用大模型指导边缘机器人安全，实现工业标准合规的低延迟控制
LLM-Guided Safety Agent for Edge Robotics with an ISO-Compliant Perception-Compute-Control Architecture
- 将自然语言安全规范转为可执行断言，部署于异构边缘运行时
- 采用对称双模冗余设计，实现在边缘约束下的容错闭环执行
- 基于双RK3588平台，达成ISO 13849 Category 3和PL d标准要求
在人机交互中确保功能安全极具挑战，因AI感知具有固有的概率性，而工业标准要求确定性行为。本文提出一种基于大语言模型（LLM）指导的安全代理，构建于符合ISO标准的低延迟感知-计算-控制架构之上。方法将自然语言安全规范转化为可执行谓词，并通过冗余异构边缘运行时进行部署。为在边缘约束下实现容错闭环执行，采用对称双模冗余设计，实现感知、计算与控制的并行独立执行。我们在双RK3588平台原型系统，并在典型人机交互场景中评估。结果表明，该方案以低成本硬件实现了面向ISO 13849 Category 3和PL d的实用边缘部署路径，支持安全关键型具身AI的实际落地。
原文摘要 · Abstract (English)
Ensuring functional safety in human-robot interaction is challenging because AI perception is inherently probabilistic, whereas industrial standards require deterministic behavior. We present an LLM-guided safety agent for edge robotics, built on an ISO-compliant low-latency perception-compute-control architecture. Our method translates natural-language safety regulations into executable predicates and deploys them through a redundant heterogeneous edge runtime. For fault-tolerant closed-loop execution under edge constraints, we adopt a symmetric dual-modular redundancy design with parallel independent execution for low-latency perception, computation, and control. We prototype the system on a dual-RK3588 platform and evaluate it in representative human-robot interaction scenarios. The results demonstrate a practical edge implementation path toward ISO 13849 Category 3 and PL d using cost-effective hardware, supporting practical deployment of safety-critical embodied AI.
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