Deep Long-Term Ship Motion Prediction with Anomaly Detection for Autonomous Maritime UAV Landing

Tien Thanh Nguyen; Maxim Vochten; Hafeez Chaudhary; Geert De Cubber

doi:10.1007/978-3-032-29469-2_27

Deep Long-Term Ship Motion Prediction with Anomaly Detection for Autonomous Maritime UAV Landing

Robotics & Autonomous Systems

Agilica BV

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

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Abstract

Autonomous landing of Unmanned Aerial Vehicles (UAVs) on maritime vessels is challenging due to stochastic wave-induced motions. This paper proposes a realtime robust deep learning framework for long-term ship motion prediction using exclusively Inertial Measurement Unit (IMU) data. To mitigate risks from sudden vessel or environmental shifts, an anomaly detection mechanism verifies data stability prior to forecasting. The system is trained via a high-fidelity simulator
and validated against real-world vessel telemetry, demonstrating strong sim-to-real generalization. Integrating this network into the UAV control loop enables the determination of optimal landing windows and precise attitude synchronization, significantly reducing landing impact forces. Code and datasets are provided for open access.

Originalsprache	Englisch
Titel	Robotics and Mechatronics - Proceedings of ISRM 2026
Redakteure/-innen	Vu Linh Nguyen, Phong Dien Nguyen, Nguyen Quang Hoang, Giuseppe Carbone
Herausgeber (Verlag)	Springer Science and Business Media B.V.
Seiten	259-268
Seitenumfang	10
ISBN (Print)	9783032294685
DOIs	https://doi.org/10.1007/978-3-032-29469-2_27 https://doi.org/10.1007/978-3-032-29469-2_27
Publikationsstatus	Veröffentlicht - 2026
Veranstaltung	9th IFToMM International Symposium on Robotics and Mechatronics, ISRM 2026 - Hanoi, Vietnam Dauer: 15 Juli 2026 → 17 Juli 2026

Publikationsreihe

Name	Mechanisms and Machine Science
Band	213
ISSN (Print)	2211-0984
ISSN (elektronisch)	2211-0992

Konferenz

Konferenz	9th IFToMM International Symposium on Robotics and Mechatronics, ISRM 2026
Land/Gebiet	Vietnam
Ort	Hanoi
Zeitraum	15/07/26 → 17/07/26

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Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

SAILS: Autonomous Maritime UAS Landing Solution
Nguyên, T. (Forschende), De cubber, G. (Leitende(r) Forscher/-in) & Vochten, M. (Leitende(r) Forscher/-in)
1/12/24 → 31/12/27
Projekt: Forschung › DEFRA

Ship Motion Prediction
Nguyen, T.-T. (Urheber), Springer Nature, 14 Juli 2026
DOI: 10.1007/978-3-032-29469-2_27
Datensatz

Dieses zitieren

Nguyen, T. T., Vochten, M., Chaudhary, H., & De Cubber, G. (2026). Deep Long-Term Ship Motion Prediction with Anomaly Detection for Autonomous Maritime UAV Landing. in V. L. Nguyen, P. D. Nguyen, N. Quang Hoang, & G. Carbone (Hrsg.), Robotics and Mechatronics - Proceedings of ISRM 2026 (S. 259-268). (Mechanisms and Machine Science; Band 213). Springer Science and Business Media B.V.. https://doi.org/10.1007/978-3-032-29469-2_27, https://doi.org/10.1007/978-3-032-29469-2_27

Nguyen, Tien Thanh ; Vochten, Maxim ; Chaudhary, Hafeez et al. / Deep Long-Term Ship Motion Prediction with Anomaly Detection for Autonomous Maritime UAV Landing. Robotics and Mechatronics - Proceedings of ISRM 2026. Hrsg. / Vu Linh Nguyen ; Phong Dien Nguyen ; Nguyen Quang Hoang ; Giuseppe Carbone. Springer Science and Business Media B.V., 2026. S. 259-268 (Mechanisms and Machine Science).

@inproceedings{24a24d86614548ce8aaa50d960048cf6,

title = "Deep Long-Term Ship Motion Prediction with Anomaly Detection for Autonomous Maritime UAV Landing",

abstract = "Autonomous landing of Unmanned Aerial Vehicles (UAVs) on maritime vessels is challenging due to stochastic wave-induced motions. This paper proposes a realtime robust deep learning framework for long-term ship motion prediction using exclusively Inertial Measurement Unit (IMU) data. To mitigate risks from sudden vessel or environmental shifts, an anomaly detection mechanism verifies data stability prior to forecasting. The system is trained via a high-fidelity simulator and validated against real-world vessel telemetry, demonstrating strong sim-to-real generalization. Integrating this network into the UAV control loop enables the determination of optimal landing windows and precise attitude synchronization, significantly reducing landing impact forces. Code and datasets are provided for open access.",

keywords = "anomaly detection, deep time-series forecasting, maritime UAV landing, ship motion prediction",

author = "Nguyen, \{Tien Thanh\} and Maxim Vochten and Hafeez Chaudhary and \{De Cubber\}, Geert",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2027.; 9th IFToMM International Symposium on Robotics and Mechatronics, ISRM 2026 ; Conference date: 15-07-2026 Through 17-07-2026",

year = "2026",

doi = "10.1007/978-3-032-29469-2\_27",

language = "English",

isbn = "9783032294685",

series = "Mechanisms and Machine Science",

publisher = "Springer Science and Business Media B.V.",

pages = "259--268",

editor = "Nguyen, \{Vu Linh\} and Nguyen, \{Phong Dien\} and \{Quang Hoang\}, Nguyen and Giuseppe Carbone",

booktitle = "Robotics and Mechatronics - Proceedings of ISRM 2026",

}

Nguyen, TT , Vochten, M, Chaudhary, H & De Cubber, G 2026, Deep Long-Term Ship Motion Prediction with Anomaly Detection for Autonomous Maritime UAV Landing. in VL Nguyen, PD Nguyen, N Quang Hoang & G Carbone (Hrsg.), Robotics and Mechatronics - Proceedings of ISRM 2026. Mechanisms and Machine Science, Bd. 213, Springer Science and Business Media B.V., S. 259-268, 9th IFToMM International Symposium on Robotics and Mechatronics, ISRM 2026, Hanoi, Vietnam, 15/07/26. https://doi.org/10.1007/978-3-032-29469-2_27, https://doi.org/10.1007/978-3-032-29469-2_27

Deep Long-Term Ship Motion Prediction with Anomaly Detection for Autonomous Maritime UAV Landing. / Nguyen, Tien Thanh ; Vochten, Maxim; Chaudhary, Hafeez et al.
Robotics and Mechatronics - Proceedings of ISRM 2026. Hrsg. / Vu Linh Nguyen; Phong Dien Nguyen; Nguyen Quang Hoang; Giuseppe Carbone. Springer Science and Business Media B.V., 2026. S. 259-268 (Mechanisms and Machine Science; Band 213).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Deep Long-Term Ship Motion Prediction with Anomaly Detection for Autonomous Maritime UAV Landing

AU - Nguyen, Tien Thanh

AU - Vochten, Maxim

AU - Chaudhary, Hafeez

AU - De Cubber, Geert

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2027.

PY - 2026

Y1 - 2026

N2 - Autonomous landing of Unmanned Aerial Vehicles (UAVs) on maritime vessels is challenging due to stochastic wave-induced motions. This paper proposes a realtime robust deep learning framework for long-term ship motion prediction using exclusively Inertial Measurement Unit (IMU) data. To mitigate risks from sudden vessel or environmental shifts, an anomaly detection mechanism verifies data stability prior to forecasting. The system is trained via a high-fidelity simulator and validated against real-world vessel telemetry, demonstrating strong sim-to-real generalization. Integrating this network into the UAV control loop enables the determination of optimal landing windows and precise attitude synchronization, significantly reducing landing impact forces. Code and datasets are provided for open access.

AB - Autonomous landing of Unmanned Aerial Vehicles (UAVs) on maritime vessels is challenging due to stochastic wave-induced motions. This paper proposes a realtime robust deep learning framework for long-term ship motion prediction using exclusively Inertial Measurement Unit (IMU) data. To mitigate risks from sudden vessel or environmental shifts, an anomaly detection mechanism verifies data stability prior to forecasting. The system is trained via a high-fidelity simulator and validated against real-world vessel telemetry, demonstrating strong sim-to-real generalization. Integrating this network into the UAV control loop enables the determination of optimal landing windows and precise attitude synchronization, significantly reducing landing impact forces. Code and datasets are provided for open access.

KW - anomaly detection

KW - deep time-series forecasting

KW - maritime UAV landing

KW - ship motion prediction

UR - https://www.scopus.com/pages/publications/105045216175

U2 - 10.1007/978-3-032-29469-2_27

DO - 10.1007/978-3-032-29469-2_27

M3 - Conference contribution

AN - SCOPUS:105045216175

SN - 9783032294685

T3 - Mechanisms and Machine Science

SP - 259

EP - 268

BT - Robotics and Mechatronics - Proceedings of ISRM 2026

A2 - Nguyen, Vu Linh

A2 - Nguyen, Phong Dien

A2 - Quang Hoang, Nguyen

A2 - Carbone, Giuseppe

PB - Springer Science and Business Media B.V.

T2 - 9th IFToMM International Symposium on Robotics and Mechatronics, ISRM 2026

Y2 - 15 July 2026 through 17 July 2026

ER -

Nguyen TT , Vochten M, Chaudhary H, De Cubber G. Deep Long-Term Ship Motion Prediction with Anomaly Detection for Autonomous Maritime UAV Landing. in Nguyen VL, Nguyen PD, Quang Hoang N, Carbone G, Hrsg., Robotics and Mechatronics - Proceedings of ISRM 2026. Springer Science and Business Media B.V. 2026. S. 259-268. (Mechanisms and Machine Science). doi: 10.1007/978-3-032-29469-2_27, 10.1007/978-3-032-29469-2_27

Deep Long-Term Ship Motion Prediction with Anomaly Detection for Autonomous Maritime UAV Landing

Abstract

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SAILS: Autonomous Maritime UAS Landing Solution

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Ship Motion Prediction

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