Underwater object detection is one of the most challenging research topics in computer
vision technology. The complex underwater environment makes underwater images suffer …

The rapid technological development of computing power and system operations today
allows for increasingly advanced algorithm implementation, as well as path planning in real …

This paper introduces a novel reference tracking control approach implemented using a
combination of the Actor-Critic Reinforcement Learning (RL) framework and the Grey Wolf …

In this paper, an adversarial deep reinforcement learning-based control method is proposed
to address the issue of robust depth tracking of an underactuated autonomous underwater …

Detecting and monitoring underwater organisms is very important for sea aquaculture. The
human eye struggles to quickly distinguish between aquatic species due to their variety and …

This paper presents a robust cooperative distributed task allocation and mission planning
system that enables a fleet of a certain class of autonomous underwater vehicles (AUVs) for …

This paper concentrates on the issue of finite-time coordinated formation control of discrete-
time multiple autonomous underwater vehicles (AUVs) with input saturation and time …

Multi-agent reinforcement learning is promising for real-world applications as it encourages
agents to perceive and interact with their surrounding environment autonomously. However …

This paper presents a novel fixed-time adaptive Fault Tolerant Control (FTC) framework for
MIMO nonlinear Euler-Lagrange systems using sliding mode-based strategy, reinforcement …

A manipulator system may have both kinematics and dynamics uncertainties, which pose
difficulties in controller design. To solve the above problem, this study proposes an adaptive …