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

Résultats de recherche: Chapitre dans un livre, un rapport, des actes de conférences › Contribution à une conférence › Revue par des pairs

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Résumé

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.

langue originale	Anglais
titre	Robotics and Mechatronics - Proceedings of ISRM 2026
rédacteurs en chef	Vu Linh Nguyen, Phong Dien Nguyen, Nguyen Quang Hoang, Giuseppe Carbone
Editeur	Springer Science and Business Media B.V.
Pages	259-268
Nombre de pages	10
ISBN (imprimé)	9783032294685
Les DOIs	https://doi.org/10.1007/978-3-032-29469-2_27 https://doi.org/10.1007/978-3-032-29469-2_27
état	Publié - 2026
Evénement	9th IFToMM International Symposium on Robotics and Mechatronics, ISRM 2026 - Hanoi, Viet-Nam Durée: 15 juil. 2026 → 17 juil. 2026

Série de publications

Nom	Mechanisms and Machine Science
Volume	213
ISSN (imprimé)	2211-0984
ISSN (Electronique)	2211-0992

Une conférence

Une conférence	9th IFToMM International Symposium on Robotics and Mechatronics, ISRM 2026
Pays/Territoire	Viet-Nam
La ville	Hanoi
période	15/07/26 → 17/07/26

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Autres fichiers et liens

Lien vers la publication dans Scopus

SAILS: Autonomous Maritime UAS Landing Solution
Nguyên, T. (Chercheur), De cubber, G. (Promoteur) & Vochten, M. (Promoteur)
1/12/24 → 31/12/27
Projet: Recherche › DEFRA

Ship Motion Prediction
Nguyen, T.-T. (Créateur), Springer Nature, 14 juil. 2026
DOI: 10.1007/978-3-032-29469-2_27
Ensemble de données

Contient cette citation

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. Dans V. L. Nguyen, P. D. Nguyen, N. Quang Hoang, & G. Carbone (eds.), Robotics and Mechatronics - Proceedings of ISRM 2026 (p. 259-268). (Mechanisms and Machine Science; Vol 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. Editeur / Vu Linh Nguyen ; Phong Dien Nguyen ; Nguyen Quang Hoang ; Giuseppe Carbone. Springer Science and Business Media B.V., 2026. p. 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",

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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. Dans VL Nguyen, PD Nguyen, N Quang Hoang & G Carbone (eds), Robotics and Mechatronics - Proceedings of ISRM 2026. Mechanisms and Machine Science, VOL. 213, Springer Science and Business Media B.V., p. 259-268, 9th IFToMM International Symposium on Robotics and Mechatronics, ISRM 2026, Hanoi, Viet-Nam, 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. Ed. / Vu Linh Nguyen; Phong Dien Nguyen; Nguyen Quang Hoang; Giuseppe Carbone. Springer Science and Business Media B.V., 2026. p. 259-268 (Mechanisms and Machine Science; Vol 213).

Résultats de recherche: Chapitre dans un livre, un rapport, des actes de conférences › Contribution à une conférence › Revue par des pairs

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. Dans Nguyen VL, Nguyen PD, Quang Hoang N, Carbone G, rédacteurs en chef, Robotics and Mechatronics - Proceedings of ISRM 2026. Springer Science and Business Media B.V. 2026. p. 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

Résumé

Série de publications

Une conférence

Accès au document

Autres fichiers et liens

Empreinte digitale

Projets

SAILS: Autonomous Maritime UAS Landing Solution

Ensembles de données

Ship Motion Prediction

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