Citation

Attaullah, Hafiz Muhammad and Ehsan, Muhammad and Basheer, Shakila and Kumar, Atul (2026) Edge-Enabled Collaborative Localization With Task-Oriented Communication for Multi-Agent Embodied AI in Smart Factories. IEEE Internet of Things Magazine. pp. 1-7. ISSN 2576-3180

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Abstract

Smart factories increasingly rely on embodied AI agents such as autonomous mobile robots and automated guided vehicles to perform real-time manufacturing, logistics, and inspection tasks. These systems require accurate and low-latency localization to enable safe navigation and coordinated multi-agent operations within dynamic industrial environments. However, conventional localization approaches often depend on centralized processing or raw multi-modal data transmission, which leads to excessive communication overhead, increased latency, and limited scalability in Industrial Internet of Things (IIoT) networks. This article presents an edge-enabled collaborative localization framework that integrates multi-agent sensing, task-oriented semantic communication, and edge-based collaborative fusion for smart factory environments. Instead of transmitting raw sensor streams, the proposed framework extracts task-relevant semantic features and transmits compact feature vectors to an edge server, significantly reducing bandwidth consumption while maintaining accurate localization. The edge layer performs collaborative pose fusion and global map refinement across multiple robots, enabling scalable and real-time localization under IIoT communication constraints. A case study-based evaluation demonstrates that the proposed approach improves localization accuracy, reduces communication latency, and enhances scalability compared with conventional cloud-centric and non-collaborative localization methods. The results highlight the potential of combining edge intelligence and semantic communication to support reliable multi-agent coordination in next-generation smart factory systems.

Item Type:	Article
Uncontrolled Keywords:	Collaborative localization, multi-agent systems, embodied AI, semantic communication, IIoT
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management > HD30.2 Electronic data processing. Information technology. Including artificial intelligence and knowledge management
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	04 Aug 2026 07:02
Last Modified:	04 Aug 2026 07:02
URII:	http://shdl.mmu.edu.my/id/eprint/16498

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