Akademik Veri Yönetim Sistemi
Global Maritime Congress, İstanbul, Türkiye, 20 - 21 Mayıs 2024, ss.215-218
This paper presents a novel approach to vessel route
optimization by employing machine learning techniques,
specifically the Deep Deterministic Policy Gradient (DDPG)
algorithm. The research aims to address the challenge of
continuous obstacle avoidance for vessels while optimizing
their routes. The proposed methodology utilizes DDPG
algorithms to develop an intelligent decision-making system
that allows vessels to navigate efficiently while continuously
avoiding obstacles. The study begins with the collection
and pre-processing of data, including vessel characteristics,
meteorological information, and obstacle positions. The preprocessed dataset is then used to train the DDPG model,
enabling it to learn the optimal vessel behavior in different
scenarios. To validate the proposed methodology, extensive
simulations are conducted with varying obstacle positions and
environmental conditions. The results demonstrate improved
route efficiency and obstacle avoidance rates compared to
traditional navigation systems, showcasing the effectiveness
of the machine learning-based approach. Furthermore,
the trained DDPG model exhibits enhanced generalization
capabilities, effectively adapting to dynamic situations and
unencountered obstacles. In conclusion, this paper presents
a novel application of DDPG algorithms in vessel route
optimization, introducing an intelligent decision-making
system capable of continuously avoiding obstacles. The
findings contribute to the advancement of autonomous vessel
navigation, providing a more efficient and safe maritime
transportation system. Future research avenues include
optimizing the DDPG algorithm further, integrating realtime sensor data, and considering multi-vessel interactions in
route planning.