Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …

This letter addresses two challenges facing samplingbased kinodynamic motion planning: a
way to identify good candidate states for local transitions and the subsequent …

Autonomous surface ships have become increasingly interesting for commercial maritime
sectors. Before deep learning (DL) was proposed, surface ship autonomy was mostly model …

A majority of marine accidents that occur can be attributed to errors in human decisions.
Through automation, the occurrence of such incidents can be minimized. Therefore …

At present, with the development of waterborne transport vehicles, research on ship faces a
new round of challenges in terms of intelligence and autonomy. The concept of maritime …

In the present paper, a decision-making agent based on reinforcement learning is designed
for establishing an obstacle avoidance strategy of an autonomous surface vessel (ASV). To …

This paper proposes a methodology for solving the curved path following problem for
underactuated vehicles under unknown ocean current influence using deep reinforcement …

We present a reinforcement learning-based (RL) control scheme for trajectory tracking of
fully-actuated surface vessels. The proposed method learns online both a model-based …

Unmanned Aerial Vehicles (UAVs) are abundantly becoming a part of society, which is a
trend that is expected to grow even further. The quadrotor is one of the drone technologies …

With the growth of traffic on inland waterways, autonomous driving technologies for vessels
will gain increasing significance to ensure traffic flow and safety. Inspired by car-following …