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

2025-03-10

AI Summary

This paper addresses three core challenges in multi-robot collaborative exploration in unknown environments: inconsistent localization and mapping, highly coupled motion planning, and communication constraints. To tackle these, it proposes the first modular taxonomy specifically designed for collaborative exploration, decomposing the technical stack into three principal modules: (i) distributed SLAM with multi-map fusion, (ii) learning-driven multi-stage collaborative motion planning, and (iii) topology control and task allocation under locally restricted communication. By systematically integrating graph matching, distributed optimization, and multi-agent reinforcement learning, the work unifies key research directions—including global/relative pose estimation, heterogeneous map alignment, and joint communication-sensing optimization. The survey comprehensively covers the full technical pipeline, identifies critical bottlenecks (e.g., consistency maintenance under dynamic topologies, robust coordination under sparse communication), and highlights emerging trends such as semantic-enhanced mapping and neuro-symbolic collaborative planning—providing a structured theoretical foundation for algorithm design and system deployment.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Multiagent Systems: Coordination and Collaboration
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

With the advancement of multi-robot technology, 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. 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.

Problem

• Review multi-robot cooperative exploration systems evolution.
• Analyze localization, mapping, and communication modules challenges.
• Discuss future research directions for real-world applications.

Innovation

• Modular framework for multi-robot exploration
• Localization and mapping with pose estimation
• Learning-based and multi-stage motion planning