6G时代多智能体协同感知与决策系统，提升救援任务完成率与通信效率。
Agentic AI-Empowered Conversational Embodied Intelligence Networks in 6G
- 融合视觉与雷达数据生成统一语义表示，支持跨模态感知。
- 动态调整编码与功率，实现95%传输效率与95.4%任务完成率。
- 可视化决策过程，适合高可靠性救援等场景应用。
在6G时代，多个具身智能设备（MEIDs）之间的语义协作对复杂任务执行至关重要。然而现有系统面临多模态信息融合、自适应通信及决策可解释性不足等问题。为此，我们提出一种协同式对话式具身智能网络（CC-EIN），集成多模态特征融合、自适应语义通信、任务协调与可解释性功能。PerceptiNet实现图像与雷达数据的跨模态融合，生成统一语义表征；自适应语义通信策略根据任务紧急程度与信道质量动态调整编码方案与传输功率；语义驱动的协作机制支持异构设备间的任务分解与无冲突协调；最后，InDec模块通过Grad-CAM可视化增强决策透明度。仿真结果在地震后救援场景中显示，CC-EIN实现95.4%的任务完成率和95%的传输效率，同时保持强语义一致性与能效表现。
原文摘要 · Abstract (English)
In the 6G era, semantic collaboration among multiple embodied intelligent devices (MEIDs) becomes crucial for complex task execution. However, existing systems face challenges in multimodal information fusion, adaptive communication, and decision interpretability. To address these limitations, we propose a collaborative Conversational Embodied Intelligence Network (CC-EIN) integrating multimodal feature fusion, adaptive semantic communication, task coordination, and interpretability. PerceptiNet performs cross-modal fusion of image and radar data to generate unified semantic representations. An adaptive semantic communication strategy dynamically adjusts coding schemes and transmission power according to task urgency and channel quality. A semantic-driven collaboration mechanism further supports task decomposition and conflict-free coordination among heterogeneous devices. Finally, the InDec module enhances decision transparency through Grad-CAM visualization. Simulation results in post-earthquake rescue scenarios demonstrate that CC-EIN achieves 95.4% task completion rate and 95% transmission efficiency while maintaining strong semantic consistency and energy efficiency.
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