DDM: Fast Near-Optimal Multi-Robot Path Planning using Diversified-Path and Optimal Sub-Problem Solution Database Heuristics

TL;DR

The paper introduces DDM, a centralized, decoupled multi-robot path planning algorithm that uses innovative heuristics to improve scalability and solution quality in warehouse-like grid environments.

Contribution

It presents a novel algorithm combining path diversification and optimal sub-problem solution databases for efficient multi-robot path planning.

Findings

Achieves high scalability in complex environments

Maintains high solution optimality

Effective in dynamic re-planning scenarios

Abstract

We propose a novel centralized and decoupled algorithm, DDM, for solving multi-robot path planning problems in grid graphs, targeting on-demand and automated warehouse-like settings. Two settings are studied: a traditional one whose objective is to move a set of robots from their respective initial vertices to the goal vertices as quickly as possible, and a dynamic one which requires frequent re-planning to accommodate for goal configuration adjustments. Among other techniques, DDM is mainly enabled through exploiting two innovative heuristics: path diversification and optimal sub-problem solution databases. The two heuristics attack two distinct phases of a decoupling-based planner: while path diversification allows the more effective use of the entire workspace for robot travel, optimal sub-problem solution databases facilitate the fast resolution of local path conflicts. Extensive evaluation demonstrates that DDM achieves high levels of scalability and high levels of solution optimality.