A Concept for Bio-Agentic Visual Communication: Bridging Swarm Intelligence with Biological Analogues

Area of Science:

• Robotics
• Artificial Intelligence
• Bio-inspired Engineering

Background:

• Biological swarms exhibit decentralized, adaptive communication essential for coordination without central control.
• Existing unmanned aerial vehicle (UAV) communication systems face limitations in radio-frequency-denied environments.

Purpose of the Study:

• To develop a bio-inspired visual communication system for UAV swarms operating in radio-frequency-denied environments.
• To translate biological communication principles into a generative visual language for UAV agents.
• To evaluate the effectiveness of bio-agentic communication in preserving and adapting signal meaning.

Main Methods:

• Constructed a configuration space encoding visual messages via trajectories and LED patterns, inspired by natural animal signals (e.g., bee waggle dance, deer flagging).
• Utilized a large language model (LLM) with retrieval-augmented generation (RAG) for interpreting perception data and generating symbolic UAV responses.
• Evaluated the system through five test cases assessing within-modality fidelity and cross-modal translation, using covariance and eigenvalue-decomposition analysis.

Main Results:

• Motion-based signaling demonstrated near-perfect clarity (0.992) and expressiveness (1.000), indicating highly effective decentralized communication.
• LED-only and multi-signal communication achieved high expressiveness (~1.000) but significantly lower clarity (≤0.298).
• The bio-agentic approach supports clear, expressive, and decentralized communication.

Conclusions:

• Biological communication principles can be effectively translated into visual languages for UAV swarms.
• Motion-based visual signaling offers a robust solution for decentralized communication in challenging RF-denied environments.
• Further research is needed to optimize multi-modal signaling for enhanced clarity and expressiveness.