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 …

The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …

Advances in Single-vehicle intelligence of automated driving has encountered great
challenges because of limited capabilities in perception and interaction with complex traffic …

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 …

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 …

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 …

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

Real-world problems often exhibit complex relationships and dependencies, which can be
effectively captured by graph learning systems. Graph attention networks (GATs) have …

Multi-robot systems such as swarms of aerial robots are naturally suited to offer additional
flexibility, resilience, and robustness in several tasks compared to a single robot by enabling …

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 …