发表机构
Tsinghua University; State Key Laboratory of Space Network and Communications, Tsinghua University; Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学; 空间网络与通信国家重点实验室（清华大学）; 北京信息科学与技术国家研究中心（清华大学））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对低空经济对无人机协作ISAC的需求，本文提出SI-TokCom框架，通过令牌通信实现具身无人机智能体的高效协作，在保证通信与感知速率的同时降低推进能量，性能优于局部基准。
AI 中文摘要
新兴的低空经济需要由无人机（UAV）支撑的集成感知与通信（ISAC），以实现可靠的连接和环境感知。具体而言，具身无人机智能体通过将感知、决策与物理行动关联的闭环，为支持自主作业提供了一种有前景的手段。然而，每架无人机仅能获取局部观测信息，有效的协作需要交换局部状态与意图。直接共享此类信息会产生大量信令开销，阻碍动态环境下的及时协调。为解决该问题，本文研究了一种由具身无人机智能体组成的协作式ISAC网络，构建了联合令牌通信（TokCom）与物理控制问题，以在满足通信和感知速率要求的前提下最小化总推进能量。随后，本文提出了一种由多智能体具身策略学习驱动的状态-意图令牌通信（SI-TokCom）框架。具体而言，独立预训练的码本可实现局部状态与意图的紧凑交换，而无人机智能体则基于局部观测和接收的令牌，联合学习选择与组合令牌并确定物理行动。仿真结果表明，SI-TokCom分别达到了集中式基准通信速率和感知速率的98.9%与99.1%；与局部基准相比，其对应速率分别提升了5.0%与43.6%，且推进能量基本保持不变。这些结果凸显了TokCom在ISAC系统中实现具身无人机智能体间通信高效协作的潜力。
英文摘要
The emerging low-altitude economy demands unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) for reliable connectivity and environmental awareness. In particular, embodied UAV agents offer a promising means of supporting autonomous operations through a closed loop linking perception, decision-making, and physical actions. However, each UAV has access only to local observations, and effective cooperation requires exchanging local states and intentions. Directly sharing such information can incur substantial signaling overhead and hinder timely coordination in dynamic environments. To deal with this problem, this paper investigates a cooperative ISAC network of embodied UAV agents and formulates a joint token communications (TokCom) and physical control problem to minimize total propulsion energy subject to communication and sensing rate requirements. Then, we propose a state--intent TokCom (SI-TokCom) framework driven by multi-agent embodied policy learning. Specifically, separate pretrained codebooks enable compact exchanges of local states and intentions, while UAV agents jointly learn to select and compose tokens and determine physical actions based on local observations and received tokens. Simulation results show that SI-TokCom achieves 98.9\% and 99.1\% of the centralized baseline's communication and sensing rates, respectively. Compared with the local baseline, it improves the corresponding rates by 5.0\% and 43.6\%, respectively, with essentially unchanged propulsion energy. These results highlight the potential of TokCom for communication-efficient cooperation among embodied UAV agents in ISAC systems.
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