面向基于粒子群优化（PSO）的集群导航与避障的通信高效数字孪生框架
A Communication-Efficient Digital Twin Framework for PSO-Based Swarm Navigation and Obstacle Avoidance
AI总结：
针对工业集群目标定位的避障与通信难题，提出部署于MEC服务器的DT辅助PSO框架，可减少通信负载、加快收敛，在复杂工业协同探索场景具有应用潜力。
摘要：
工业环境中基于集群的目标定位面临两大主要挑战：在障碍物密集的空间中导航，以及管理智能体之间密集的通信。本文提出一种通信高效的数字孪生（DT）框架，用于基于粒子群优化（PSO）的集群导航与避障。该DT部署在多接入边缘计算（MEC）服务器上，维护环境的虚拟副本，以便在智能体陷入困境或连通性差时提供全局引导和避障路径。通过减少不必要的点对点通信并集中环境信息，所提框架同时提升了导航效率和通信资源利用率。仿真结果表明，与分布式点对点PSO和随机游走PSO方法相比，带避障的DT辅助PSO实现了更快的收敛速度和显著更低的通信负载。这些发现凸显了将DT与集群智能相结合，以增强复杂工业场景（如化学泄漏定位）中协同探索的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
