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arxiv: 2606.12349 · v1 · pith:EY35DACInew · submitted 2026-06-10 · 💻 cs.RO · cs.SY· eess.SY

Traceable Virtual Sea Trials in the Marine Robotics Unity Simulator for Manoeuvring Assessment of Unmanned Surface Vehicles

Paria Rezayan This is my paper

Pith reviewed 2026-06-27 09:55 UTC · model grok-4.3

classification 💻 cs.RO cs.SYeess.SY
keywords virtual sea trialsunmanned surface vehiclesmanoeuvring assessmentsystem identificationhydrodynamic derivativesturning circlezig-zag manoeuvres
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The pith

A virtual sea trial framework produces traceable and repeatable manoeuvre data for unmanned surface vehicles in simulation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper introduces a standardised workflow for executing turning circle and zig-zag manoeuvres in a marine robotics simulator. The framework includes traceable command logging, data conditioning for system identification, and automated extraction of metrics aligned with international assessment standards. It aims to overcome the limitations of physical sea trials by providing auditable datasets that support hydrodynamic derivative estimation and digital twin development. Case study results show low variation between port and starboard turns and overshoot angles that satisfy compliance criteria.

Core claim

The dedicated TC/ZZ data acquisition and post-processing pipeline improves repeatability and auditability of simulator-based manoeuvres while producing SI-ready datasets for hydrodynamic-derivative identification and digital-twin workflows, with explicit separation of command inputs from realised actuation for differential-thrust steering.

What carries the argument

The TC/ZZ data acquisition and post-processing pipeline that handles traceable command-actuation logging and automated extraction of IMO/ITTC-aligned manoeuvring metrics.

Load-bearing premise

The simulator's physics model produces manoeuvre data sufficiently representative of real USV hydrodynamics for the generated datasets to support system identification and derivative estimation.

What would settle it

Direct comparison of hydrodynamic derivatives estimated from the virtual datasets against those from physical sea trials on the same vehicle would reveal large discrepancies if the data are not representative.

Figures

Figures reproduced from arXiv: 2606.12349 by Paria Rezayan.

Figure 1
Figure 1. Figure 1: Standard USV manoeuvres and metrics: (a) TC with advance, transfer, and tactical diameter; [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Example MARUS simulation scene showcasing the USV in the virtual environment. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: ROS–Unity architecture for actuation-traceable virtual sea trials. Red: ROS control layer; blue: [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Simulated USV with differential-thrust actuators, onboard sensors, and body-fixed surge– [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Coordinate systems and heading conventions used for manoeuvre analysis. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Representative kinematic profiles for TC to Starboard and ZZ 20 [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: TC trajectories for port and starboard manoeuvres in normalised longitudinal and transverse [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: ZZ responses for 10◦/10◦ and 20◦/20◦ tests in port-first and starboard-first executions. The corresponding simulation recordings are provided as supplementary videos: Video 3, ZZ 10◦/10◦ Port￾first, Video 4, ZZ 10◦/10◦ Starboard-first, Video 5, ZZ 20◦/20◦ Port-first, and Video 6, ZZ 20◦/20◦ Starboard-first [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Command–execution traceability during ZZ 10 [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Yaw-rate consistency validation comparing logged and reconstructed yaw rate for TC to Port. [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
read the original abstract

Accurate identification of hydrodynamic derivatives is essential for control and navigation of Unmanned Surface Vehicles (USVs), but high-fidelity manoeuvring data from physical sea trials are constrained by cost and safety. Turning Circle (TC) and Zig-Zag (ZZ) trials remain fundamental to IMO and ITTC assessment procedures. This paper extends the Marine Robotics Unity Simulator (MARUS) by introducing a standardised Virtual Sea Trial framework for automated execution and data generation of TC/ZZ manoeuvres, with traceable command-actuation logging, system-identification (SI)-focused data conditioning, and automated extraction of IMO/ITTC-aligned manoeuvring metrics. A key contribution is a dedicated TC/ZZ data acquisition and post-processing pipeline, improving the repeatability and auditability of simulator-based manoeuvres while producing SI-ready datasets for hydrodynamic-derivative identification and digital-twin workflows. Another feature is explicit command-execution separation for differential-thrust steering, where inputs are recorded as ordered rudder-equivalent commands and realised actuation is logged as an execution-level proxy derived from applied thrust. Case-study results demonstrate repeatable and compliant manoeuvre behaviour. For TC tests, the normalised advance differs by approximately 3.9 percent between port and starboard sides, while the tactical diameter differs by approximately 4.6 to 4.7 percent. For ZZ tests, first and second overshoot excesses remain below 1 degree for both +/- 10 degree and +/- 20 degree manoeuvres, satisfying IMO criteria, while peak yaw rates range from approximately 4.1 to 5.8 deg/s. Overall, the framework provides a repeatable and auditable virtual sea-trial workflow for generating IMO/ITTC-aligned datasets and supporting system identification, hydrodynamic-derivative estimation, and digital-twin calibration.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper extends the Marine Robotics Unity Simulator (MARUS) with a Virtual Sea Trial framework for automated Turning Circle (TC) and Zig-Zag (ZZ) manoeuvres on USVs. It adds traceable command-actuation logging (with explicit separation for differential-thrust steering), SI-focused data conditioning, and automated extraction of IMO/ITTC-aligned metrics. Case-study results report internal repeatability (TC advance difference ~3.9%, tactical diameter difference ~4.6–4.7%; ZZ overshoots <1° and peak yaw rates 4.1–5.8 deg/s) meeting IMO criteria, with the central claim that the workflow produces auditable, SI-ready datasets supporting hydrodynamic-derivative estimation and digital-twin calibration.

Significance. If the simulator's physics model is shown to be representative, the framework would provide a practical, standardised tool for generating repeatable virtual sea-trial data at lower cost and risk than physical trials. The traceable logging and post-processing pipeline address real needs for auditability in marine robotics workflows. The reported internal consistency and IMO compliance are useful indicators of workflow reliability within the simulator environment.

major comments (2)
  1. [Abstract / case-study results] Abstract / case-study results: the claim that the generated datasets support system identification and hydrodynamic-derivative estimation is not demonstrated. No derivatives are extracted from the TC/ZZ traces, no comparison to physical sea-trial data is shown, and the reported metrics establish only internal simulator repeatability and IMO compliance rather than external representativeness.
  2. [Description of the TC/ZZ data acquisition and post-processing pipeline] Description of the TC/ZZ data acquisition and post-processing pipeline: the assumption that MARUS manoeuvre data are hydrodynamically representative enough for SI and derivative estimation remains untested. No sensitivity analysis on the underlying physics model or limited validation against real USV data is provided, leaving the downstream utility claims unsubstantiated.
minor comments (1)
  1. [Abstract] The abstract states peak yaw rates of 4.1–5.8 deg/s without specifying the exact manoeuvre, speed, or rudder angle; adding this detail would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need to clarify the manuscript's scope. The work focuses on developing a traceable virtual sea trial framework and pipeline for generating auditable, SI-ready datasets from automated TC/ZZ manoeuvres in MARUS, with demonstrated internal repeatability and IMO compliance. We do not perform hydrodynamic derivative extraction or external validation here, as those are intended downstream uses. We address each major comment below and propose targeted revisions for clarity.

read point-by-point responses

  1. Referee: [Abstract / case-study results] Abstract / case-study results: the claim that the generated datasets support system identification and hydrodynamic-derivative estimation is not demonstrated. No derivatives are extracted from the TC/ZZ traces, no comparison to physical sea-trial data is shown, and the reported metrics establish only internal simulator repeatability and IMO compliance rather than external representativeness.

    Authors: We agree the manuscript does not extract derivatives or compare to physical data. The central contribution is the automated, traceable acquisition and conditioning pipeline that produces datasets formatted and audited for subsequent SI and derivative estimation (e.g., with explicit command-actuation separation and SI-focused post-processing). The case-study metrics establish that the generated traces are repeatable and IMO-compliant within the simulator, which is a necessary foundation for their use in SI workflows. The abstract and conclusions use 'supporting' and 'for hydrodynamic-derivative identification' to indicate intended utility rather than completed demonstration. To address potential overstatement, we will revise the abstract, introduction, and conclusions to state that the framework 'produces SI-ready datasets enabling' or 'prepared for' hydrodynamic-derivative estimation, removing any implication of completed SI. revision: partial

  2. Referee: [Description of the TC/ZZ data acquisition and post-processing pipeline] Description of the TC/ZZ data acquisition and post-processing pipeline: the assumption that MARUS manoeuvre data are hydrodynamically representative enough for SI and derivative estimation remains untested. No sensitivity analysis on the underlying physics model or limited validation against real USV data is provided, leaving the downstream utility claims unsubstantiated.

    Authors: The paper assumes the MARUS physics model (based on standard hydrodynamic formulations in Unity) provides a suitable environment for virtual trials, consistent with other simulation studies in marine robotics. No sensitivity analysis or real-USV validation is included because the manuscript scope is the traceable pipeline and automation, not model validation. The reported internal consistency demonstrates the workflow's reliability for producing auditable data, but external representativeness would indeed require separate benchmarking. We will add an explicit limitations paragraph noting that downstream SI utility depends on the fidelity of the underlying simulator model and recommending future physical validation. revision: yes

Circularity Check

0 steps flagged

No circularity: framework description with no derivation chain

full rationale

The paper describes a software framework extension to MARUS for automated TC/ZZ virtual sea trials, including data pipelines and metric extraction. No equations, parameter fitting, predictions, or mathematical derivations appear in the provided text. All reported results concern internal simulator repeatability and IMO compliance metrics generated within the simulator itself; the central claim is the existence and auditability of the workflow, which does not reduce to any self-referential input by construction. This matches the default non-circular case for a pure engineering-framework paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The contribution is a software framework extension rather than a theoretical model or derivation, so no free parameters, axioms, or invented entities are introduced or required.

pith-pipeline@v0.9.1-grok · 5860 in / 1145 out tokens · 23510 ms · 2026-06-27T09:55:48.030862+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

24 extracted references · 19 canonical work pages · 1 internal anchor

  1. [1]

    Ocean Engineering 266, 112940

    System identification of vessel manoeuvring mod- els. Ocean Engineering 266, 112940. doi:10.1016/j.oceaneng.2022.112940. Amer, A., ´Alvarez-Tu˜n´on, O., U˘gurlu, H.˙I., Le Fevre Sejersen, J., Brodskiy, Y ., Kayacan, E.,

    work page doi:10.1016/j.oceaneng.2022.112940 2022

  2. [2]

    Unav- sim: A visually realistic underwater robotics simulator and synthetic data-generation framework, in: 2023 21st International Conference on Advanced Robotics (ICAR), pp. 570–576. doi:10.1109/IC AR58858.2023.10406819. An, G., Xiang, G., Xiang, X., Guedes Soares, C.,

    work page doi:10.1109/ic 2023

  3. [3]

    Condition monitoring of axial piston pumps based on machine learning-driven real-time CFD simu- lation

    Physics informed neural networks based identi- fication modelling of ship maneuvering motion and associated optimal excitation design. Engineering Applications of Computational Fluid Mechanics 19, 2566860. doi:10.1080/19942060.2025. 2566860. Ariza Ramirez, W., Leong, Z.Q., Nguyen, H., Jayasinghe, S.G.,

    work page doi:10.1080/19942060.2025 2025

  4. [4]

    Ocean Engineering 166, 26–36

    Non-parametric dynamic system identification of ships using multi-output gaussian processes. Ocean Engineering 166, 26–36. doi:10 .1016/j.oceaneng.2018.07.056. Bai, X., Li, B., Xu, X., et al.,

    2018

  5. [5]

    Journal of Marine Science and Application 21, 47–58

    A review of current research and advances in unmanned surface vehicles. Journal of Marine Science and Application 21, 47–58. doi:10.1007/s11804-022-002 76-9. 14 Benjamin, M., Schmidt, H., Newman, P.M., Leonard, J.J.,

    work page doi:10.1007/s11804-022-002

  6. [6]

    Lehman and K

    Autonomy for unmanned marine vehi- cles with moos-ivp, in: Marine Robot Autonomy. Springer, pp. 47–90. doi:10.1007/978-1-461 4-5659-9-2. Fossen, T.I.,

    work page doi:10.1007/978-1-461

  7. [7]

    URL: https://doi.org/10.48550/arXiv.2502.11887, doi:10.48550/arXiv.2502.11

    Stonefish: Supporting machine learning research in marine robotics. URL: https://doi.org/10.48550/arXiv.2502.11887, doi:10.48550/arXiv.2502.11

    work page doi:10.48550/arxiv.2502.11887

  8. [8]

    International Maritime Organization,

    arXiv:2502.11887 [cs]. International Maritime Organization,

    arXiv

  9. [9]

    ASVSim (AirSim for Surface Vehicles): A High-Fidelity Simulation Framework for Autonomous Surface Vehicle Research

    Asvsim (airsim for surface vehicles): A high-fidelity simulation framework for autonomous surface vehicle research. URL:https://doi.org/10.48550/arXiv.2506.22174, doi:10.48550/arX iv.2506.22174. arXiv:2506.22174 [cs]. Loncar, I., Obradovic, J., Krasevac, N., Mandic, L., Kvasic, I., Ferreira, F., Slosic, V ., Nad, D., Miskovic, N.,

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2506.22174

  10. [10]

    Marus – a marine robotics simulator, in: OCEANS 2022, Hampton Roads, pp. 1–7. doi:10 .1109/OCEANS47191.2022.9976969. Luo, W., Moreira, L., Guedes Soares, C.,

    arXiv 2022

  11. [11]

    Ocean Engineering 82, 150–159

    Manoeuvring simulation of catamaran by using implicit models based on support vector machines. Ocean Engineering 82, 150–159. doi:10.1016/j.ocea neng.2014.03.008. Lv, Z., Wang, X., Wang, G., Xing, X., Lv, C., Yu, F.,

    work page doi:10.1016/j.ocea 2014

  12. [12]

    Manhaes, M.M.M., Scherer, S.A., V oss, M., Douat, L.R., Rauschenbach, T.,

    doi:10.3390/jmse13050969. Manhaes, M.M.M., Scherer, S.A., V oss, M., Douat, L.R., Rauschenbach, T.,

    work page doi:10.3390/jmse13050969

  13. [13]

    Uuv simulator: A gazebo-based package for underwater intervention and multi-robot simulation, in: OCEANS 2016 MTS/IEEE Monterey, pp. 1–8. doi:10.1109/OCEANS.2016.7761080. Moreira, L., Guedes Soares, C.,

    work page doi:10.1109/oceans.2016.7761080 2016

  14. [14]

    Modeling, Identification and Control: A Norwegian Research Bulletin 27, 259–275

    An overview of the marine systems simu- lator (mss): A simulink toolbox for marine control systems. Modeling, Identification and Control: A Norwegian Research Bulletin 27, 259–275. doi:10.4173/mic.2006.4.4. Potokar, E., Ashford, S., Kaess, M., Mangelson, J.G.,

    work page doi:10.4173/mic.2006.4.4 2006

  15. [15]

    In: 2022 International Conference on Robotics and Automation (ICRA)

    Holoocean: An underwater robotics simulator, in: 2022 International Conference on Robotics and Automation (ICRA), pp. 3040–3046. doi:10.1109/ICRA46639.2022.9812353. Smith, P., Dunbabin, M.,

    work page doi:10.1109/icra46639.2022.9812353 2022

  16. [16]

    IEEE Journal of Oceanic Engineering 44, 310–319

    High-fidelity autonomous surface vehicle simulator for the maritime robotx challenge. IEEE Journal of Oceanic Engineering 44, 310–319. doi:10.1109/JOE.2018.2 875571. Sukas, O.F., Kinaci, O.K., Bal, S.,

    work page doi:10.1109/joe.2018.2 2018

  17. [17]

    Ocean Engineering 192, 106239

    Theoretical background and application of mansim for ship maneuvering simulations. Ocean Engineering 192, 106239. doi:10.1016/j.oceaneng.2019. 106239. Tadros, M., Aung, M.Z., Louvros, P., Pollalis, C., Nazemian, A., Boulougouris, E.,

    work page doi:10.1016/j.oceaneng.2019 2019

  18. [18]

    Journal of Marine Science and Engineering 13,

    Ship manoeu- vring research 2010–2025: From hydrodynamics and control to digital twins, ai and mass. Journal of Marine Science and Engineering 13,

    2010

  19. [19]

    Umenberger, J., Wgberg, J., Manchester, I.R., Sch¨on, T.B.,

    doi:10.3390/jmse13122322. Umenberger, J., Wgberg, J., Manchester, I.R., Sch¨on, T.B.,

    work page doi:10.3390/jmse13122322

  20. [20]

    Automatica 96, 280–292

    Maximum likelihood identification of 15 stable linear dynamical systems. Automatica 96, 280–292. doi:10.1016/j.automatica.201 8.06.036. Wang, Z., Qiu, C., Dong, Z., Cheng, S., Zheng, L., Chen, S.,

    work page doi:10.1016/j.automatica.201

  21. [21]

    Xu, H., Guedes Soares, C.,

    doi:10.3390/jmse13071341. Xu, H., Guedes Soares, C.,

    work page doi:10.3390/jmse13071341

  22. [22]

    Journal of Marine Science and Application 24, 459–478

    Review of system identification for manoeuvring modelling of marine surface ships. Journal of Marine Science and Application 24, 459–478. doi:10.1007/s11804-0 25-00681-w. Yasukawa, H., Yoshimura, Y .,

    work page doi:10.1007/s11804-0

  23. [23]

    ISA Transactions 58, 186–195

    Multi-innovation auto-constructed least squares identification for 4 dof ship manoeuvring modelling with full-scale trial data. ISA Transactions 58, 186–195. doi:10 .1016/j.isatra.2015.04.004. Zhang, M.M., Choi, W.S., Herman, J., Davis, D., V ogt, C., McCarrin, M., Vijay, Y ., Dutia, D., Lew, W., Peters, S., Bingham, B.,

    2015

  24. [24]

    Dave aquatic virtual environment: Toward a general underwater robotics simulator, in: 2022 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV), pp. 1–8. doi:10.1109/AUV53081.2022.9965808. 16

    work page doi:10.1109/auv53081.2022.9965808 2022