Publikation
A Communication-Efficient Digital Twin Framework for PSO-Based Swarm Navigation and Obstacle Avoidance
Siyu Yuan; Khurshid Alam; Dennis Krummacker; Bin Han; Hans Dieter Schotten
In: IEEE Access (IEEE), Vol. 14, Pages 124019-124029, IEEE, 8/2026.
Zusammenfassung
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.
Projekte
- 6G-Terafactory - Open-RAN basiertes Campusnetz für industrielle Echtzeitanwendungen
- Open6GHub+ - Offenes 6G Transferzentrum für Wirtschaft, Wissenschaft und Gesellschaft