Embodied Agentic Intelligence for LAWNs: Joint Collision Avoidance Path Planning and Predictive Beamforming Design

Abstract

Low-altitude wireless networks (LAWNs) are emerging as a foundational infrastructure for the low-altitude economy, necessitating unmanned aerial vehicles (UAVs) to operate as embodied wireless agents that can navigate complex air-ground environments. This paper investigates the critical challenge of maintaining reliable and covert information transmission for such agents under stringent mobility and detection constraints. To address this, we propose an embodied agentic framework that synergizes safety-critical path planning with cognitive predictive beamforming through a hierarchical execution-reasoning loop. Specifically, the execution layer utilizes model predictive control (MPC) to solve a constrained optimization problem, linearizing obstacle-avoidance requirements into tractable dual-variable inequalities to ensure collision-free navigation. This generates a deterministic sequence of future coordinates that serves as a spatial intent prior to reduce environmental uncertainty. Subsequently, the cognitive reasoning layer leverages a large language model (LLM) to integrate these motion priors with sensing-assisted historical channel state information and warden locations through a cross-modal attention mechanism. By capturing the intricate dependencies between the agent kinematic states and the non-stationary wireless channel, the LLM proactively optimizes predictive beamforming to satisfy covertness requirements against mobile wardens. Simulation results validate the efficacy of this joint design, demonstrating that the UAV successfully tracks reference trajectories while maintaining safety margins in obstacle-cluttered environments. Furthermore, the proposed framework achieves superior covertness performance compared to conventional benchmarks.