Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

Abstract Purpose of Review This review summarizes the broad roles that communication
formats and technologies have played in enabling multi-robot systems. We approach this …

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 …

Deep Reinforcement Learning has made significant progress in multi-agent systems in
recent years. In this review article, we have focused on presenting recent approaches on …

The control of a multiple unmanned aerial vehicle (UAV) system is popular and attracting a
lot of attentions. This is motivated by many practical civil and commercial UAV applications …

Planning optimal paths for large numbers of robots is computationally expensive. In this
paper, we introduce a new framework for multirobot path planning called subdimensional …

In autonomous multirobot systems one of the concerns is how to prevent collisions between
the individual robots. One approach to this problem involves finding coordinated trajectories …

Robustness is key to engineering, automation, and science as a whole. However, the
property of robustness is often underpinned by costly requirements such as over …

This work deals with the problem of planning conflict-free paths for mobile robots in cluttered
environments. Since centralized, coupled planning algorithms are computationally …

Distributed mobile robotics (DMR) involves teams of networked robots navigating in a
physical space to achieve tasks in a coordinated fashion. A major challenge in DMR is to …