Embodied UAV agents share tokens to save energy and boost cooperation
An Embodied Multiagent Framework Based on Token Communications for Cooperative ISAC
Multiagent Systems
Summary
Flying drones need to work together efficiently to sense their environment and communicate, but sharing all information directly can slow them down and waste energy. The authors propose a way for drones to talk using compact tokens that represent their local state and intentions. This lets them coordinate better while using nearly the same amount of propulsion energy. Their tests show this token system works almost as well as a perfect centralized setup but uses fewer communication resources.
What this means in practice
- •For drone fleet operators: Enable drone fleets to coordinate sensing and communication more efficiently using token-based message exchanges.
- •For autonomous vehicle system designers: Incorporate compact token communications into multi-agent robotic systems to reduce communication overhead during cooperative tasks.
Authors
Jiahe Guo, Jun Du, Chunxiao Jiang, Jintao Wang
Abstract
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.