Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

Construction machinery and robots are essential equipment for major infrastructure. The
application of deep learning technology can improve the construction quality and alleviate …

This paper proposes an A* guiding deep Q-network (AG-DQN) algorithm for solving the
pathfinding problem of an automated guided vehicle (AGV) in a robotic mobile fulfillment …

Abstract In the Multi-Agent Path Finding (MAPF) problem, a set of agents moving on a graph
must reach their own respective destinations without inter-agent collisions. In practical MAPF …

We study the problem of generating arbitrarily large environments to improve the throughput
of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effective …

Learning communication via deep reinforcement learning (RL) or imitation learning (IL) has
recently been shown to be an effective way to solve Multi-Agent Path Finding (MAPF) …

With the rapid progress in Multi-Agent Path Finding (MAPF), researchers have studied how
MAPF algorithms can be deployed to coordinate hundreds of robots in large automated …

The smart factory is at the heart of Industry 4.0 and is the new paradigm for establishing
advanced manufacturing systems and realizing modern manufacturing objectives such as …

Multi-agent pathfinding (MAPF) is a well-studied abstract model for navigation in a multi-
robot system, where every robot finds the path to its goal position without any collision. Due …

Amphibious ground-aerial vehicles fuse flying and driving modes to enable more flexible air-
land mobility and have received growing attention recently. By analyzing the existing …