{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:02:58Z","timestamp":1781366578254,"version":"3.54.1"},"reference-count":88,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Robotics and Autonomous Systems"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.robot.2026.105357","type":"journal-article","created":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T20:32:02Z","timestamp":1768681922000},"page":"105357","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Vision-driven river following of UAV via safe reinforcement learning using semantic dynamics model"],"prefix":"10.1016","volume":"198","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7527-5955","authenticated-orcid":false,"given":"Zihan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nina","family":"Mahmoudian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.robot.2026.105357_b1","first-page":"39","article-title":"Multi-rotor drone to fly autonomously along a river using a single-lens camera and image processing","volume":"4","author":"Taufik","year":"2015","journal-title":"Int. J. Mech. Eng."},{"issue":"13","key":"10.1016\/j.robot.2026.105357_b2","doi-asserted-by":"crossref","first-page":"4681","DOI":"10.3390\/s22134681","article-title":"ROSEBUD: A deep fluvial segmentation dataset for monocular vision-based river navigation and obstacle avoidance","volume":"22","author":"Lambert","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.robot.2026.105357_b3","doi-asserted-by":"crossref","first-page":"4755","DOI":"10.1109\/JSTARS.2023.3275068","article-title":"Aerial fluvial image dataset for deep semantic segmentation neural networks and its benchmarks","volume":"16","author":"Wang","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"10.1016\/j.robot.2026.105357_b4","series-title":"2024 IEEE\/RSJ International Conference on Intelligent Robots and Systems","first-page":"9976","article-title":"Synergistic reinforcement and imitation learning for vision-driven autonomous flight of UAV along river","author":"Wang","year":"2024"},{"key":"10.1016\/j.robot.2026.105357_b5","series-title":"A Bradford Book","article-title":"Reinforcement learning: An introduction","author":"Sutton","year":"2018"},{"key":"10.1016\/j.robot.2026.105357_b6","series-title":"Information gathering in decentralized POMDPs by policy graph improvement","author":"Lauri","year":"2019"},{"key":"10.1016\/j.robot.2026.105357_b7","series-title":"Submodular reinforcement learning","author":"Prajapat","year":"2023"},{"key":"10.1016\/j.robot.2026.105357_b8","series-title":"Constrained Markov Decision Processes","author":"Altman","year":"2021"},{"key":"10.1016\/j.robot.2026.105357_b9","series-title":"High-dimensional continuous control using generalized advantage estimation","author":"Schulman","year":"2015"},{"key":"10.1016\/j.robot.2026.105357_b10","series-title":"Visual semantic navigation using scene priors","author":"Yang","year":"2018"},{"key":"10.1016\/j.robot.2026.105357_b11","doi-asserted-by":"crossref","unstructured":"B. Mayo, T. Hazan, A. Tal, Visual navigation with spatial attention, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 16898\u201316907.","DOI":"10.1109\/CVPR46437.2021.01662"},{"issue":"3","key":"10.1016\/j.robot.2026.105357_b12","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1007\/s10846-021-01566-0","article-title":"A deep reinforcement learning approach with visual semantic navigation with memory for mobile robots in indoor home context","volume":"104","author":"Santos","year":"2022","journal-title":"J. Intell. Robot. Syst."},{"issue":"7","key":"10.1016\/j.robot.2026.105357_b13","doi-asserted-by":"crossref","first-page":"9019","DOI":"10.1007\/s11063-023-11190-8","article-title":"Double graph attention networks for visual semantic navigation","volume":"55","author":"Lyu","year":"2023","journal-title":"Neural Process. Lett."},{"key":"10.1016\/j.robot.2026.105357_b14","series-title":"Dream to control: Learning behaviors by latent imagination","author":"Hafner","year":"2019"},{"key":"10.1016\/j.robot.2026.105357_b15","series-title":"Mastering diverse domains through world models","author":"Hafner","year":"2023"},{"key":"10.1016\/j.robot.2026.105357_b16","series-title":"International Conference on Machine Learning","first-page":"2555","article-title":"Learning latent dynamics for planning from pixels","author":"Hafner","year":"2019"},{"key":"10.1016\/j.robot.2026.105357_b17","series-title":"Mastering atari with discrete world models","author":"Hafner","year":"2020"},{"key":"10.1016\/j.robot.2026.105357_b18","series-title":"Safe dreamerv3: Safe reinforcement learning with world models","author":"Huang","year":"2023"},{"key":"10.1016\/j.robot.2026.105357_b19","series-title":"International Conference on Machine Learning","first-page":"22","article-title":"Constrained policy optimization","author":"Achiam","year":"2017"},{"key":"10.1016\/j.robot.2026.105357_b20","first-page":"15338","article-title":"First order constrained optimization in policy space","volume":"33","author":"Zhang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"20","key":"10.1016\/j.robot.2026.105357_b21","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.ifacol.2024.10.090","article-title":"Vision-driven UAV river following: Benchmarking with safe reinforcement learning","volume":"58","author":"Wang","year":"2024","journal-title":"IFAC-PapersOnLine"},{"issue":"5","key":"10.1016\/j.robot.2026.105357_b22","doi-asserted-by":"crossref","first-page":"855","DOI":"10.3390\/math8050855","article-title":"Semantic segmentation to develop an indoor navigation system for an autonomous mobile robot","volume":"8","author":"Teso-Fz-Betono","year":"2020","journal-title":"Mathematics"},{"key":"10.1016\/j.robot.2026.105357_b23","series-title":"2019 IEEE 9th International Conference on Consumer Electronics","first-page":"174","article-title":"Vision-based road-following using results of semantic segmentation for autonomous navigation","author":"Miyamoto","year":"2019"},{"key":"10.1016\/j.robot.2026.105357_b24","series-title":"2021 IEEE International Conference on Robotics and Automation","first-page":"2450","article-title":"Semantically-aware strategies for stereo-visual robotic obstacle avoidance","author":"Hong","year":"2021"},{"key":"10.1016\/j.robot.2026.105357_b25","series-title":"Virtual-to-real: Learning to control in visual semantic segmentation","author":"Hong","year":"2018"},{"key":"10.1016\/j.robot.2026.105357_b26","series-title":"2020 IEEE International Conference on Robotics and Automation","first-page":"6411","article-title":"Simulation-based reinforcement learning for real-world autonomous driving","author":"Osi\u0144ski","year":"2020"},{"issue":"3","key":"10.1016\/j.robot.2026.105357_b27","doi-asserted-by":"crossref","first-page":"8138","DOI":"10.1109\/LRA.2022.3187278","article-title":"Ga-nav: Efficient terrain segmentation for robot navigation in unstructured outdoor environments","volume":"7","author":"Guan","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.robot.2026.105357_b28","series-title":"Vision-Based Unmanned Aerial Vehicle Navigation in Virtual Complex Environment using Deep Reinforcement Learning","author":"Liang","year":"2021"},{"issue":"3","key":"10.1016\/j.robot.2026.105357_b29","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s10846-022-01593-5","article-title":"Vision-based 2D navigation of unmanned aerial vehicles in riverine environments with imitation learning","volume":"104","author":"Wei","year":"2022","journal-title":"J. Intell. Robot. Syst."},{"key":"10.1016\/j.robot.2026.105357_b30","series-title":"Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics","first-page":"627","article-title":"A reduction of imitation learning and structured prediction to no-regret online learning","author":"Ross","year":"2011"},{"key":"10.1016\/j.robot.2026.105357_b31","series-title":"Unity: A general platform for intelligent agents","author":"Juliani","year":"2020"},{"key":"10.1016\/j.robot.2026.105357_b32","series-title":"Aerial fluvial image dataset (AFID) for semantic segmentation","author":"Wang","year":"2022"},{"issue":"3","key":"10.1016\/j.robot.2026.105357_b33","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1080\/09540099108946587","article-title":"Function optimization using connectionist reinforcement learning algorithms","volume":"3","author":"Williams","year":"1991","journal-title":"Connect. Sci."},{"key":"10.1016\/j.robot.2026.105357_b34","series-title":"International Conference on Machine Learning","first-page":"1407","article-title":"Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures","author":"Espeholt","year":"2018"},{"key":"10.1016\/j.robot.2026.105357_b35","series-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013"},{"issue":"106","key":"10.1016\/j.robot.2026.105357_b36","doi-asserted-by":"crossref","first-page":"eadt1497","DOI":"10.1126\/scirobotics.adt1497","article-title":"A review of learning-based dynamics models for robotic manipulation","volume":"10","author":"Ai","year":"2025","journal-title":"Sci. Robot."},{"key":"10.1016\/j.robot.2026.105357_b37","series-title":"2017 IEEE International Conference on Robotics and Automation","first-page":"2786","article-title":"Deep visual foresight for planning robot motion","author":"Finn","year":"2017"},{"key":"10.1016\/j.robot.2026.105357_b38","series-title":"International Conference on Machine Learning","first-page":"9133","article-title":"Responsive safety in reinforcement learning by pid lagrangian methods","author":"Stooke","year":"2020"},{"key":"10.1016\/j.robot.2026.105357_b39","series-title":"Learning for Dynamics and Control Conference","first-page":"97","article-title":"Joint synthesis of safety certificate and safe control policy using constrained reinforcement learning","author":"Ma","year":"2022"},{"key":"10.1016\/j.robot.2026.105357_b40","first-page":"7288","article-title":"Augmented proximal policy optimization for safe reinforcement learning","volume":"vol. 37, no. 6","author":"Dai","year":"2023"},{"key":"10.1016\/j.robot.2026.105357_b41","unstructured":"Y. Hogewind, T.D. Simao, T. Kachman, N. Jansen, Safe reinforcement learning from pixels using a stochastic latent representation, in: The Eleventh International Conference on Learning Representations, 2022."},{"key":"10.1016\/j.robot.2026.105357_b42","series-title":"Constrained policy optimization via Bayesian world models","author":"As","year":"2022"},{"issue":"1","key":"10.1016\/j.robot.2026.105357_b43","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1146\/annurev-control-042920-020211","article-title":"Safe learning in robotics: From learning-based control to safe reinforcement learning","volume":"5","author":"Brunke","year":"2022","journal-title":"Annu. Rev. Control. Robot. Auton. Syst."},{"key":"10.1016\/j.robot.2026.105357_b44","unstructured":"F.S. Roza, K. Roscher, S. G\u00fcnnemann, Towards Probabilistic Safety Guarantees for Model-Free Reinforcement Learning, in: 42nd International Conference on Computer Safety, Reliability and Security, SAFECOMP 2023, 2023."},{"key":"10.1016\/j.robot.2026.105357_b45","series-title":"2024 IEEE\/RSJ International Conference on Intelligent Robots and Systems","first-page":"10415","article-title":"Neural control barrier functions for safe navigation","author":"Harms","year":"2024"},{"key":"10.1016\/j.robot.2026.105357_b46","series-title":"2024 IEEE International Conference on Robotics and Automation","first-page":"11532","article-title":"How to train your neural control barrier function: Learning safety filters for complex input-constrained systems","author":"So","year":"2024"},{"key":"10.1016\/j.robot.2026.105357_b47","first-page":"5685","article-title":"Exact verification of relu neural control barrier functions","volume":"36","author":"Zhang","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.robot.2026.105357_b48","series-title":"Conference on Robot Learning","first-page":"1970","article-title":"Safe control under input limits with neural control barrier functions","author":"Liu","year":"2023"},{"key":"10.1016\/j.robot.2026.105357_b49","series-title":"2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems","first-page":"4552","article-title":"Model-based constrained reinforcement learning using generalized control barrier function","author":"Ma","year":"2021"},{"issue":"16","key":"10.1016\/j.robot.2026.105357_b50","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.ifacol.2021.10.118","article-title":"Maneuvering with safety guarantees using control barrier functions","volume":"54","author":"Marley","year":"2021","journal-title":"IFAC-PapersOnLine"},{"key":"10.1016\/j.robot.2026.105357_b51","series-title":"Learning control barrier functions and their application in reinforcement learning: A survey","author":"Guerrier","year":"2024"},{"key":"10.1016\/j.robot.2026.105357_b52","series-title":"Safe reinforcement learning via probabilistic logic shields","author":"Yang","year":"2023"},{"key":"10.1016\/j.robot.2026.105357_b53","series-title":"International Conference on Tools and Algorithms for the Construction and Analysis of Systems","first-page":"533","article-title":"Shield synthesis: Runtime enforcement for reactive systems","author":"Bloem","year":"2015"},{"key":"10.1016\/j.robot.2026.105357_b54","article-title":"Safe reinforcement learning via shielding","volume":"vol. 32, no. 1","author":"Alshiekh","year":"2018"},{"key":"10.1016\/j.robot.2026.105357_b55","doi-asserted-by":"crossref","DOI":"10.1016\/j.artint.2023.103987","article-title":"Risk-aware shielding of partially observable monte carlo planning policies","volume":"324","author":"Mazzi","year":"2023","journal-title":"Artificial Intelligence"},{"key":"10.1016\/j.robot.2026.105357_b56","article-title":"Constrained cross-entropy method for safe reinforcement learning","volume":"31","author":"Wen","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.robot.2026.105357_b57","series-title":"Robotics: Science and Systems","first-page":"1","article-title":"Safe reinforcement learning via statistical model predictive shielding.","author":"Bastani","year":"2021"},{"key":"10.1016\/j.robot.2026.105357_b58","series-title":"Dynamic model predictive shielding for provably safe reinforcement learning","author":"Banerjee","year":"2024"},{"key":"10.1016\/j.robot.2026.105357_b59","series-title":"ECAI 2023","first-page":"883","article-title":"Approximate model-based shielding for safe reinforcement learning","author":"Goodall","year":"2023"},{"key":"10.1016\/j.robot.2026.105357_b60","unstructured":"B. K\u00f6nighofer, R. Bloem, S. Junges, N. Jansen, A. Serban, Safe reinforcement learning using probabilistic shields, in: International Conference on Concurrency Theory: 31st CONCUR, 2020."},{"issue":"4","key":"10.1016\/j.robot.2026.105357_b61","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s11334-022-00480-4","article-title":"Online shielding for reinforcement learning","volume":"19","author":"K\u00f6nighofer","year":"2023","journal-title":"Innov. Syst. Softw. Eng."},{"issue":"9","key":"10.1016\/j.robot.2026.105357_b62","article-title":"Variance reduction techniques for gradient estimates in reinforcement learning","volume":"5","author":"Greensmith","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.robot.2026.105357_b63","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1023\/A:1022672621406","article-title":"Simple statistical gradient-following algorithms for connectionist reinforcement learning","volume":"8","author":"Williams","year":"1992","journal-title":"Mach. Learn."},{"key":"10.1016\/j.robot.2026.105357_b64","article-title":"Policy gradient methods for reinforcement learning with function approximation","volume":"12","author":"Sutton","year":"1999","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.robot.2026.105357_b65","series-title":"Variance reduction for score functions using optimal baselines","author":"Keane","year":"2022"},{"issue":"3","key":"10.1016\/j.robot.2026.105357_b66","doi-asserted-by":"crossref","first-page":"2346","DOI":"10.1109\/LRA.2018.2809549","article-title":"Unsupervised deep homography: A fast and robust homography estimation model","volume":"3","author":"Nguyen","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.robot.2026.105357_b67","series-title":"Parameterizing Homographies","author":"Baker","year":"2006"},{"key":"10.1016\/j.robot.2026.105357_b68","doi-asserted-by":"crossref","unstructured":"E. Riba, D. Mishkin, D. Ponsa, E. Rublee, G. Bradski, Kornia: an open source differentiable computer vision library for pytorch, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2020, pp. 3674\u20133683.","DOI":"10.1109\/WACV45572.2020.9093363"},{"key":"10.1016\/j.robot.2026.105357_b69","series-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"Chung","year":"2014"},{"key":"10.1016\/j.robot.2026.105357_b70","series-title":"International Conference on Computers and Games","first-page":"72","article-title":"Efficient selectivity and backup operators in Monte-Carlo tree search","author":"Coulom","year":"2006"},{"key":"10.1016\/j.robot.2026.105357_b71","article-title":"Imagination-augmented agents for deep reinforcement learning","volume":"30","author":"Racani\u00e8re","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.robot.2026.105357_b72","series-title":"Model-based value estimation for efficient model-free reinforcement learning","author":"Feinberg","year":"2018"},{"key":"10.1016\/j.robot.2026.105357_b73","series-title":"Averaging n-step returns reduces variance in reinforcement learning","author":"Daley","year":"2024"},{"key":"10.1016\/j.robot.2026.105357_b74","first-page":"12141","article-title":"Truncating trajectories in monte carlo policy evaluation: an adaptive approach","volume":"36","author":"Poiani","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.robot.2026.105357_b75","first-page":"2608","article-title":"Towards safe reinforcement learning with a safety editor policy","volume":"35","author":"Yu","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"268","key":"10.1016\/j.robot.2026.105357_b76","first-page":"1","article-title":"Stable-baselines3: Reliable reinforcement learning implementations","volume":"22","author":"Raffin","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.robot.2026.105357_b77","article-title":"Sample-efficient deep reinforcement learning via episodic backward update","volume":"32","author":"Lee","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.robot.2026.105357_b78","unstructured":"S. Kapturowski, G. Ostrovski, J. Quan, R. Munos, W. Dabney, Recurrent experience replay in distributed reinforcement learning, in: International Conference on Learning Representations, 2018."},{"key":"10.1016\/j.robot.2026.105357_b79","series-title":"Computer Aided Geometric Design","first-page":"317","article-title":"A class of local interpolating splines","author":"Catmull","year":"1974"},{"key":"10.1016\/j.robot.2026.105357_b80","series-title":"Gymnasium: A standard interface for reinforcement learning environments","author":"Towers","year":"2024"},{"key":"10.1016\/j.robot.2026.105357_b81","series-title":"Unity perception package","author":"Unity Technologies","year":"2020"},{"key":"10.1016\/j.robot.2026.105357_b82","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"issue":"285","key":"10.1016\/j.robot.2026.105357_b83","first-page":"1","article-title":"Omnisafe: An infrastructure for accelerating safe reinforcement learning research","volume":"25","author":"Ji","year":"2024","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.robot.2026.105357_b84","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.robot.2026.105357_b85","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.robot.2026.105357_b86","series-title":"Segmentation Models Pytorch","author":"Yakubovskiy","year":"2020"},{"key":"10.1016\/j.robot.2026.105357_b87","doi-asserted-by":"crossref","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","author":"Hochreiter","year":"1997","journal-title":"Neural Comput. MIT-Press"},{"key":"10.1016\/j.robot.2026.105357_b88","series-title":"NIPS","article-title":"Attention is all you need","author":"Waswani","year":"2017"}],"container-title":["Robotics and Autonomous Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0921889026000308?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0921889026000308?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T16:33:00Z","timestamp":1770481980000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0921889026000308"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":88,"alternative-id":["S0921889026000308"],"URL":"https:\/\/doi.org\/10.1016\/j.robot.2026.105357","relation":{},"ISSN":["0921-8890"],"issn-type":[{"value":"0921-8890","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Vision-driven river following of UAV via safe reinforcement learning using semantic dynamics model","name":"articletitle","label":"Article Title"},{"value":"Robotics and Autonomous Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.robot.2026.105357","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"105357"}}