Numerical solutions to Zermelo’s navigation problem under variable ocean current fields
Year 2026,
Volume: 15 Issue: 1
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37
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52
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31.03.2026
Vahit Çalışır
Abstract
Zermelo’s navigation problem seeks the time-optimal heading strategy for a vessel of fixed speed navigating through a variable ocean current field. This paper reformulates the problem as an optimal control problem governed by the dynamic Hamilton-Jacobi-Bellman (HJB) equation and implements two complementary numerical solvers: a grid-based Eulerian level-set scheme (Method I) and a Lagrangian extremal field algorithm (Method II). Convergence is analysed within the viscosity solution framework, yielding (ℎ1/2) and (ℎ) rate bounds under Lipschitz and semiconcavity conditions, respectively. Grid refinement studies on a Rankine vortex and a double-gyre circulation confirm that Method I achieves 𝑂(ℎ3/2) in smooth regimes and degrades to 𝑂(ℎ0.48) under strong currents, while Method II reaches 𝑂(ℎ1.00) and 𝑂(ℎ0.76) in the same settings. Globally optimal 15-day routes for an Adriatic Sea mission are computed in under one minute on a standard workstation, confirming operational feasibility. This study presents a direct comparison of Eulerian and Lagrangian numerical formulations for Zermelo’s navigation problem and analyses their convergence behaviour within the viscosity solution framework. The results provide practical guidance for numerical method selection and grid resolution in time-optimal ship routing problems.
Ethical Statement
For this type of study, formal consent is not required.
Supporting Institution
This study was carried out without financial support from any institution.
Thanks
This work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Oceanographic velocity data for the Adriatic Sea benchmark were obtained from the Mediterranean Sea Physics Reanalysis product distributed by the Copernicus Marine Environment Monitoring Service (CMEMS), dataset identifier MEDSEA_MULTIYEAR_PHY_006_004; the specific mission scenario follows the configuration of Lolla et al. (2014). Computational experiments were performed on a standard workstation (Intel Core i7, 16 GB RAM) without dedicated HPC resources.
References
-
Alan, A. R., & Bayındır, C. (2026). The predictability of oceanic circulations via FFT-ANFIS spectral adaptive fuzzy network. In D. Baleanu, N. Özdemir, Y. Karaca, M. N. Janardhanan, F. Eviergen & I. Küçükkoç (Eds.), Nonlinear dynamical control, computer simulation and optimization systems: Theory and applications (pp. 53-62). World Scientific Publishing Co. https://doi.org/10.1142/9789819815432_0006
-
Alan, A. R., Bayındır, C., Özaydın, F., & Altıntaş, A. A. (2023). The predictability of the 30 October 2020 İzmir–Samos tsunami hydrodynamics and enhancement of its early warning time by LSTM deep learning network. Water, 15(23), 4195. https://doi.org/10.3390/w15234195
-
Aldea, N., & Kopacz, P. (2021). Generalized loxodromes with application to time-optimal navigation in arbitrary wind. Journal of the Franklin Institute, 358(1), 776-799. https://doi.org/10.1016/j.jfranklin.2020.11.009
-
Aldea, N., & Kopacz, P. (2025). Time geodesics on a slippery cross slope under gravitational wind. Nonlinear Analysis: Real World Applications, 81, 104177. https://doi.org/10.1016/j.nonrwa.2024.104177
-
Bao, D., Robles, C., & Shen, Z. (2004). Zermelo navigation on Riemannian manifolds. Journal of Differential Geometry, 66(3), 377-435. https://doi.org/10.4310/jdg/1098137838
-
Bardi, M., & Capuzzo-Dolcetta, I. (2008). Optimal control and viscosity solutions of Hamilton-Jacobi-Bellman equations [Reprint of the 1997 edition]. Birkhäuser Boston. https://doi.org/10.1007/978-0-8176-4755-1
-
Bayındır, C., Ozaydin, F., Altintas, A. A., Eristi, T., & Alan, A. R. (2024). Lagrangian drifter path identification and prediction: SINDy vs neural ODE. arXiv preprint 2411.04350. https://doi.org/10.48550/arXiv.2411.04350
-
Bayen, T., Bouali, A., Bourdin, L., & Cots, O. (2024). Loss control regions in optimal control problems. Journal of Differential Equations, 405, 359-397. https://doi.org/10.1016/j.jde.2024.06.016
-
Bonnard, B., Cots, O., Gergaud, J., & Wembe, B. (2022). Abnormal geodesics in 2D-Zermelo navigation problems in the case of revolution and the fan shape of the small time balls. Systems & Control Letters, 161, 105140. https://doi.org/10.1016/j.sysconle.2022.105140
-
Bonnard, B., Cots, O., & Wembe, B. (2023). Zermelo navigation problems on surfaces of revolution and geometric optimal control. ESAIM: Control, Optimisation and Calculus of Variations, 29, 60. https://doi.org/10.1051/cocv/2023052
-
Brody, D. C., Gibbons, G. W., & Meier, D. M. (2016). A Riemannian approach to Randers geodesics. Journal of Geometry and Physics, 106, 98-101. https://doi.org/10.1016/j.geomphys.2016.03.019
-
Chu, S., Feng, H., Ou, Y., Lin, M., & Li, D. (2026). FINDER: Flow-aware intelligent navigation through distilled experience and reinforcement learning for UUVs in complex flow fields. Ocean Engineering, 343, 123188. https://doi.org/10.1016/j.oceaneng.2025.123188
-
Garau, B., Alvarez, A., & Oliver, G. (2005). Path planning of autonomous underwater vehicles in current fields with complex spatial variability: An A* approach. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Spain. pp. 194-198. https://doi.org/10.1109/ROBOT.2005.1570118
-
Hao, S., Ma, W., Han, Y., Zheng, H., & Ma, D. (2023). Optimal path planning of unmanned surface vehicle under current environment. Ocean Engineering, 286, 115591. https://doi.org/10.1016/j.oceaneng.2023.115591
-
Kong, Y., Xiong, C., Zhang, X., Xu, F., Zhang, C., & Song, L. (2025). Optimized ship path planning integrating historical trajectory features with environmental constraints in port waterway. Ocean Engineering, 340, 122399. https://doi.org/10.1016/j.oceaneng.2025.122399
-
Kopacz, P. (2014). Application of codimension one foliation in Zermelo’s problem on Riemannian manifolds. Differential Geometry and its Applications, 35, 334-349. https://doi.org/10.1016/j.difgeo.2014.04.007
-
Lai, J., Ren, Z., Wu, Z., Tan, Q., & Xie, S. (2025). Learning-based real-time optimal control of unmanned surface vessels in dynamic environment with obstacles. Ocean Engineering, 335, 121505. https://doi.org/10.1016/j.oceaneng.2025.121505
-
Lolla, T., Lermusiaux, P. F. J., Ueckermann, M. P., & Haley, P. J. (2014). Time-optimal path planning in dynamic flows using level set equations: Theory and schemes. Ocean Dynamics, 64, 1373-1397. https://doi.org/10.1007/s10236-014-0757-y
-
Markvorsen, S. (2016). A Finsler geodesic spray paradigm for wildfire spread modelling. Nonlinear Analysis: Real World Applications, 28, 208-228. https://doi.org/10.1016/j.nonrwa.2015.09.011
-
Mirebeau, J.-M. (2019). Riemannian fast-marching on Cartesian grids, using Voronoi’s first reduction of quadratic forms. SIAM Journal on Numerical Analysis, 57(6), 2608-2655. https://doi.org/10.1137/17M1127466
-
Rhoads, B., Mezic, I., & Poje, A. (2010). Minimum time feedback control of autonomous underwater vehicles. Proceedings of the 49th IEEE Conference on Decision and Control, Atlanta, USA. pp. 5828-5834. https://doi.org/10.1109/CDC.2010.5717576
-
Robinson, A. R. (1996). Physical processes, field estimation and interdisciplinary ocean modeling. Earth-Science Reviews, 40(1-2), 3-54. https://doi.org/10.1016/0012-8252(95)00030-5
-
Sethian, J. A., & Vladimirsky, A. (2001). Ordered upwind methods for static Hamilton-Jacobi equations. Proceedings of the National Academy of Sciences, 98(20), 11069-11074. https://doi.org/10.1073/pnas.201222998
-
Shadden, S. C., Lekien, F., & Marsden, J. E. (2005). Definition and properties of Lagrangian coherent structures from finite-time Lyapunov exponents in two-dimensional aperiodic flows. Physica D: Nonlinear Phenomena, 212(3-4), 271-304. https://doi.org/10.1016/j.physd.2005.10.007
-
Sun, Y., Gu, R., Chen, X., Sun, R., Xin, L., & Bai, L. (2022). Efficient time-optimal path planning of AUV under the ocean currents based on graph and clustering strategy. Ocean Engineering, 259, 111907. https://doi.org/10.1016/j.oceaneng.2022.111907
-
Théodoridès, P. (1938). La navigation aérienne par vent variable. Bulletin Mathématique de La Société Roumaine Des Sciences, 40(1/2), 39-54.
-
Tonti, F., Rabault, J., & Vinuesa, R. (2025). Navigation in a simplified urban flow through deep reinforcement learning. Journal of Computational Physics, 538, 114194. https://doi.org/10.1016/j.jcp.2025.114194
-
Xia, Q. (2013). On the flag curvature of a class of Randers metric generated from the navigation problem. Journal of Mathematical Analysis and Applications, 397(1), 415-427. https://doi.org/10.1016/j.jmaa.2012.07.035
-
Xiao, Y., Huang, Y., Zhang, Y., & Wang, H. (2025). Local weather routing in avoidance of adverse sea conditions based on reachability theory. Ocean Engineering, 315, 119834. https://doi.org/10.1016/j.oceaneng.2024.119834
-
Zermelo, E. (1931). Über das navigationsproblem bei ruhender oder veränderlicher windverteilung. ZAMM – Journal of Applied Mathematics and MechanicsZeitschrift für angewandte Mathematik und Mechanik, 11(2), 114-124. https://doi.org/10.1002/zamm.19310110205
-
Zhang, M., Kim, D., Tezdogan, T., & Yuan, Z.-M. (2024). Time-optimal control of ship manoeuvring under wave loads. Ocean Engineering, 293, 116627. https://doi.org/10.1016/j.oceaneng.2023.116627
-
Zhang, M., Yu, S.-R., Chung, K. S., Chen, M.-L., & Yuan, Z.-M. (2023). Time-optimal path planning and tracking based on nonlinear model predictive control and its application on automatic berthing. Ocean Engineering, 286, 115228. https://doi.org/10.1016/j.oceaneng.2023.115228
-
Zhang, Y., Zheng, S., Xu, C., & Cai, S. (2025a). Efficient navigation in vortical flows based on reinforcement learning and flow field prediction. Ocean Engineering, 327, 120937. https://doi.org/10.1016/j.oceaneng.2025.120937
-
Zhang, Y., Wang, J., Wang, J., & Wang, H. (2025b). Time-optimal trajectory planning for unmanned ships along a specified path based on the phase plane. Journal of Ocean Engineering and Science. In press. https://doi.org/10.1016/j.joes.2025.12.010