{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T09:53:14Z","timestamp":1781603594938,"version":"3.54.5"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"34","license":[{"start":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T00:00:00Z","timestamp":1726272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T00:00:00Z","timestamp":1726272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100010667","name":"H2020 Industrial Leadership","doi-asserted-by":"publisher","award":["956573"],"award-info":[{"award-number":["956573"]}],"id":[{"id":"10.13039\/100010667","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Methods for socially-aware robot path planning are increasingly needed as robots and humans increasingly coexist in shared industrial spaces. The practice of clearly separated zones for humans and robots in shop floors is transitioning towards spaces where both humans and robot operate, often collaboratively. To allow for safer and more efficient manufacturing operations in shared workspaces, mobile robot fleet path planning needs to predict human movement. Accounting for the spatiotemporal nature of the problem, the present work introduces a spatiotemporal graph neural network approach that uses graph convolution and gated recurrent units, together with an attention mechanism to capture the spatial and temporal dependencies in the data and predict human occupancy based on past observations. The obtained results indicate that the graph network-based approach is suitable for short-term predictions but the rising uncertainty beyond short-term would limit its applicability. Furthermore, the addition of learnable edge weights, a feature exclusive to graph neural networks, enhances the predictive capabilities of the model. Adding workspace context-specific embeddings to graph nodes has additionally been explored, bringing modest performance improvements. Further research is needed to extend the predictive capabilities beyond the range of scenarios captured through the original training, and towards establishing standardised benchmarks for testing human motion prediction in industrial environments.<\/jats:p>","DOI":"10.1007\/s00521-024-10369-x","type":"journal-article","created":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T09:02:25Z","timestamp":1726304545000},"page":"21743-21759","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Graph network-based human movement prediction for socially-aware robot navigation in shared workspaces"],"prefix":"10.1007","volume":"36","author":[{"given":"Casper","family":"Dik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4335-6915","authenticated-orcid":false,"given":"Christos","family":"Emmanouilidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bertrand","family":"Duqueroie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,14]]},"reference":[{"key":"10369_CR1","unstructured":"Addad, B., Cavalletti, C., Duqueroie, B., Fojud, A., Lorin, S., & Tumilowicz, A. (2022, June 6). Digital Twins and Data Analysis for Crowd Management in High Capacity Stations. WCRR\u201922, 13th World Conference on Railway Research."},{"issue":"7","key":"10369_CR2","doi-asserted-by":"publisher","first-page":"485","DOI":"10.3390\/ijgi10070485","volume":"10","author":"J Bai","year":"2021","unstructured":"Bai J, Zhu J, Song Y, Zhao L, Hou Z, Du R, Li H (2021) A3T-GCN: attention temporal graph convolutional network for traffic forecasting. ISPRS Int J Geo-Information 10(7):485. https:\/\/doi.org\/10.3390\/ijgi10070485","journal-title":"ISPRS Int J Geo-Information"},{"key":"10369_CR3","doi-asserted-by":"publisher","unstructured":"Bartoli, F., Lisanti, G., Ballan, L., & Bimbo, A. del. (2018). Context-Aware Trajectory Prediction; Context-Aware Trajectory Prediction. In 2018 24th International Conference on Pattern Recognition (ICPR). https:\/\/doi.org\/10.1109\/ICPR.2018.8545447","DOI":"10.1109\/ICPR.2018.8545447"},{"key":"10369_CR4","doi-asserted-by":"publisher","first-page":"102022","DOI":"10.1016\/j.rcim.2020.102022","volume":"67","author":"ZM Bi","year":"2021","unstructured":"Bi ZM, Luo C, Miao Z, Bing Zhang WJ, Zhang LW (2021) Safety assurance mechanisms of collaborative robotic systems in manufacturing. Robot Computer-Integr Manuf 67:102022. https:\/\/doi.org\/10.1016\/j.rcim.2020.102022","journal-title":"Robot Computer-Integr Manuf"},{"key":"10369_CR5","unstructured":"Choi, C., Malla, S., Patil, A., & Choi, J. H. (2019). DROGON: A Trajectory Prediction Model based on Intention-Conditioned Behavior Reasoning. http:\/\/arxiv.org\/abs\/1908.00024"},{"key":"10369_CR6","unstructured":"Eiffert, S., & Sukkarieh, S. (2019). Predicting Responses to a Robot\u2019s Future Motion using Generative Recurrent Neural Networks. CoRR, abs\/1909.13486. http:\/\/arxiv.org\/abs\/1909.13486"},{"issue":"2","key":"10369_CR7","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1002\/rob.20109","volume":"23","author":"D Ferguson","year":"2006","unstructured":"Ferguson D, Stentz A (2006) Using interpolation to improve path planning the field D* algorithm. J Field Robot 23(2):79\u2013101. https:\/\/doi.org\/10.1002\/rob.20109","journal-title":"J Field Robot"},{"issue":"5","key":"10369_CR8","doi-asserted-by":"publisher","first-page":"4282","DOI":"10.1103\/PhysRevE.51.4282","volume":"51","author":"D Helbing","year":"1995","unstructured":"Helbing D, Molnar P (1995) Social force model for pedestrian dynamics. Phys Rev E 51(5):4282\u20134286. https:\/\/doi.org\/10.1103\/PhysRevE.51.4282","journal-title":"Phys Rev E"},{"issue":"15\u201316","key":"10369_CR9","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1080\/01691864.2019.1636714","volume":"33","author":"A Hentout","year":"2019","unstructured":"Hentout A, Aouache M, Maoudj A, Akli I (2019) Human\u2013robot interaction in industrial collaborative robotics: a literature review of the decade 2008\u20132017. Adv Robot 33(15\u201316):764\u2013799. https:\/\/doi.org\/10.1080\/01691864.2019.1636714","journal-title":"Adv Robot"},{"key":"10369_CR10","doi-asserted-by":"publisher","first-page":"81546","DOI":"10.1109\/ACCESS.2022.3194146","volume":"10","author":"M Kamezaki","year":"2022","unstructured":"Kamezaki M, Kobayashi A, Kono R, Hirayama M, Sugano S (2022) Dynamic waypoint navigation: model-based adaptive trajectory planner for human-symbiotic mobile robots. IEEE Access 10:81546\u201381555. https:\/\/doi.org\/10.1109\/ACCESS.2022.3194146","journal-title":"IEEE Access"},{"issue":"2","key":"10369_CR11","doi-asserted-by":"publisher","first-page":"3922","DOI":"10.1109\/LRA.2022.3148451","volume":"7","author":"M Kamezaki","year":"2022","unstructured":"Kamezaki M, Tsuburaya Y, Kanada T, Hirayama M, Sugano S (2022) Reactive, proactive, and inducible proximal crowd robot navigation method based on inducible social force model. IEEE Robot Autom Letters 7(2):3922\u20133929. https:\/\/doi.org\/10.1109\/LRA.2022.3148451","journal-title":"IEEE Robot Autom Letters"},{"key":"10369_CR12","doi-asserted-by":"publisher","first-page":"3277","DOI":"10.1109\/ICRA40945.2020.9197434","volume":"2020","author":"KD Katyal","year":"2020","unstructured":"Katyal KD, Hager GD, Huang C-M (2020) Intent-aware pedestrian prediction for adaptive crowd navigation. IEEE Int Conf on Robot Autom (ICRA) 2020:3277\u20133283. https:\/\/doi.org\/10.1109\/ICRA40945.2020.9197434","journal-title":"IEEE Int Conf on Robot Autom (ICRA)"},{"key":"10369_CR13","unstructured":"Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2017). Graph Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. CoRR, abs\/1707.01926. http:\/\/arxiv.org\/abs\/1707.01926"},{"key":"10369_CR14","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.jmsy.2017.04.009","volume":"44","author":"H Liu","year":"2017","unstructured":"Liu H, Wang L (2017) Human motion prediction for human-robot collaboration. J Manuf Syst 44:287\u2013294. https:\/\/doi.org\/10.1016\/j.jmsy.2017.04.009","journal-title":"J Manuf Syst"},{"key":"10369_CR15","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1109\/ROBOT.2010.5509779","volume":"2010","author":"M Luber","year":"2010","unstructured":"Luber M, Stork JA, Tipaldi GD, Arras KO (2010) People tracking with human motion predictions from social forces. IEEE Int Conf on Robot Autom 2010:464\u2013469. https:\/\/doi.org\/10.1109\/ROBOT.2010.5509779","journal-title":"IEEE Int Conf on Robot Autom"},{"key":"10369_CR16","doi-asserted-by":"publisher","first-page":"14412","DOI":"10.1109\/CVPR42600.2020.01443","volume":"2020","author":"A Mohamed","year":"2020","unstructured":"Mohamed A, Qian K, Elhoseiny M, Claudel C (2020) Social-STGCNN: a social spatio-temporal graph convolutional neural network for human trajectory prediction. IEEE\/CVF Conf Computer Vision Pattern Recognit (CVPR) 2020:14412\u201314420. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01443","journal-title":"IEEE\/CVF Conf Computer Vision Pattern Recognit (CVPR)"},{"key":"10369_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2021.103837","author":"R M\u00f6ller","year":"2021","unstructured":"M\u00f6ller R, Furnari A, Battiato S, H\u00e4rm\u00e4 A, Farinella GM (2021) A survey on human-aware robot navigation. Robot Auton Sys. https:\/\/doi.org\/10.1016\/j.robot.2021.103837","journal-title":"Robot Auton Sys"},{"key":"10369_CR18","unstructured":"Navarro, L., Flacher, F., & Meyer, C. (2015). SE-Star: A Large-Scale Human Behavior Simulation for Planning, Decision-Making and Training. In R. Bordini, E. Elkind, G. Weiss, & P. Yolum (Eds.), AAMAS\u201915: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems (pp. 1939\u20131940)."},{"key":"10369_CR19","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-030-11015-4_16","volume-title":"Computer Vision\u2014ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part III","author":"N Nikhil","year":"2019","unstructured":"Nikhil N, Morris BT (2019) Convolutional Neural Network for Trajectory Prediction. In: Leal-Taix\u00e9 L, Roth S (eds) Computer Vision\u2014ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part III. Springer, Cham, pp 186\u2013196. https:\/\/doi.org\/10.1007\/978-3-030-11015-4_16"},{"issue":"6","key":"10369_CR20","doi-asserted-by":"publisher","first-page":"4838","DOI":"10.1609\/aaai.v35i6.16616","volume":"35","author":"G Panagopoulos","year":"2021","unstructured":"Panagopoulos G, Nikolentzos G, Vazirgiannis M (2021) Transfer Graph Neural Networks for Pandemic Forecasting. Proc of the AAAI Conf on Artificial Int 35(6):4838\u20134845. https:\/\/doi.org\/10.1609\/aaai.v35i6.16616","journal-title":"Proc of the AAAI Conf on Artificial Int"},{"issue":"20","key":"10369_CR21","doi-asserted-by":"publisher","first-page":"6847","DOI":"10.1080\/00207543.2022.2138611","volume":"61","author":"JM Ro\u017eanec","year":"2022","unstructured":"Ro\u017eanec JM, Novalija I, Zajec P, Kenda K, Ghinani HT, Suh S, Veliou E, Papamartzivanos D, Giannetsos T, Menesidou SA, Alonso R, Cauli N, Meloni A, Recupero DR, Kyriazis D, Sofianidis G, Theodoropoulos S, Fortuna B, Mladeni\u0107 D, Soldatos J (2022) Human-centric artificial intelligence architecture for industry 5.0 applications. Int J Prod Res 61(20):6847\u20136872. https:\/\/doi.org\/10.1080\/00207543.2022.2138611","journal-title":"Int J Prod Res"},{"key":"10369_CR22","doi-asserted-by":"publisher","unstructured":"Rozemberczki, B., Scherer, P., He, Y., Panagopoulos, G., Riedel, A., Astefanoaei, M., Kiss, O., Beres, F., L\u00f3pez, G., Collignon, N., Sarkar, R. (2021). PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models. Proceedings of the 30th ACM International Conference on Information and; Knowledge Management, 4564\u20134573. https:\/\/doi.org\/10.1145\/3459637.3482014","DOI":"10.1145\/3459637.3482014"},{"issue":"8","key":"10369_CR23","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1177\/0278364920917446","volume":"39","author":"A Rudenko","year":"2020","unstructured":"Rudenko A, Palmieri L, Herman M, Kitani KM, Gavrila DM, Arras KO (2020) Human motion trajectory prediction: a survey. The Int J Robot Res 39(8):895\u2013935. https:\/\/doi.org\/10.1177\/0278364920917446","journal-title":"The Int J Robot Res"},{"key":"10369_CR24","doi-asserted-by":"publisher","unstructured":"Rudenko A, Palmieri L, Huang W, Lilienthal AJ, Arras KO (2022) The Atlas Benchmark: An Automated Evaluation Framework for Human Motion Prediction. IEEE, pp. 636\u2013643. 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 636\u2013643. https:\/\/doi.org\/10.1109\/RO-MAN53752.2022.9900656","DOI":"10.1109\/RO-MAN53752.2022.9900656"},{"issue":"4","key":"10369_CR25","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1109\/TIV.2018.2873901","volume":"3","author":"K Saleh","year":"2018","unstructured":"Saleh K, Hossny M, Nahavandi S (2018) Intent Prediction of Pedestrians via Motion Trajectories Using Stacked Recurrent Neural Networks. IEEE Trans Intell Veh 3(4):414\u2013424. https:\/\/doi.org\/10.1109\/TIV.2018.2873901","journal-title":"IEEE Trans Intell Veh"},{"key":"10369_CR26","doi-asserted-by":"publisher","DOI":"10.23915\/distill.00033","author":"B Sanchez-Lengeling","year":"2021","unstructured":"Sanchez-Lengeling B, Reif E, Pearce A, Wiltschko AB (2021) A Gentle Introduction to Graph Neural Networks. Distill. https:\/\/doi.org\/10.23915\/distill.00033","journal-title":"Distill"},{"key":"10369_CR27","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-642-19457-3_1","volume-title":"Robotics Research","author":"Jur van den Berg","year":"2011","unstructured":"van den Berg Jur, Guy Stephen J, Lin Ming, Manocha Dinesh (2011) Reciprocal n-body Collision Avoidance. In: Pradalier C, Siegwart R, Hirzinger G (eds) Robotics Research. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 3\u201319. https:\/\/doi.org\/10.1007\/978-3-642-19457-3_1"},{"key":"10369_CR28","unstructured":"Vemula, A., M\u00fclling, K., & Oh, J. (2017). Social Attention: Modeling Attention in Human Crowds. CoRR, abs\/1710.04689. http:\/\/arxiv.org\/abs\/1710.04689"},{"issue":"4","key":"10369_CR29","doi-asserted-by":"publisher","first-page":"6932","DOI":"10.1109\/LRA.2020.3026638","volume":"5","author":"B Wang","year":"2020","unstructured":"Wang B, Liu Z, Li Q, Prorok A (2020) Mobile Robot Path Planning in Dynamic Environments Through Globally Guided Reinforcement Learning. IEEE Robot Autom Lett 5(4):6932\u20136939. https:\/\/doi.org\/10.1109\/LRA.2020.3026638","journal-title":"IEEE Robot Autom Lett"},{"key":"10369_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2023.102608","author":"S Wang","year":"2024","unstructured":"Wang S, Zhang J, Wang P, Law J, Calinescu R, Mihaylova L (2024) A deep learning-enhanced Digital Twin framework for improving safety and reliability in human\u2013robot collaborative manufacturing. Robotics and Computer-Integrated Manufacturing. https:\/\/doi.org\/10.1016\/j.rcim.2023.102608","journal-title":"Robotics and Computer-Integrated Manufacturing"},{"issue":"1","key":"10369_CR31","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2021) A Comprehensive Survey on Graph Neural Networks. IEEE Trans Neural Networks Learning Sys 32(1):4\u201324. https:\/\/doi.org\/10.1109\/TNNLS.2020.2978386","journal-title":"IEEE Trans Neural Networks Learning Sys"},{"issue":"9","key":"10369_CR32","doi-asserted-by":"publisher","first-page":"4354","DOI":"10.1109\/TIP.2016.2590322","volume":"25","author":"S Yi","year":"2016","unstructured":"Yi S, Li H, Wang X (2016) Pedestrian Behavior Modeling from Stationary Crowds with Applications to Intelligent Surveillance. IEEE Trans Image Process 25(9):4354\u20134368. https:\/\/doi.org\/10.1109\/TIP.2016.2590322","journal-title":"IEEE Trans Image Process"},{"key":"10369_CR33","unstructured":"Yu, B., Yin, H., & Zhu, Z. (2017). Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting. CoRR, abs\/1709.04875. http:\/\/arxiv.org\/abs\/1709.04875"},{"key":"10369_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105236","author":"B Zhang","year":"2022","unstructured":"Zhang B, Wang T, Zhou C, Conci N, Liu H (2022) Human trajectory forecasting using a flow-based generative model. Engineering Applications of Artificial Intelligence. https:\/\/doi.org\/10.1016\/j.engappai.2022.105236","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"9","key":"10369_CR35","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2020","unstructured":"Zhao L, Song Y, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H (2020) T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. IEEE Trans Intell Transp Syst 21(9):3848\u20133858. https:\/\/doi.org\/10.1109\/TITS.2019.2935152","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10369_CR36","doi-asserted-by":"publisher","first-page":"12118","DOI":"10.1109\/CVPR.2019.01240","volume":"2019","author":"T Zhao","year":"2019","unstructured":"Zhao T, Xu Y, Monfort M, Choi W, Baker C, Zhao Y, Wang Y, Wu YN (2019) Multi-Agent Tensor Fusion for Contextual Trajectory Prediction. IEEE\/CVF Conf Computer Vision and Pattern Recognition (CVPR) 2019:12118\u201312126. https:\/\/doi.org\/10.1109\/CVPR.2019.01240","journal-title":"IEEE\/CVF Conf Computer Vision and Pattern Recognition (CVPR)"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10369-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10369-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10369-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T09:10:48Z","timestamp":1732353048000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10369-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,14]]},"references-count":36,"journal-issue":{"issue":"34","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["10369"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10369-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,14]]},"assertion":[{"value":"26 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2024","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 no financial or non-financial interests are in anyway, directly or indirectly, related to the submitted work. This work was supported through H2020 project STAR, grant ID 956573.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}