Implicit semantic control manifolds for learning-enabled multi-UAV coordination

doi:10.1007/s10514-026-10265-4

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Implicit semantic control manifolds for learning-enabled multi-UAV coordination

2026-09-01

DOI：10.1007/s10514-026-10265-4

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Abstract

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Unmanned aerial vehicle (UAV) swarm agents operating in radio-frequency (RF)-degraded environments require coordination mechanisms that connect directly observable signals to executable responses. This work presents an embodied visual-communication approach in which bio-inspired motion–LED glyphs are represented by a reduced six-parameter semantic chart embedded within a full 24-dimensional hybrid execution manifold. A learned translator large language model (LLM) maps a perceived glyph to a response that is instantiated as an executable trajectory and propagated through closed-loop quadrotor dynamics. The system is evaluated in 200 three-UAV search-and-rescue trials with receiver-specific degradation from sensing range, field of view, occlusion, and relative motion. Under clean observations, the quantized small model and rule-based translator both achieve $$100\%$$ semantic correctness, but under single- and two-parameter corruption, the quantized small model achieves $$64.7\%$$ and $$64.1\%$$ , compared with $$44.8\%$$ and $$35.2\%$$ for rule-based, while reducing mean multi-agent trajectory error from $$2.902~\textrm{m}$$ to $$0.993~\textrm{m}$$ . All methods maintain $$100\%$$ rotor-allocation and finite-horizon feasibility. The quantized small model also has comparable overall semantic correctness with the large model ( $$83.3\%$$ vs. $$86.3\%$$ ) while dropping latency from $$5.115~\textrm{s}$$ to $$2.789~\textrm{s}$$ and is validated onboard a Jetson–Pixhawk UAV platform, where airborne inference and bounded command execution produce measurable physical motion. These results establish a unified pathway from degraded visual observation to semantically meaningful, dynamically grounded UAV coordination.

Keywords:

Multi-UAV systems

Autonomous aerial robotics

Embodied intelligence

Bio-inspired communication

Learning-enabled control

Large language models