A fundamental task in robotics is to plan collision‐free motions among a set of obstacles.
Recently, learning‐based motion‐planning methods have shown significant advantages in …

State-of-the-art navigation methods leverage a spatial memory to generalize to new
environments, but their occupancy maps are limited to capturing the geometric structures …

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 …

Getting robots to navigate to multiple objects autonomously is essential yet difficult in robot
applications. One of the key challenges is how to explore environments efficiently with …

Autonomous vehicles use 3D sensors for perception. Cooperative perception enables
vehicles to share sensor readings with each other to improve safety. Prior work in …

In real-world environments, ranging from urban disastrous scenes to underground mining
tunnels, autonomous mobile robots are being deployed in harsh and cluttered …

In this article, we address a multi-robot planning problem in environments with partially
unknown semantics. The environment is assumed to have a known geometric structure (eg …

The availability of maps of indoor environments is often fundamental for autonomous mobile
robots to efficiently operate in industrial, office, and domestic applications. When robots build …

This paper addresses a new semantic multi-robot planning problem in uncertain and
dynamic environments. Particularly, the environment is occupied with mobile and uncertain …

In autonomous navigation, sensors suffer from massive occlusion in cluttered environments,
leaving a significant amount of space unknown. In practice, treating the unknown space in …