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This paper introduces an LLM-guided safety agent for edge robotics that translates natural language safety regulations into executable predicates within an ISO-compliant architecture. The system employs a symmetric dual-modular redundancy design with parallel execution to achieve fault-tolerant closed-loop control under edge constraints. Experiments on a dual-RK3588 platform demonstrate a pathway to achieving ISO 13849 Category 3 and PL d compliance using cost-effective hardware.
Achieve ISO 13849 Category 3 and PL d safety levels for edge robots using LLMs and commodity hardware.
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.
