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Multi-Robot System for Cooperative Exploration in Unknown Environments: A Survey

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arxiv 2503.07278 v3 pith:ZI4GHRVR submitted 2025-03-10 cs.RO

classification cs.RO
keywords explorationcooperativemulti-robotmodulecommunicationenvironmentsreviewfield
verification ladder T0 review T1 audit T2 compute T3 formal
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With the real need of field exploration in large-scale and extreme outdoor environments, cooperative exploration tasks have garnered increasing attention. This paper presents a comprehensive review of multi-robot cooperative exploration systems. First, we review the evolution of robotic exploration and introduce a modular research framework tailored for multi-robot cooperative exploration. Based on this framework, we systematically categorize and summarize key system components. As a foundational module for multi-robot exploration, the localization and mapping module is primarily introduced by focusing on global and relative pose estimation, as well as multi-robot map merging techniques. The cooperative motion module is further divided into learning-based approaches and multi-stage planning, with the latter encompassing target generation, task allocation, and motion planning strategies. Given the communication constraints of real-world environments, we also analyze the communication module, emphasizing how robots exchange information within local communication ranges and under limited transmission capabilities. In addition, we introduce the actual application of multi-robot cooperative exploration systems in DARPA SubT Challenge. Finally, we discuss the challenges and future research directions for multi-robot cooperative exploration in light of real-world trends. This review aims to serve as a valuable reference for researchers and practitioners in the field.

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Cited by 2 Pith papers

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  1. A Closed-Form 4-DoF Inter-Robot Pose Estimator using Bearing-only Measurements

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    A closed-form 4-DoF inter-robot pose estimator is proposed using bearing-only measurements, with observability analysis showing it requires less stringent motion excitation than 6-DoF methods and an autonomous test mo...

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    Median-based plug-and-play reweighting of attention neighbors improves accident-detection rates under five LiDAR corruptions in simulated connected autonomous driving without retraining.

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