Unmanned surface vehicle navigation through generative adversarial imitation learning

Chaysri, Piyabhum; Spatharis, Christos; Blekas, Konstantinos; Vlachos, Kostas

doi:10.1016/j.oceaneng.2023.114989

Cited by 5 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We evaluated the performance of the proposed reinforcement learning framework by conducting simulations in an environment closely resembling the one utilized in our previous research [15]. The simulation incorporates both the kinematic and dynamic model of the USV, along with simulated wind and wind-generated wave disturbances.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Given our primary focus on machine learning, reinforcement learning (RL), control of autonomous marine platforms, and intelligent agents [11][12][13], we have already delved into the study and implementation of ship navigation algorithms utilizing RL within simulated environments [14,15], and control of autonomous robotic marine platforms [16,17]. Encouragingly, we have achieved promising results, attaining high accuracy and self-learning capabilities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design and Implementation of a Low-Cost Intelligent Unmanned Surface Vehicle

Chaysri,

Spatharis,

Vlachos

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

This article describes the design and construction journey of a self-developed unmanned surface vehicle (USV). In order to increase the accessibility and lower the barrier of entry we propose a low-cost (under EUR 1000) approach to the vessel construction with great adaptability and customizability. This design prioritizes minimal power consumption as a key objective. It focuses on elucidating the intricacies of both the design and assembly processes involved in creating an economical USV. Utilizing easily accessible components, the boat outlined in this study has been already participated in the 1st Aegean Ro-boat Race 2023 competition and is tailored for entry into similar robotic competitions. Its primary functionalities encompass autonomous sea navigation coupled with sophisticated collision avoidance capabilities. Finally, we studied reinforcement learning strategies for constructing a robust intelligent controller for the task of USV navigation under disturbances and we show some preliminary simulation results we have obtained.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%