Graph Neural Networks (GNNs) are a paradigm-shifting neural architecture to facilitate the
learning of complex multi-agent behaviors. Recent work has demonstrated remarkable …

We develop a hybrid system architecture that enables a team of mobile robots to complete a
task in a complex environment by self-organizing into a multihop ad hoc network and solving …

In this paper, we consider networks of mobile robots responsible for servicing a collection of
tasks in complex environments, while ensuring end-to-end connectivity with a fixed …

This paper proposes a method to maintain the strong connectivity property of a mobile robot
ad hoc network in the presence of disturbances or additional control goals. Each robot has a …

Multi-robot teams that intelligently gather information have the potential to transform
industries as diverse as agriculture, space exploration, mining, environmental monitoring …

In this thesis, we are particularly interested in investigating machine learning (especially
graph neural network) based approaches to find the trade-off between optimality and …

In this paper we propose a hybrid architecture that allows a team of mobile robots to self-
organize into a multi-hop ad-hoc network and solve the joint mobility and communication …

An emergency scenario is characterized by the unpredictability of the environment
conditions and by the scarcity of the available communication infrastructures. After a natural …

Enabling a team of robots to self-organize into a multi-hop ad hoc network as it
simultaneously completes a task requires a system architecture that controls both the motion …

COMMUNICATION connectivity is a common requirement in multi-agent systems. Multi-
agent systems typically need to maintain communication connectivity to complete their tasks …