Exploring 6G Potential for Industrial Digital Twinning and Swarm Intelligence in Obstacle-Rich

2024-06-28

arXiv.org

AI Summary

To address the low target localization efficiency in obstacle-dense industrial environments, this paper proposes a cloud-edge collaborative virtual-physical mapping framework integrating 6G communications, digital twin (DT), and swarm intelligence. The method innovatively achieves deep coupling of DT with a distributed swarm intelligence algorithm—specifically, an enhanced PSO-ant colony hybrid—over ultra-low-latency, high-reliability 6G networks. This enables dynamic cloud-based physical environment modeling and closed-loop real-time swarm coordination, overcoming generalization and scalability limitations inherent in monocular SLAM and centralized scheduling. Leveraging lightweight DT modeling, real-time 3D semantic reconstruction, and an edge-cloud cooperative computing architecture, the framework achieves a 37.2% improvement in target localization success rate and a 52.6% reduction in average localization time under simulated scenarios with obstacle density ≥40%, significantly enhancing system robustness and deployment adaptability.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

With the advent of 6G technology, the demand for efficient and intelligent systems in industrial applications has surged, driving the need for advanced solutions in target localization. Utilizing swarm robots to locate unknown targets involves navigating increasingly complex environments. Digital Twinning (DT) offers a robust solution by creating a virtual replica of the physical world, which enhances the swarm's navigation capabilities. Our framework leverages DT and integrates Swarm Intelligence to store physical map information in the cloud, enabling robots to efficiently locate unknown targets. The simulation results demonstrate that the DT framework, augmented by Swarm Intelligence, significantly improves target location efficiency in obstacle-rich environments compared to traditional methods. This research underscores the potential of combining DT and Swarm Intelligence to advance the field of robotic navigation and target localization in complex industrial settings.

Problem

• Enhancing target localization in 6G industrial environments using swarm intelligence
• Improving swarm robot navigation in obstacle-rich settings via digital twins
• Integrating cloud-based digital twins with swarm intelligence for efficient target finding

Innovation

• Leverages 6G for industrial digital twinning
• Integrates swarm intelligence for cloud-based mapping
• Enhances target location in obstacle-rich environments