Efficient Heuristics for Multi-Robot Path Planning in Crowded Environments

TL;DR

This paper introduces two hybrid algorithms, DCBS and SCBS, that significantly improve the efficiency and solution quality of multi-robot path planning in dense environments, addressing a key challenge in the field.

Contribution

The study develops and validates two novel hybrid algorithms that effectively balance optimality and computational efficiency in dense multi-robot path planning scenarios.

Findings

DCBS and SCBS reduce computational time compared to existing methods.

They improve solution quality over rule-based approaches.

Algorithms are suitable for high-density, real-time applications.

Abstract

Optimal Multi-Robot Path Planning (MRPP) has garnered significant attention due to its many applications in domains including warehouse automation, transportation, and swarm robotics. Current MRPP solvers can be divided into reduction-based, search-based, and rule-based categories, each with their strengths and limitations. Regardless of the methodology, however, the issue of handling dense MRPP instances remains a significant challenge, where existing approaches generally demonstrate a dichotomy regarding solution optimality and efficiency. This study seeks to bridge the gap in optimal MRPP resolution for dense, highly-entangled scenarios, with potential applications to high-density storage systems and traffic congestion control. Toward that goal, we analyze the behaviors of SOTA MRPP algorithms in dense settings and develop two hybrid algorithms leveraging the strengths of existing SOTA algorithms: DCBS (database-accelerated enhanced conflict-based search) and SCBS (sparsified enhanced conflict-based search). Experimental validations demonstrate that DCBS and SCBS deliver a significant reduction in computational time compared to existing bounded-suboptimal methods and improve solution quality compared to existing rule-based methods, achieving a desirable balance between computational efficiency and solution optimality. As a result, DCBS and SCBS are particularly suitable for quickly computing good-quality solutions for multi-robot routing in dense settings