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Hybrid CBS-PRM for Multi-robot Path Planning in Congested Environments

  • Yang Zhou
  • , Haoyu Tian
  • , Xin Li
  • , Siqi Chen
  • , Guoxiao Liu
  • , Weiran Yao*
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • Shenzhen Institute of Information Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a novel hybrid CBS-PRM framework for efficient multi-robot path planning in congested environments. Addressing the NP-hard complexity of multi-agent navigation, our approach integrates conflict-based search with probabilistic roadmaps through three key innovations: adaptive rectangular sampling regions that concentrate nodes in critical pathways while avoiding obstacles; a directional expansion strategy resolving connectivity issues in narrow passages via potential field-guided optimization; and dynamic connection length adjustment responding to local environmental constraints. The methodology demonstrates significant improvements in computational efficiency and path quality while maintaining rigorous collision avoidance guarantees, providing an effective solution for warehouse automation and industrial robotics applications requiring coordinated multi-agent movement through constrained spaces.

Original languageEnglish
Title of host publicationProceedings of 5th 2025 International Conference on Autonomous Unmanned Systems (ICAUS)
EditorsShaorong Xie, Yifeng Niu, Wenxing Fu, Yi Qu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages301-310
Number of pages10
ISBN (Print)9789819576470
DOIs
StatePublished - 2026
Event5th International Conference on Autonomous Unmanned Systems, ICAUS 2025 - Shanghai, China
Duration: 17 Oct 202519 Oct 2025

Publication series

NameLecture Notes in Electrical Engineering
Volume1575 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference5th International Conference on Autonomous Unmanned Systems, ICAUS 2025
Country/TerritoryChina
CityShanghai
Period17/10/2519/10/25

Keywords

  • conflict-based search
  • multi-agent path planning
  • probabilistic roadmap

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