PSOベース群ロボットナビゲーションと障害物回避のための通信効率の良いデジタルツインフレームワーク
A Communication-Efficient Digital Twin Framework for PSO-Based Swarm Navigation and Obstacle Avoidance
MECサーバ上のデジタルツインが環境の仮想レプリカを保持し、PSOによる群ロボットのナビゲーションと障害物回避を支援することで、通信負荷を抑えつつ収束を高速化するフレームワークを提案した。
著者: Siyu Yuan, Khurshid Alam, Bin Han, Dennis Krummacker, Hans D. Schotten
分類: cs.RO, cs.MA
原文アブストラクト
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.
