Deep Reinforcement Learning (DRL) has emerged as a promising approach to enhance
motion control and decision-making through a wide range of robotic applications. While prior …

This paper presents the construction and prototyping of the Hybrid Unmanned Aerial
Underwater Vehicle (HUAUV) capable to develop stabilized flights and presents the initial …

Mapless navigation for Automated Guided Vehicles (AGV) via Deep Reinforcement
Learning (DRL) algorithms has attracted significantly rising attention in recent years …

Deep Reinforcement Learning (Deep-RL) techniques for motion control have been
continuously used to deal with decision-making problems for a wide variety of robots …

Abstract Hybrid Unmanned Aerial Underwater Vehicle (HUAUV) is a class of multi-modal
mobile robots characterized by the ability to fly and navigate underwater, as well as being …

Quadrotors have been used in diverse areas, such as agriculture, civil construction, fire
monitoring, and so on. However, these vehicles cannot perform missions that require …

This paper presents the investigation of the ground effect on hybrid aerial-aquatic vehicles.
The ground effect is the formation of an air bag, caused by the thrusters during the landing …

This paper offers a feasibility study on using simulated environments for training
autonomous underwater vehicles (AUVs). With the goal of monitoring marine megafauna …

The present study focuses on the implementation of Deep Reinforcement Learning (Deep-
RL) techniques for a parallel manipulator robot, specifically the Delta Robot, within a …

This work presents a study on parallel and distributional deep reinforcement learning
applied to the mapless navigation of UAVs. For this, we developed an approach based on …