Abstract:Swarm-based target localization in industrial environments faces two major challenges: navigating obstacle-rich spaces and managing intensive communication among agents. This paper proposes a communication-efficient digital twin (DT) framework for Particle Swarm Optimization (PSO)-based swarm navigation and obstacle avoidance. The DT, deployed on a Multi-Access Edge Computing (MEC) server, maintains a virtual replica of the environment to provide global guidance and obstacle bypassing when agents become trapped or experience poor connectivity. By reducing unnecessary peer-to-peer communication and centralizing environmental information, the proposed framework improves both navigation efficiency and communication resource utilization. Simulation results demonstrate that the DT-assisted PSO with obstacle avoidance achieves faster convergence and significantly lower communication load compared with decentralized P2P and random-walk PSO approaches. These findings highlight the potential of integrating DT with swarm intelligence to enhance cooperative exploration in complex industrial scenarios such as chemical leakage localization.
From: Bin Han [view email]
[v1]
Fri, 28 Jun 2024 13:57:51 UTC (728 KB)
[v2]
Tue, 2 Jul 2024 04:19:35 UTC (728 KB)
[v3]
Fri, 25 Jul 2025 12:03:24 UTC (2,614 KB)
[v4]
Sun, 9 Aug 2026 18:48:12 UTC (4,822 KB)
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