3 research outputs found
Navigating the Ocean with DRL: Path following for marine vessels
Human error is a substantial factor in marine accidents, accounting for 85%
of all reported incidents. By reducing the need for human intervention in
vessel navigation, AI-based methods can potentially reduce the risk of
accidents. AI techniques, such as Deep Reinforcement Learning (DRL), have the
potential to improve vessel navigation in challenging conditions, such as in
restricted waterways and in the presence of obstacles. This is because DRL
algorithms can optimize multiple objectives, such as path following and
collision avoidance, while being more efficient to implement compared to
traditional methods. In this study, a DRL agent is trained using the Deep
Deterministic Policy Gradient (DDPG) algorithm for path following and waypoint
tracking. Furthermore, the trained agent is evaluated against a traditional PD
controller with an Integral Line of Sight (ILOS) guidance system for the same.
This study uses the Kriso Container Ship (KCS) as a test case for evaluating
the performance of different controllers. The ship's dynamics are modeled using
the maneuvering Modelling Group (MMG) model. This mathematical simulation is
used to train a DRL-based controller and to tune the gains of a traditional PD
controller. The simulation environment is also used to assess the controller's
effectiveness in the presence of wind.Comment: Proceedings of the Sixth International Conference in Ocean
Engineering (ICOE2023
Comparison of path following in ships using modern and traditional controllers
Vessel navigation is difficult in restricted waterways and in the presence of
static and dynamic obstacles. This difficulty can be attributed to the
high-level decisions taken by humans during these maneuvers, which is evident
from the fact that 85% of the reported marine accidents are traced back to
human errors. Artificial intelligence-based methods offer us a way to eliminate
human intervention in vessel navigation. Newer methods like Deep Reinforcement
Learning (DRL) can optimize multiple objectives like path following and
collision avoidance at the same time while being computationally cheaper to
implement in comparison to traditional approaches. Before addressing the
challenge of collision avoidance along with path following, the performance of
DRL-based controllers on the path following task alone must be established.
Therefore, this study trains a DRL agent using Proximal Policy Optimization
(PPO) algorithm and tests it against a traditional PD controller guided by an
Integral Line of Sight (ILOS) guidance system. The Krisco Container Ship (KCS)
is chosen to test the different controllers. The ship dynamics are
mathematically simulated using the Maneuvering Modelling Group (MMG) model
developed by the Japanese. The simulation environment is used to train the deep
reinforcement learning-based controller and is also used to tune the gains of
the traditional PD controller. The effectiveness of the controllers in the
presence of wind is also investigated.Comment: Proceedings of the Sixth International Conference in Ocean
Engineering (ICOE2023