让无人机实时运行智能任务，动态调度语言模型完成复杂指令。
AERIS: Aerial-Edge Role-Driven Intelligence at Runtime via Orchestrated Language-Model Swarm
- 将小语言模型按角色动态部署于无人机边缘，支持实时任务分解与执行。
- 在心跳定时机制下保持感知-决策-控制闭环，实现长时间任务规划。
- 适合需要快速响应的无人机智能系统开发，尤其适用于资源受限场景。
将大语言模型融入机器人系统可提升自主性，但实际部署受限于严格的周期约束和有限算力。本文提出AERIS：一种面向空中平台的边缘部署框架。它将专用的小型语言模型与轻量级感知和控制模块组织为可动态实例化的角色，并根据资源变化实时重新绑定至不同执行器，从而将智能能力推向边缘。AERIS通过注意力-子目标对齐机制实现长时程指令分解，通过消息中标注当前活跃指令步骤，逐步逼近长期目标。我们在高保真无人机视觉-语言导航基准上评估了AERIS。在心跳定时执行机制下，AERIS维持低频规划器与高频控制器之间的稳定感知-决策-控制循环，支持实时闭环运行。我们进一步通过两项真实世界实验验证其可部署性，聚焦于路径规划与快速响应。演示视频见附录。
原文摘要 · Abstract (English)
Integrating large language models into robotic systems holds promise for enhancing autonomy, yet practical deployment remains constrained by strict heartbeat-constrained scheduling and limited computational power. We propose AERIS: an edge deployment framework for aerial platforms. It organizes dedicated small language models combined with lightweight perception and control modules into roles that can be instantiated at runtime, and dynamically rebinds them across different executors as resources change, thereby pushing intelligent capabilities to the edge. AERIS achieves long-horizon instruction decomposition through an attention-subgoal alignment mechanism, which involves annotating the currently active instruction step in messages, thereby progressively approaching long-term objectives. We evaluate AERIS on a high-fidelity UAV Vision-and-Language Navigation benchmark. Under a heartbeat-timed execution mechanism, AERIS maintains a stable perception-decision-control loop between a low-frequency planner and a high-frequency controller, supporting real-time closed-loop operation. We further validate its deployability through two real-world experiments focused on planning and fast response. A demonstration video is provided in the supplementary materials.
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