Abstract Path Planning problems with Unmanned Aerial Vehicles (UAVs) are among the
most studied knowledge areas in the related literature. However, few of them have been …

Numerous path-planning studies have been conducted in past decades due to the
challenges of obtaining optimal solutions. This paper reviews multi-robot path-planning …

The expressive power of Graph Neural Networks (GNNs) has been studied extensively
through the Weisfeiler-Leman (WL) graph isomorphism test. However, standard GNNs and …

We propose a framework for planning in unknown dynamic environments with probabilistic
safety guarantees using conformal prediction. Particularly, we design a model predictive …

Path planning for mobile robots in large dynamic environments is a challenging problem, as
the robots are required to efficiently reach their given goals while simultaneously avoiding …

The domains of transport and logistics are increasingly relying on autonomous mobile
robots for the handling and distribution of passengers or resources. At large system scales …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

Graph neural networks (GNNs) have emerged as a powerful tool for nonlinear processing of
graph signals, exhibiting success in recommender systems, power outage prediction, and …

We consider the problem of designing distributed collision-avoidance multi-agent control in
large-scale environments with potentially moving obstacles, where a large number of agents …

Multi-agent path finding (MAPF) is an indispensable component of large-scale robot
deployments in numerous domains ranging from airport management to warehouse …