Development of a mobile service robot system: enhancing localization, guidance, and search tasks in indoor environments
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Data
2025-08-06
Autores
Rodriguez, Elio David Triana
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Universidade Federal do Espírito Santo
Resumo
Advancements in service robotic systems demand robust Human-Robot Interaction (HRI) strategies capable of operating in multilingual and dynamic environments. However, current HRI approaches based on Natural Language Processing (NLP) often face limitations related to scalability, ambiguity in communication, and dif f iculty in linking unstructured input to structured data, thereby reducing robotic adaptability. This study proposes an HRI framework that integrates NLP through Large Language Models, combined with a decision-making algorithm grounded in Generative Artificial Intelligence (Generative AI) and context-aware reasoning. The system adopts a modular architecture comprising request validation, map valida tion, and response generation, enabling the synthesis and association of structured andunstructured data. As a result, the robot is capable of navigating, guiding users, executing adaptive tasks, and responding to user requests through a chat-style in terface. The framework was implemented on a mobile robot that was structurally, electrically, and software-wise adapted, culminating in the development of an au tonomous system able to complete localization, guidance and search tasks in in door environments. The NLP-based interaction modules were then integrated, and the resulting autonomous responses to user requests were evaluated as satisfactory. Usability evaluations conducted with real users, using the System Usability Scale (SUS), yielded consistently high scores ranging from “good” to “excellent.” How ever, participants reported a slightly lower perception of accuracy and increased frustration when operating in fully autonomous LLM mode compared to a pre programmed control mode. On the other hand, validation experiments demon strated a 91% success rate, confirming the system’s capability to process user re quests andexecutetaskstypicalofguiderobots. Theseresultsvalidatethefeasibility of integrating LLMs into multilingual robotic systems, highlighting both the poten tial and current limitations of NLP in HRI. They also highlight the transformative role of LLMs in enhancing natural language understanding and decision-making in real-world scenarios. Future work should focus on improving the handling of ambiguous user requests and refining feedback mechanisms to enhance the overall user experience.
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Human robot interaction , Large language models , Natural language processing , Mobile robot , Decision-making algorithm , Processamento de linguagem natural , Modelos de linguagem de grande escala , Interação humano-robô , Robô móvel , Algoritmo de tomada de
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