Efficient exploration strategies are vital in tasks such as search-and-rescue missions and
disaster surveying. Unmanned Aerial Vehicles (UAVs) have become particularly popular in …

In this study, a novel end-to-end path planning algorithm based on deep reinforcement
learning is proposed for aerial robots deployed in dense environments. The learning agent …

Abstract Unmanned Aerial Vehicles are useful tools for many applications. However,
autonomous path planning for Unmanned Aerial Vehicles in unfamiliar environments is a …

In this work, we introduce REFORMA, a novel robust reinforcement learning (RL) approach
to design controllers for unmanned aerial vehicles (UAVs) robust to unknown disturbances …

As the application of UAVs becomes more and more widespread, accidents such as
accidental injuries to personnel, property damage, and loss and destruction of UAVs due to …

Aerial robots (drones) offer critical advantages in missions where human participation is
impeded due to hazardous conditions. Among these, search and rescue missions in disaster …

Efficient exploration strategies are vital in tasks such as search-and-rescue missions and
disaster surveying. Unmanned Aerial Vehicles (UAVs) have become particularly popular in …

This work targets what we consider to be the foundational step for urban airborne robots, a
safe landing. Our attention is directed toward what we deem the most crucial aspect of the …

We propose an algorithm that combines Reinforcement Learning (RL) and a PID cascade
control for landing a drone on a moving unmanned ground vehicle (UGV). Unlike other …

From industrial to space robotics, safe landing is an essential component for flight
operations. With the growing interest in artificial intelligence, we direct our attention to …