Graph-Based Multi-Robot Path Finding and Planning

TL;DR

This paper reviews graph-based algorithms for multi-robot path planning, highlighting recent advances that enable real-time, large-scale, collision-free navigation in complex environments.

Contribution

It provides a comprehensive survey of classic and state-of-the-art MAPF algorithms and their adaptations for real-world multi-robot systems.

Findings

Recent algorithms can plan for hundreds of robots in seconds.

Variants of MAPF address real-world constraints like robot kinematics.

MAPF techniques are effective in applications like warehouse automation and drone navigation.

Abstract

Purpose of Review Planning collision-free paths for multiple robots is important for real-world multi-robot systems and has been studied as an optimization problem on graphs, called Multi-Agent Path Finding (MAPF). This review surveys different categories of classic and state-of-the-art MAPF algorithms and different research attempts to tackle the challenges of generalizing MAPF techniques to real-world scenarios. Recent Findings Solving MAPF problems optimally is computationally challenging. Recent advances have resulted in MAPF algorithms that can compute collision-free paths for hundreds of robots and thousands of navigation tasks in seconds of runtime. Many variants of MAPF have been formalized to adapt MAPF techniques to different real-world requirements, such as considerations of robot kinematics, online optimization for real-time systems, and the integration of task assignment and path planning. Summary Algorithmic techniques for MAPF problems have addressed important aspects of several multi-robot applications, including automated warehouse fulfillment and sortation, automated train scheduling, and navigation of non-holonomic robots and quadcopters. This showcases their potential for real-world applications of large-scale multi-robot systems.