In this paper, the service robot named “Black Bot” as receptionist robot is descried, that is a small three-wheeled mobile platform with a differential drive, which was controlled by a miniPc. The Backlot could sense its surroundings with the aid of various electronic sensors while mechanical actuators were used to move it around. Robot’s behaviour was determined by the program, which was loaded to the microcontrollers and Pc. The experiment results demonstrated the feasibility and advantages of this predictive control on the trajectory tracking of a mobile robot. The service robot is designed to assist humans with reception tasks. The robot will interact closely with a group of humans in their everyday environment. This means that it is essential to create models for natural and intuitive communication between humans and robots. In this paper, the service robot named “Black Bot” as receptionist robot is descried, that is a small three-wheeled mobile platform with a differential drive, which was controlled by a mini Pc.

The proposed AI-based voice-controlled robot demonstrates a significant advancement in human–robot interaction by enabling natural and intuitive communication through speech. By integrating speech recognition, Natural Language Processing (NLP), and intelligent control systems, the robot can understand user commands and perform tasks efficiently in real time.The system successfully reduces the complexity of human–machine interaction by eliminating the need for traditional input devices, making it more accessible and user-friendly for a wide range of users, including elderly and physically challenged individuals. Additionally, the incorporation of learning capabilities allows the robot to adapt to user preferences and improve its performance over time. System successfully reduces the complexity of human–machine interaction by eliminating the need for traditional input devices, making it more accessible and user-friendly for a wide range of users, including elderly and challenged individuals. Additionally, the incorporation of learning capabilities allows the robot to adapt to user preferences and improve its performance over time. In conclusion, this project contributes to the growing field of intelligent robotics by bridging the gap between humans and machines, paving the way for future innovations in smart homes, healthcare, education, and service industries.

[1] Lawrence R. Rabiner and Biing-Hwang Juang, “Fundamentals of Speech Recognition”, Prentice Hall, 1993. [2] Daniel Jurafsky and James H. Martin, “Speech and Language Processing”, Pearson, 3rd Edition, 2023. [3] IEEE, “IEEE Standard for Speech Recognition Systems”, IEEE Publications. [4] Tom M. Mitchell, “Machine Learning”, McGraw-Hill, 1997. [5] Google AI, “Speech-to-Text API Documentation”, 2024. [6] OpenAI, “Advances in Natural Language Processing Models”, 2023. [7] Cynthia Breazeal, “Designing Sociable Robots”, MIT Press, 2002. [8] MIT Media Lab, “Human-Robot Interaction Research”, 2022. [9] IEEE Robotics and Automation Society, “Human-Robot Interaction: Trends and Challenges”, 2021. [10] National Institute of Standards and Technology (NIST), “Evaluation of Speech Recognition Ssytem