Multi-agent path finding (MAPF) is an essential component of many large-scale, real-world
robot deployments, from aerial swarms to warehouse automation. However, despite the …

We consider the problem of finding distributed controllers for large networks of mobile robots
with interacting dynamics and sparsely available communications. Our approach is to learn …

Recently, significant gains have been made in our understanding of multi-robot systems,
and such systems have been deployed in domains as diverse as precision agriculture …

Unlike autonomous ground vehicles (AGVs), unmanned aerial vehicles (UAVs) have a
higher dimensional configuration space, which makes the motion planning of multi-UAVs a …

The multi-robot coverage problem is an essential building block for systems that perform
tasks like inspection, exploration, or search and rescue. We discretize the coverage problem …

This letter presents a data-driven decentralized trajectory optimization approach for multi-
robot motion planning in dynamic environments. When navigating in a shared space, each …

We propose distributed control laws for a group of anonymous mobile agents to form desired
circle formations when the agents move in the one-dimensional space of a circle. The …

Semantic segmentation enables robots to perceive and reason about their environments
beyond geometry. Most of such systems build upon deep learning approaches. As …

In this work, we study the problem of decentralized multi-agent perimeter defense that asks
for computing actions for defenders with local perceptions and communications to maximize …

In multi-robot systems, although the idea of behaviors allows for an efficient solution to low-
level tasks, high-level missions can rarely be achieved by the execution of a single behavior …