Decision-Making Algorithm Based on Multiple Context-Aware Agents for Human-Robot Interaction

Decision-making in robotic systems within real-world environments involves significant challenges, such as interpreting ambiguous user requests, integrating unstructured contextual data, and effectively utilizing sensory information. Current approaches in Human-Robot Interaction (HRI), which rely on Natural Language Processing (NLP) techniques, often encounter limitations such as scalability, ambiguity in communication, and the inability to relate unstructured data to structured data. These constraints reduce robots' adaptability and limit their functional flexibility in dynamic environments. This study proposes a novel decision-making algorithm that integrates Generative Artificial Intelligence with context-aware systems and utilizes a modular framework comprising request validation, map validation, and response generation. By leveraging Large Language Models (LLMs) and Retrieval-Augmented Generation strategies, the system efficiently synthesizes and relates structured and unstructured data, enabling robots to navigate, guide users, and perform adaptive functions. Validation experiments demonstrated a 91% success rate, showcasing the system's ability to process user requests and execute tasks typical of guide robots. This highlights the transformative role of LLMs in NLP, revolutionizing HRI and enhancing decision-making in real-world scenarios.