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 …

Recently, neural control policies have outperformed existing model-based planning-and-
control methods for autonomously navigating quadrotors through cluttered environments in …

Collision detection between objects is critical for simulation, control, and learning for robotic
systems. How-ever, existing collision detection routines are inherently non-differentiable …

This letter presents a safety trajectory planning and tracking architecture for a quadrotor
autopilot. Motor saturation constraints are explicitly considered in obstacle avoidance …

Generating time-optimal quadrotor trajectories is challenging due to the complex dynamics
of high-speed, agile flight. In this paper, we propose a data-driven method for real-time time …

Navigating mobile robots through environments shared with humans is challenging. From
the perspective of the robot, humans are dynamic obstacles that must be avoided. These …

In this letter, we propose a new method called Clustering Topological PRM (CTopPRM) for
finding multiple topologically distinct paths in 3D cluttered environments. Finding such …

Aggressive time-optimal control of quadcopters poses a significant challenge in the field of
robotics. The state-of-the-art approach leverages reinforcement learning (RL) to train optimal …

This letter addresses the problem of traversing through unknown, tilted, and narrow gaps for
quadrotors using Deep Reinforcement Learning (DRL). Previous learning-based methods …

Studies that broaden drone applications into complex tasks require a stable control
framework. Recently, deep reinforcement learning (RL) algorithms have been exploited in …