Voice-Controlled Bot Navigation: A Novel NLP Approach in Human-Computer Interaction

Abstract

This paper presents a novel approach to human-computer interaction through voice-controlled navigation of mobile robots, specifically the TurtleBot2i and ROSbot XL, using advanced Natural Language Processing (NLP) techniques. The system integrates the Robot Operating System (ROS) with Python-based speech recognition frameworks, enabling users to control robots through voice commands for navigation and manipulation tasks. The project leverages Large Language Models (LLMs) like OpenAI's Whisper and GPT-4ο models to convert speech into actionable instructions, enhancing accuracy even in noisy environments. Key innovations include real-time voice-to-command translation, flexible interaction with informal commands, and dynamic audio feedback to improve user experience. This work provides a comprehensive analysis of the system's architecture, highlighting the challenges and solutions in developing a reliable voice-controlled robotic system.