotic exploration based on multiple rapidly-exploring randomized trees. In: 2017 IEEE\/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 1396\u20131402","DOI":"10.1109\/IROS.2017.8202319"},{"issue":"4","key":"1144_CR18","doi-asserted-by":"publisher","first-page":"1720","DOI":"10.1109\/TASE.2019.2894748","volume":"16","author":"C Wang","year":"2019","unstructured":"Wang C, Chi W, Sun Y et al (2019) Autonomous robotic exploration by incremental road map construction. IEEE Trans Autom Sci Eng 16(4):1720\u20131731","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"1144_CR19","doi-asserted-by":"crossref","unstructured":"Cavinato V, Eppenberger T, Youakim D et al (2021) Dynamic-aware autonomous exploration in populated environments. In: 2021 IEEE international conference on robotics and automation (ICRA). IEEE, pp 1312\u20131318","DOI":"10.1109\/ICRA48506.2021.9560933"},{"key":"1144_CR20","doi-asserted-by":"crossref","unstructured":"Yu J, Tong J, Xu Y et al (2021) Smmr-explore: Submap-based multi-robot exploration system with multi-robot multi-target potential field exploration method. In: 2021 IEEE international conference on robotics and automation (ICRA). IEEE, pp 8779\u20138785","DOI":"10.1109\/ICRA48506.2021.9561328"},{"key":"1144_CR21","doi-asserted-by":"crossref","unstructured":"Zhu H, Cao C, Xia Y et al (2021) DSVP: Dual-stage viewpoint planner for rapid exploration by dynamic expansion. In: 2021 IEEE\/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 7623\u20137630","DOI":"10.1109\/IROS51168.2021.9636473"},{"issue":"4","key":"1144_CR22","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.engappai.2009.12.005","volume":"23","author":"M Juli\u00e1","year":"2010","unstructured":"Juli\u00e1 M, Reinoso O, Gil A et al (2010) A hybrid solution to the multi-robot integrated exploration problem. Eng Appl Artif Intell 23(4):473\u2013486","journal-title":"Eng Appl Artif Intell"},{"key":"1144_CR23","doi-asserted-by":"crossref","unstructured":"Zhang X, Chu Y, Liu Y et al (2021) A novel informative autonomous exploration strategy with uniform sampling for quadrotors. In: IEEE transactions on industrial electronics","DOI":"10.1109\/TIE.2021.3137616"},{"key":"1144_CR24","doi-asserted-by":"crossref","unstructured":"Zhong P, Chen B, Cui Y et al (2021) Space-heuristic navigation and occupancy map prediction for robot autonomous exploration. In: International conference on algorithms and architectures for parallel processing. Springer, Cham, pp 578\u2013594","DOI":"10.1007\/978-3-030-95384-3_36"},{"key":"1144_CR25","doi-asserted-by":"crossref","unstructured":"Shrestha R, Tian FP, Feng W et al (2019) Learned map prediction for enhanced mobile robot exploration. In: 2019 international conference on robotics and automation (ICRA). IEEE, pp 1197\u20131204","DOI":"10.1109\/ICRA.2019.8793769"},{"key":"1144_CR26","doi-asserted-by":"crossref","unstructured":"Zhu D, Li T, Ho D et al (2018) Deep reinforcement learning supervised autonomous exploration in office environments. In: 2018 IEEE international conference on robotics and automation (ICRA). IEEE, pp 7548\u20137555","DOI":"10.1109\/ICRA.2018.8463213"},{"issue":"2","key":"1144_CR27","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/LRA.2019.2891991","volume":"4","author":"F Niroui","year":"2019","unstructured":"Niroui F, Zhang K, Kashino Z et al (2019) Deep reinforcement learning robot for search and rescue applications: exploration in unknown cluttered environments[J]. IEEE Robot Autom Lett 4(2):610\u2013617","journal-title":"IEEE Robot Autom Lett"},{"issue":"1","key":"1144_CR28","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1109\/LRA.2021.3118078","volume":"7","author":"Z Zheng","year":"2021","unstructured":"Zheng Z, Cao C, Pan J (2021) A hierarchical approach for mobile robot exploration in pedestrian crowd. IEEE Robot Autom Lett 7(1):175\u2013182","journal-title":"IEEE Robot Autom Lett"},{"key":"1144_CR29","doi-asserted-by":"crossref","unstructured":"Zhu D, Li T, Ho D et al (2018) Deep reinforcement learning supervised autonomous exploration in office environments. In: 2018 IEEE international conference on robotics and automation (ICRA). IEEE, pp 7548\u20137555","DOI":"10.1109\/ICRA.2018.8463213"},{"issue":"6","key":"1144_CR30","doi-asserted-by":"publisher","first-page":"2064","DOI":"10.1109\/TNNLS.2019.2927869","volume":"31","author":"H Li","year":"2019","unstructured":"Li H, Zhang Q, Zhao D (2019) Deep reinforcement learning-based automatic exploration for navigation in unknown environment[J]. IEEE Trans Neural Netw Learn Syst 31(6):2064\u20132076","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1144_CR31","doi-asserted-by":"crossref","unstructured":"Chen F, Martin JD, Huang Y et al (2020) Autonomous exploration under uncertainty via deep reinforcement learning on graphs. In: 2020 IEEE\/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 6140\u20136147","DOI":"10.1109\/IROS45743.2020.9341657"},{"key":"1144_CR32","doi-asserted-by":"crossref","unstructured":"Lee WC, Lim MC, Choi HL (2021) Extendable navigation network based reinforcement learning for indoor robot exploration. In: 2021 IEEE international conference on robotics and automation (ICRA). IEEE, pp 11508\u201311514","DOI":"10.1109\/ICRA48506.2021.9561040"},{"key":"1144_CR33","doi-asserted-by":"crossref","unstructured":"Xu Y, Yu J, Tang J et al (2022) Explore-bench: data sets, metrics and evaluations for frontier-based and deep-reinforcement-learning-based autonomous exploration. arXiv preprint arXiv:2202.11931","DOI":"10.1109\/ICRA46639.2022.9812344"},{"key":"1144_CR34","doi-asserted-by":"crossref","unstructured":"Tsang F, Walker T, MacDonald RA et al (2021) LAMP: learning a motion policy to repeatedly navigate in an uncertain environment. In: IEEE transactions on robotics","DOI":"10.1109\/TRO.2021.3109414"},{"issue":"4","key":"1144_CR35","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1109\/TRO.2020.2975428","volume":"36","author":"A Francis","year":"2020","unstructured":"Francis A, Faust A, Chiang HTL et al (2020) Long-range indoor navigation with prm-rl. IEEE Trans Robot 36(4):1115\u20131134","journal-title":"IEEE Trans Robot"},{"key":"1144_CR36","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhang S, Chen S et al (2021) Straight skeleton based automatic generation of hierarchical topological map in indoor environment. In: 2021 IEEE international intelligent transportation systems conference (ITSC). IEEE, pp 2229\u20132236","DOI":"10.1109\/ITSC48978.2021.9564514"},{"key":"1144_CR37","doi-asserted-by":"crossref","unstructured":"Chi W, Wang J, Ding Z et al (2021) A reusable generalized voronoi diagram based feature tree for fast robot motion planning in trapped environments. IEEE Sens J","DOI":"10.1109\/JSEN.2021.3054888"},{"issue":"12","key":"1144_CR38","doi-asserted-by":"publisher","first-page":"10621","DOI":"10.1109\/TIE.2019.2962425","volume":"67","author":"J Wang","year":"2020","unstructured":"Wang J, Meng MQH (2020) Optimal path planning using generalized voronoi graph and multiple potential functions. IEEE Trans Ind Electron 67(12):10621\u201310630","journal-title":"IEEE Trans Ind Electron"},{"issue":"4","key":"1144_CR39","doi-asserted-by":"publisher","first-page":"3854","DOI":"10.1109\/TIE.2021.3075852","volume":"69","author":"Z Wu","year":"2022","unstructured":"Wu Z, Chen Y, Liang J et al (2022) ST-FMT*: a fast optimal global motion planning for mobile robot. IEEE Trans Ind Electron 69(4):3854\u20133864","journal-title":"IEEE Trans Ind Electron"},{"issue":"5","key":"1144_CR40","doi-asserted-by":"publisher","first-page":"4926","DOI":"10.1109\/TIE.2021.3078390","volume":"69","author":"W Chi","year":"2022","unstructured":"Chi W, Ding Z, Wang J et al (2022) A generalized Voronoi diagram based efficient heuristic path planning method for RRTs in mobile robots[J]. IEEE Trans Ind Electron 69(5):4926\u20134937","journal-title":"IEEE Trans Ind Electron"},{"issue":"10","key":"1144_CR41","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.robot.2012.08.010","volume":"61","author":"B Lau","year":"2013","unstructured":"Lau B, Sprunk C, Burgard W (2013) Efficient grid-based spatial representations for robot navigation in dynamic environments. Robot Autonom Syst 61(10):1116\u20131130","journal-title":"Robot Autonom Syst"},{"key":"1144_CR42","doi-asserted-by":"crossref","unstructured":"Castellini A, Marchesini E, Farinelli A (2021) Partially observable Monte Carlo Planning with state variable constraints for mobile robot navigation. Eng Appl Artif Intell 104","DOI":"10.1016\/j.engappai.2021.104382"},{"key":"1144_CR43","doi-asserted-by":"crossref","unstructured":"Ramakrishnan SK, Al-Halah Z, Grauman K (2020) Occupancy anticipation for efficient exploration and navigation. In: European conference on computer vision. Springer, Cham, pp 400\u2013418","DOI":"10.1007\/978-3-030-58558-7_24"},{"key":"1144_CR44","unstructured":"Sutton RS, McAllester D, Singh S et al (1999) Policy gradient methods for reinforcement learning with function approximation. Adv Neural Inf Process Syst 12"},{"key":"1144_CR45","unstructured":"Schulman J, Wolski F, Dhariwal P et al (2017) Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347"},{"key":"1144_CR46","unstructured":"Hill A, Raffin A, Ernestus M, Gleave A, Kanervisto A, Traore R, Dhariwal P, Hesse C, Klimov O, Nichol A, Plappert M, Radford A, Schulman J, Sidor S, Wu Y (2018) Stable baselines. https:\/\/github.com\/hill-a\/stable-baselines"},{"issue":"2","key":"1144_CR47","doi-asserted-by":"publisher","first-page":"2729","DOI":"10.1109\/LRA.2021.3062008","volume":"6","author":"Z Xu","year":"2021","unstructured":"Xu Z, Deng D, Shimada K (2021) Autonomous UAV exploration of dynamic environments via incremental sampling and probabilistic roadmap[J]. IEEE Robot Autom Lett 6(2):2729\u20132736","journal-title":"IEEE Robot Autom Lett"},{"key":"1144_CR48","unstructured":"Welling M, Kipf TN (2016) Semi-supervised classification with graph convolutional networks. In: J. International conference on learning representations (ICLR 2017)"},{"key":"1144_CR49","unstructured":"Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271"},{"key":"1144_CR50","unstructured":"Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. arXiv preprint arXiv:1409.2329"},{"issue":"8","key":"1144_CR51","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.1002\/rob.21993","volume":"37","author":"T Dang","year":"2020","unstructured":"Dang T, Tranzatto M, Khattak S et al (2020) Graph-based subterranean exploration path planning using aerial and legged robots. J Field Robot 37(8):1363\u20131388","journal-title":"J Field Robot"},{"key":"1144_CR52","doi-asserted-by":"crossref","unstructured":"Yan S, Wu Z, Wang J et al (2022) Real-world learning control for autonomous exploration of a biomimetic robotic shark. IEEE Trans Ind Electron","DOI":"10.1109\/TIE.2022.3174306"},{"key":"1144_CR53","doi-asserted-by":"crossref","unstructured":"Kim SK, Bouman A, Salhotra G et al (2021) Plgrim: Hierarchical value learning for large-scale exploration in unknown environments. In: Proceedings of the international conference on automated planning and scheduling, vol 31, pp 652\u2013662","DOI":"10.1609\/icaps.v31i1.16014"},{"issue":"4","key":"1144_CR54","doi-asserted-by":"publisher","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","volume":"37","author":"B Lim","year":"2021","unstructured":"Lim B, Ar\u0131k S\u00d6, Loeff N et al (2021) Temporal fusion transformers for interpretable multi-horizon time series forecasting. Int J Forecast 37(4):1748\u20131764","journal-title":"Int J Forecast"},{"issue":"8","key":"1144_CR55","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"issue":"18","key":"1144_CR56","doi-asserted-by":"publisher","first-page":"10139","DOI":"10.1002\/rnc.6354","volume":"32","author":"C Zhou","year":"2022","unstructured":"Zhou C, Tao H, Chen Y et al (2022) Robust point-to-point iterative learning control for constrained systems: a minimum energy approach[J]. Int J Robust Nonlinear Control 32(18):10139\u201310161","journal-title":"Int J Robust Nonlinear Control"},{"key":"1144_CR57","doi-asserted-by":"crossref","unstructured":"Guan S, Zhuang Z, Tao H et al (2023) Feedback-aided PD-type iterative learning control for time-varying systems with non-uniform trial lengths. Trans Inst Meas Control 01423312221142564","DOI":"10.1177\/01423312221142564"},{"issue":"5","key":"1144_CR58","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1002\/acs.3396","volume":"36","author":"Z Zhuang","year":"2022","unstructured":"Zhuang Z, Tao H, Chen Y et al (2022) Iterative learning control for repetitive tasks with randomly varying trial lengths using successive projection[J]. Int J Adapt Control Signal Process 36(5):1196\u20131215","journal-title":"Int J Adapt Control Signal Process"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01144-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-023-01144-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01144-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T19:08:04Z","timestamp":1698433684000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-023-01144-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,29]]},"references-count":58,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["1144"],"URL":"https:\/\/doi.org\/10.1007\/s40747-023-01144-x","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,29]]},"assertion":[{"value":"20 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}