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 …

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 …

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 …

In the multi-agent pathfinding problem (MAPF) we are given a set of agents each with
respective start and goal positions. The task is to find paths for all agents while avoiding …

The task in the multi-agent path finding problem (MAPF) is to find paths for multiple agents,
each with a different start and goal position, such that agents do not collide. A successful …

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 …

Multi-agent pathfinding (MAPF) is an area of expanding research interest. At the core of this
research area, numerous diverse search-based techniques were developed in the past 6 …

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

Abstract Multi-Agent Path Finding has been widely studied in the past few years due to its
broad application in the field of robotics and AI. However, previous solvers rely on several …

Multi-agent pathfinding (MAPF) has been widely used to solve large-scale real-world
problems, eg, automation warehouses. The learning-based, fully decentralized framework …