Effective communication is key to successful, decentralized, multi-robot path planning. Yet, it
is far from obvious what information is crucial to the task at hand, and how and when it must …

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 …

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 …

Abstract Purpose of Review Planning collision-free paths for multiple robots is important for
real-world multi-robot systems and has been studied as an optimization problem on graphs …

Multirobot path planning leads multiple robots from start positions to designated goal
positions by generating efficient and collision-free paths. Multirobot systems realize …

Multi-Agent Path Finding (MAPF) is essential to large-scale robotic systems. Recent
methods have applied reinforcement learning (RL) to learn decentralized polices in partially …

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 …

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 …

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

Multi-agent navigation in dynamic environments is of great industrial value when deploying
a large scale fleet of robot to real-world applications. This paper proposes a decentralized …