  Safety limitations in service robotics across various industries have raised
significant concerns about the need for robust mechanisms ensuring that robots
adhere to safe practices, thereby preventing actions that might harm humans or
cause property damage. Despite advances, including the integration of Knowledge
Graphs (KGs) with Large Language Models (LLMs), challenges in ensuring
consistent safety in autonomous robot actions persist. In this paper, we
propose a novel integration of Large Language Models with Embodied Robotic
Control Prompts (ERCPs) and Embodied Knowledge Graphs (EKGs) to enhance the
safety framework for service robots. ERCPs are designed as predefined
instructions that ensure LLMs generate safe and precise responses. These
responses are subsequently validated by EKGs, which provide a comprehensive
knowledge base ensuring that the actions of the robot are continuously aligned
with safety protocols, thereby promoting safer operational practices in varied
contexts. Our experimental setup involved diverse real-world tasks, where
robots equipped with our framework demonstrated significantly higher compliance
with safety standards compared to traditional methods. This integration fosters
secure human-robot interactions and positions our methodology at the forefront
of AI-driven safety innovations in service robotics.
