{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T20:08:07Z","timestamp":1775592487682,"version":"3.50.1"},"reference-count":70,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"103","content-domain":{"domain":["www.science.org"],"crossmark-restriction":true},"short-container-title":["Sci. Robot."],"published-print":{"date-parts":[[2025,6,18]]},"abstract":"<jats:p>The rapid development of autonomous robots has resulted in marked societal and economic benefits. However, enabling robots to navigate complex environments with human-like agility remains a formidable challenge. Unlike robots, humans excel at pathfinding because of their superior spatial awareness and their ability to leverage experience. Inspired by these observations, we designed a neural network to simulate the intuitive pathfinding abilities of humans, integrating global environmental information and previous experiences to identify feasible pathways. Experiments demonstrated that, unlike traditional algorithms whose efficiency deteriorates in complex settings, the proposed method maintains stable computational performance. To further enhance motion quality, we introduce a numerically stable spatiotemporal trajectory optimizer with a unique bilayer polynomial trajectory representation in flat space. This optimization leverages differential flatness to enhance efficiency and fundamentally eliminates singularities in the original problem, thereby robustly converging to continuous and feasible motion even in complex maneuvering scenarios. Our hierarchical motion planner, validated through large-scale maze experiments, combines front-end path planning with back-end trajectory refinement, achieving robust and efficient navigation. We anticipate that our planner will advance stable navigation for robots in complex environments, thereby propelling the progress of robotic autonomy.<\/jats:p>","DOI":"10.1126\/scirobotics.ads4551","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T18:00:56Z","timestamp":1750269656000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":7,"title":["Hierarchically depicting vehicle trajectory with stability in complex environments"],"prefix":"10.1126","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7617-5964","authenticated-orcid":true,"given":"Zhichao","family":"Han","sequence":"first","affiliation":[{"name":"Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou, China."},{"name":"Huzhou Institute of Zhejiang University, Huzhou, China."}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9689-8328","authenticated-orcid":true,"given":"Mengze","family":"Tian","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou, China."}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9253-3459","authenticated-orcid":true,"given":"Zaitian","family":"Gongye","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou, China."}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4090-8457","authenticated-orcid":true,"given":"Donglai","family":"Xue","sequence":"additional","affiliation":[{"name":"Huzhou Institute of Zhejiang University, Huzhou, China."}]},{"given":"Jiaxi","family":"Xing","sequence":"additional","affiliation":[{"name":"Huzhou Institute of Zhejiang University, Huzhou, China."}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5157-4205","authenticated-orcid":true,"given":"Qianhao","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou, China."},{"name":"Huzhou Institute of Zhejiang University, Huzhou, China."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0377-770X","authenticated-orcid":true,"given":"Yuman","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou, China."},{"name":"Huzhou Institute of Zhejiang University, Huzhou, China."}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2865-6517","authenticated-orcid":true,"given":"Jingping","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou, China."},{"name":"Huzhou Institute of Zhejiang University, Huzhou, China."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2759-6364","authenticated-orcid":true,"given":"Chao","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou, China."},{"name":"Huzhou Institute of Zhejiang University, Huzhou, China."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6513-374X","authenticated-orcid":true,"given":"Fei","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou, China."},{"name":"Huzhou Institute of Zhejiang University, Huzhou, China."}]}],"member":"221","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.psych.50.1.651"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1152\/jn.00033.2017"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","unstructured":"J. Wiener S. Shettleworth V. P. Bingman K. Cheng S. Healy L. F. Jacobs K. J. Jeffery H. A. Mallot R. Menzel N. S. Newcombe \u201cAnimal navigation: A synthesis\u201d in Animal Thinking: Contemporary Issues in Comparative Cognition (MIT Press 2011) pp. 51\u201376.","DOI":"10.7551\/mitpress\/9780262016636.003.0005"},{"key":"e_1_3_2_5_2","first-page":"76","article-title":"Robot planning in the real world: Research challenges and opportunities","volume":"37","author":"Alterovitz R.","year":"2016","unstructured":"R. Alterovitz, S. Koenig, M. Likhachev, Robot planning in the real world: Research challenges and opportunities. AI Mag. 37, 76\u201384 (2016).","journal-title":"AI Mag."},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature08499"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/nn1777"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2007.10.012"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1101\/lm.575107"},{"key":"e_1_3_2_10_2","first-page":"743","article-title":"A review on graph search algorithms for optimal energy efficient path planning for an unmanned air vehicle","volume":"15","author":"Debnath S. K.","year":"2019","unstructured":"S. K. Debnath, R. Omar, N. B. A. Latip, S. Shely, E. Nadira, C. K. N. C. K. Melor, T. K. Chakraborty, E. Natarajan, A review on graph search algorithms for optimal energy efficient path planning for an unmanned air vehicle. Indones. J. Electr. Eng. Comput. Sci. 15, 743\u2013749 (2019).","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","unstructured":"A.Bry N. Roy \u201cRapidly-exploring random belief trees for motion planning under uncertainty \u201d in Proceedings of the IEEE International Conference on Robotics and Automation (IEEE 2011) pp. 723\u2013730.","DOI":"10.1109\/ICRA.2011.5980508"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364909359210"},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","unstructured":"J. D. Gammell S. S. Srinivasa T. D. Barfoot \u201cInformed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic \u201d in Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IEEE 2014) pp. 2997\u20133004.","DOI":"10.1109\/IROS.2014.6942976"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364911406761"},{"key":"e_1_3_2_15_2","doi-asserted-by":"crossref","unstructured":"R. Pepy A. Lambert H. Mounier \u201cPath planning using a dynamic vehicle model \u201d in 2006 2nd International Conference on Information & Communication Technologies (IEEE 2006) vol. 1 pp. 781\u2013786.","DOI":"10.1109\/ICTTA.2006.1684472"},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","unstructured":"M. Phillips M. Likhachev \u201cSipp: Safe interval path planning for dynamic environments \u201d in 2011 IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2011) pp. 5628\u20135635.","DOI":"10.1109\/ICRA.2011.5980306"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3187270"},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","unstructured":"D. J. Webb J. van den Berg \u201cKinodynamic RRT*: Asymptotically optimal motion planning for robots with linear dynamics \u201d in 2013 IEEE International Conference on Robotics and Automation (IEEE 2013) pp. 5054\u20135061.","DOI":"10.1109\/ICRA.2013.6631299"},{"key":"e_1_3_2_19_2","doi-asserted-by":"crossref","unstructured":"J. Huh D. D. Lee V. Isler \u201cLearning continuous cost-to-go functions for non-holonomic systems \u201d in 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2011) pp. 5772\u20135779.","DOI":"10.1109\/IROS51168.2021.9636139"},{"key":"e_1_3_2_20_2","unstructured":"J. J. Johnson U. S. Kalra A. Bhatia L. Li A. H. Qureshi M. C. Yip Motion planning transformers: A motion planning framework for mobile robots. arXiv:2106.02791 [cs.RO] (2021)."},{"key":"e_1_3_2_21_2","doi-asserted-by":"crossref","unstructured":"J. J. Johnson L. Li F. Liu A. H. Qureshi M. C. Yip \u201cDynamically constrained motion planning networks for non-holonomic robots \u201d in 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2020) pp. 6937\u20136943.","DOI":"10.1109\/IROS45743.2020.9341283"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12555-022-0082-z"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3067847"},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","unstructured":"A. H. Qureshi A. Simeonov M. J. Bency M. C. Yip \u201cMotion planning networks \u201d in 2019 International Conference on Robotics and Automation (ICRA) (IEEE 2019) pp. 2118\u20132124.","DOI":"10.1109\/ICRA.2019.8793889"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2021.3121408"},{"key":"e_1_3_2_26_2","unstructured":"R. Yonetani T. Taniai M. Barekatain M. Nishimura A. Kanezaki \u201cPath planning using Neural A* search \u201d in Proceedings of the 38th International Conference on Machine Learning (MLResearchPress 2021) pp. 12029\u201312039."},{"key":"e_1_3_2_27_2","unstructured":"J. Ho A. Jain P. Abbeel \u201cDenoising diffusion probabilistic models \u201d in Advances in Neural Information Processing Systems 33 (NeurIPS 2020) H. Larochelle M. Ranzato R. Hadsell M. F. Balcan H. Lin Eds. (Curran Associates 2020) pp. 6840\u20136851."},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1177\/02783649241273668"},{"key":"e_1_3_2_29_2","doi-asserted-by":"crossref","unstructured":"S. Huang Z. Wang P. Li B. Jia T. Liu Y. Zhu W. Liang S.-C. Zhu \u201cDiffusion-based generation optimization and planning in 3D scenes \u201d in 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE 2023) pp. 16750\u201316761.","DOI":"10.1109\/CVPR52729.2023.01607"},{"key":"e_1_3_2_30_2","doi-asserted-by":"crossref","unstructured":"J. Carvalho A. T. Le M. Baierl D. Koert J. Peters \u201cMotion planning diffusion: Learning and planning of robot motions with diffusion models \u201d in 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2023) pp. 1916\u20131923.","DOI":"10.1109\/IROS55552.2023.10342382"},{"key":"e_1_3_2_31_2","doi-asserted-by":"crossref","unstructured":"J. Liu M. Stamatopoulou D. Kanoulas \u201cDipper: Diffusion-based 2D path planner applied on legged robots \u201d in 2024 IEEE International Conference on Robotics and Automation (ICRA) (IEEE 2024) pp. 9264\u20139270.","DOI":"10.1109\/ICRA57147.2024.10610013"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3421789"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3047728"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2023.03.001"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cobeha.2017.06.005"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2019.2923954"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2957797"},{"key":"e_1_3_2_38_2","doi-asserted-by":"crossref","unstructured":"K. Bergman D. Axehill \u201cCombining homotopy methods and numerical optimal control to solve motion planning problems \u201d in 2018 IEEE Intelligent Vehicles Symposium (IV) (IEEE 2018) pp. 347\u2013354.","DOI":"10.1109\/IVS.2018.8500644"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.04.016"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41315-019-00109-z"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3041075"},{"key":"e_1_3_2_42_2","doi-asserted-by":"crossref","unstructured":"X. Zhang A. Liniger A. Sakai F. Borrelli \u201cAutonomous parking using optimization-based collision avoidance \u201d in 2018 IEEE Conference on Decision and Control (CDC) (IEEE 2018) pp. 4327\u20134332.","DOI":"10.1109\/CDC.2018.8619433"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3315320"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2022.3160022"},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","unstructured":"B. J. Cohen S. Chitta M. Likhachev \u201cSearch-based planning for manipulation with motion primitives \u201d in 2010 IEEE International Conference on Robotics and Automation (IEEE 2010) pp. 2902\u20132908.","DOI":"10.1109\/ROBOT.2010.5509685"},{"key":"e_1_3_2_46_2","doi-asserted-by":"crossref","unstructured":"M. Pivtoraiko A. Kelly \u201cKinodynamic motion planning with state lattice motion primitives \u201d in 2011 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IEEE 2011) pp. 2172\u20132179.","DOI":"10.1109\/IROS.2011.6094900"},{"key":"e_1_3_2_47_2","unstructured":"A. Vaswani N. Shazeer N. Parmar J. Uszkoreit L. Jones A. N. Gomez \u0141. Kaiser I. Polosukhin \u201cAttention is all you need \u201d in Advances in Neural Information Processing Systems 30 (NIPS 2017) I. Guyon U. Von Luxburg S. Bengio H. Wallach R. Fergus S. Vishwanathan R. Garnett Eds. (Curran Associates 2017)."},{"key":"e_1_3_2_48_2","doi-asserted-by":"crossref","unstructured":"I. Lugo-C\u00e1rdenas G. Flores S. Salazar R. Lozano \u201cDubins path generation for a fixed wing UAV \u201d in 2014 International Conference on Unmanned Aircraft Systems (ICUAS) (IEEE 2014) pp. 339\u2013346.","DOI":"10.1109\/ICUAS.2014.6842272"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.2140\/pjm.1990.145.367"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2022.3141876"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1090\/S0002-9939-1956-0078686-7"},{"key":"e_1_3_2_52_2","doi-asserted-by":"crossref","unstructured":"J. Tobin R. Fong A. Ray J. Schneider W. Zaremba P. Abbeel \u201cDomain randomization for transferring deep neural networks from simulation to the real world \u201d in 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2017) pp. 23\u201330.","DOI":"10.1109\/IROS.2017.8202133"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abk2822"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.adg1462"},{"key":"e_1_3_2_55_2","doi-asserted-by":"crossref","unstructured":"M. Savva A. Kadian O. Maksymets Y. Zhao E. Wijmans B. Jain J. Straub J. Liu V. Koltun J. Malik D. Parikh D. Batra \u201cHabitat: A platform for embodied AI research \u201d in Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (IEEE 2019).","DOI":"10.1109\/ICCV.2019.00943"},{"key":"e_1_3_2_56_2","unstructured":"J. Song C. Meng S. Ermon Denoising diffusion implicit models. arXiv:2010.02502 [cs.LG] (2020)."},{"key":"e_1_3_2_57_2","unstructured":"G. Moore Cramming more components onto integrated circuits (1965)."},{"key":"e_1_3_2_58_2","doi-asserted-by":"crossref","unstructured":"R. M. Murray Z. Li S. Shankar Sastry A Mathematical Introduction to Robotic Manipulation (CRC Press 2017).","DOI":"10.1201\/9781315136370"},{"key":"e_1_3_2_59_2","doi-asserted-by":"crossref","unstructured":"L. Han F. Gao B. Zhou S. Shen \u201cFiesta: Fast incremental euclidean distance fields for online motion planning of aerial robots \u201d in 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE 2019) pp. 4423\u20134430.","DOI":"10.1109\/IROS40897.2019.8968199"},{"key":"e_1_3_2_60_2","doi-asserted-by":"crossref","unstructured":"K. He X. Zhang S. Ren J. Sun \u201cDeep residual learning for image recognition \u201d in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE 2016) pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_61_2","doi-asserted-by":"crossref","unstructured":"R. Girshick J. Donahue T. Darrell J. Malik \u201cRich feature hierarchies for accurate object detection and semantic segmentation \u201d in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE 2014) pp. 580\u2013587.","DOI":"10.1109\/CVPR.2014.81"},{"key":"e_1_3_2_62_2","doi-asserted-by":"crossref","unstructured":"F. Yang C. Wang C. Cadena M. Hutter iPlanner: Imperative path planning. arXiv:2302.11434 [cs.RO] (2023).","DOI":"10.15607\/RSS.2023.XIX.064"},{"key":"e_1_3_2_63_2","doi-asserted-by":"crossref","unstructured":"M. Tanelli M. Corno S. Saveresi Modelling Simulation and Control of Two-Wheeled Vehicles (John Wiley & Sons 2014).","DOI":"10.1002\/9781118536391"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1137\/0312021"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF01589116"},{"key":"e_1_3_2_66_2","first-page":"23","article-title":"Analysis of maze generating algorithms","volume":"15","author":"Gabrov\u0161ek P.","year":"2019","unstructured":"P. Gabrov\u0161ek, Analysis of maze generating algorithms. IPSI Trans. Internet Res. 15, 23\u201330 (2019).","journal-title":"IPSI Trans. Internet Res."},{"key":"e_1_3_2_67_2","unstructured":"P. H. Kim \u201cIntelligent maze generation \u201d thesis Ohio State University Columbus OH (2019)."},{"key":"e_1_3_2_68_2","doi-asserted-by":"crossref","unstructured":"A. Kozlova J. A. Brown E. Reading \u201cExamination of representational expression in maze generation algorithms \u201d in 2015 IEEE Conference on Computational Intelligence and Games (CIG) (IEEE 2015) pp. 532\u2013533.","DOI":"10.1109\/CIG.2015.7317902"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"e_1_3_2_70_2","doi-asserted-by":"crossref","unstructured":"T. A. Driscoll L. N. Trefethen Schwarz-Christoffel Mapping (Cambridge Univ. Press 2002).","DOI":"10.1017\/CBO9780511546808"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.abc5986"}],"container-title":["Science Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.science.org\/doi\/pdf\/10.1126\/scirobotics.ads4551","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T18:01:08Z","timestamp":1750269668000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.science.org\/doi\/10.1126\/scirobotics.ads4551"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,18]]},"references-count":70,"journal-issue":{"issue":"103","published-print":{"date-parts":[[2025,6,18]]}},"alternative-id":["10.1126\/scirobotics.ads4551"],"URL":"https:\/\/doi.org\/10.1126\/scirobotics.ads4551","relation":{},"ISSN":["2470-9476"],"issn-type":[{"value":"2470-9476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,18]]},"assertion":[{"value":"2024-08-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"eads4551"}}