Herman B. Amundsen

Mr. Herman B. Amundsen


Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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Journal Article
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MBMagnus Bjerkeng
Magnus Bjerkeng
TKTrine Kirkhus
Trine Kirkhus
WCWalter Caharija
Walter Caharija
JTJens Thielemann
Jens Thielemann
HAHerman B. Amundsen
Herman B. Amundsen
EGEsten Ingar Grøtli
Esten Ingar Grøtli
Aquaculture net cage inspection and maintenance is a central issue in fish farming. Inspection using autonomous underwater vehicles is a promising solution. This paper proposes laser-camera triangulation for pose estimation to enable autonomous net following for an autonomous vehicle. The laser triangulation 3D data is experimentally compared to a doppler velocity log (DVL) in an active fish farm. We show that our system is comparable in performance to a DVL for distance and angular pose measurements. Laser triangulation is promising as a short distance ranging sensor for autonomous vehicles at a low cost compared to acoustic sensors.

9 December 2025 in arXiv
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LFLauritz Rismark Fosso
Lauritz Rismark Fosso
HAHerman B. Amundsen
Herman B. Amundsen
MXMarios Xanthidis
Marios Xanthidis
SOSveinung Johan Ohrem
Sveinung Johan Ohrem
Holonomic autonomous underwater vehicles (AUVs) have the hardware ability for agile maneuvering in both translational and rotational degrees of freedom (DOFs). However, due to challenges inherent to underwater vehicles, such as complex hydrostatics and hydrodynamics, parametric uncertainties, and frequent changes in dynamics due to payload changes, control is challenging. Performance typically relies on carefully tuned controllers targeting unique platform configurations, and a need for re-tuning for deployment under varying payloads and hydrodynamic conditions. As a consequence, agile maneuvering with simultaneous tracking of time-varying references in both translational and rotational DOFs is rarely utilized in practice. To the best of our knowledge, this paper presents the first general zero-shot sim2real deep reinforcement learning-based (DRL) velocity controller enabling path following and agile 6DOF maneuvering with a training duration of just 3 minutes. Sim2Swim, the proposed approach, inspired by state-of-the-art DRL-based position control, leverages domain randomization and massively parallelized training to converge to field-deployable control policies for AUVs of variable characteristics without post-processing or tuning. Sim2Swim is extensively validated in pool trials for a variety of configurations, showcasing robust control for highly agile motions.

1 July 2024 in HAL
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KNKenny Nguyen
Kenny Nguyen
WCWalter Caharija
Walter Caharija
JOJohan Ohrem
Johan Ohrem
JGJan Gravdahl
Jan Gravdahl
ALAntonio Loria
Antonio Loria
HAHerman B. Amundsen
Herman B. Amundsen
Submitted to IEEE Transactions on Control Systems Technology