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 …

This paper studies the multi-robot task assignment problem in which a fleet of dispersed
robots needs to efficiently transport a set of dynamically appearing packages from their initial …

In this work, we introduce SMART-LLM, an innovative framework designed for embodied
multi-robot task planning. SMART-LLM: Smart Multi-Agent Robot Task Planning using Large …

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 …

In this work, we consider the Multi-Agent Pickup-and-Delivery (MAPD) problem, where
agents constantly engage with new tasks and need to plan collision-free paths to execute …

When a traditional Deep Deterministic Policy Gradient (DDPG) algorithm is used in mobile
robot path planning, due to the limited observable environment of mobile robots, the training …

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 …

Abstract Multi-Agent Path Finding (MAPF) algorithms and their variants can find high-quality
collision-free plans for automated warehousing under simplified assumptions about the …

We formulate the problem of multi-robot path planning with due times (MRPP-DT), which is a
variant of the classical path planning problem with task due times explicitly considered. The …

Conventional multiagent path finding (MAPF) problems aim to compute an ensemble of
collision-free paths for multiple agents from their respective starting locations to preallocated …