With the decrease in the cost and size of drones in recent years, their number has also
increased exponentially. As such, the concerns regarding security aspects that are raised by …

Background: Open Access Editor's Choice Article Flying Free: A Research Overview of Deep
Learning in Drone Navigation Autonomy by Thomas Lee 1, Susan Mckeever 2 and Jane …

Autonomous, agile quadrotor flight raises fundamental challenges for robotics research in
terms of perception, planning, learning, and control. A versatile and standardized platform is …

This work presents a modular and hierarchical approach to learn policies for exploring 3D
environments, calledActive Neural SLAM'. Our approach leverages the strengths of both …

In many robotic tasks, such as autonomous drone racing, the goal is to travel through a set of
waypoints as fast as possible. A key challenge for this task is planning the timeoptimal …

In this article, we tackle the problem of flying time-optimal trajectories through multiple
waypoints with quadrotors. State-of-the-art solutions split the problem into a planning task …

Over the last decade, the use of autonomous drone systems for surveying, search and
rescue, or last-mile delivery has increased exponentially. With the rise of these applications …

Neural networks predictions are unreliable when the input sample is out of the training
distribution or corrupted by noise. Being able to detect such failures automatically is …

Dynamically changing environments, unreliable state estimation, and operation under
severe resource constraints are fundamental challenges that limit the deployment of small …

Policy search and model predictive control (MPC) are two different paradigms for robot
control: policy search has the strength of automatically learning complex policies using …