Abstract

This study introduces the development of an interactive robotic companion designed to enhance human-robot interaction (HRI) through mobility, animatronic eye movements, and natural language processing (NLP). Targeting broader accessibility, the prototype aims to provide companionship, especially for elderly individuals and those with physical limitations. The system integrates an Arduino MEGA 2560 microcontroller with key components, including an Echo Dot for NLP and voice recognition, servo-controlled animatronic eyes for lifelike responses, a TV CRT for visual feedback, and 360-degree wheels for full-range mobility. User feedback highlights high satisfaction with the robot’s responsiveness, natural interactions, and ease of use. Performance metrics reveal a power efficiency of 85%, an average response time of 1.2 seconds, and seamless mobility across various surfaces. The robot engages users through real-time responses to presence and voice commands, leveraging lifelike eye movements and interactive displays. Implementation challenges, such as sourcing compatible CRT displays and managing complex wiring, were effectively mitigated to ensure system reliability. Future work will focus on optimizing power consumption, expanding interaction capabilities, and enhancing accessibility for diverse user groups, establishing a scalable framework for AI-driven robotic companions in healthcare and other domains.

Keywords:Interactive robotic companion, animatronic eye movements, Echo Dot, human-robot interaction, voice recognition, remote control interface, power efficiency.

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1. White GNT, Cagiltay B, Michaelis JE, Mutlu B. Designing emotionally expressive social commentary to facilitate child-robot interaction. In: Interaction Design and Children. Athens, Greece: ACM; 2021. 314–325. doi:10.1145/3459990.3460714.
2. Kok CL, Ho CK, Teo TH, Kato K, Koh YY. A novel implementation of a social robot for sustainable human engagement in homecare services for ageing populations. Sensors. 2024;24(14):4466. doi:10.3390/s24144466.
3. Robinson H, MacDonald B, Broadbent E. The role of healthcare robots for older people at home: A review. Int J Soc Robot. 2014;6(4):575–591.
4. Cooper S, Di Fava A, Vivas C, Marchionni L, Ferro F. ARI: The social assistive robot and companion. 29^th International Conference on Robot and Human Interactive Communication (RO-MAN). Naples, Italy. 2020, Aug 31–Sept 4. 745–751. IEEE. doi:10.1109/RO-MAN47096.2020.9223470.
5. Pollmann K, Loh W, Fronemann N, Ziegler D. Entertainment vs. manipulation: Personalized human-robot interaction between user experience and ethical design. Technol Forecast Soc Change. 2023;189:122376. doi:10.1016/j.techfore.2023.122376.
6. Fujii A, Fukuda K. Initial study on robot emotional expression using Manpu. In: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. Boulder, CO, USA: ACM; 2024. 463–467. doi:10.1145/3610978.3640652.
7. Aly A, Tapus A. On designing expressive robot behavior: The effect of affective cues on interaction. SN Comput Sci. 2020;1(6):314. doi:10.1007/s42979-020-00263-3.
8. Hoffman G, Zuckerman O, Hirschberger G, Luria M, Shani Sherman T. Design and evaluation of a peripheral robotic conversation companion. In: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction. Portland, Oregon, USA: ACM; 2015. 3–10. doi:10.1145/2696454.2696495.
9. Marti P, Giusti L. A robot companion for inclusive games: A user-centred design perspective. International Conference on Robotics and Automation. Anchorage, AK. 2010, May 3–7. 4348–4353. IEEE. doi:10.1109/ROBOT.2010.5509385.
10. Whittaker S, Rogers Y, Petrovskaya E, Zhuang H. Designing personas for expressive robots: Personality in the new breed of moving, speaking, and colorful social home robots. J Hum-Robot Interact. 2021;10(1):1–25. doi:10.1145/3424153.
11. García-Madurga MÁ, Grilló-Méndez AJ. Artificial intelligence in the tourism industry: An overview of reviews. Adm Sci. 2023;13(8):172.
12. Kumar R, et al. Speed control of DC motor using pulse width modulation. Department of Electrical Engineering, RCC Institute of Information Technology. Kolkata, West Bengal; 2019.
13. Abul Kalam Azad University of Technology (MAKAUT).
14. Kapri N. Performance analysis and comparative study of different types of DC-DC converters. Int Res J Adv Sci Hub. 2019;1(2):5–10.
15. Yilmazlar İ. Single phase inverter design for LCD TV [Master's thesis]. Izmir, Turkey: Dokuz Eylul University; 2007.
16. Kassim AM, et al. Performance study of distance measurement sensor with different object materials and properties. 3rd International Conference on System Engineering and Technology. Shah Alam, Malaysia. 2013, Aug 19-20. 281–284. IEEE. doi:10.1109/ICSEngT.2013.6650185.
17. Möbius C, Dargie W, Schill A. Power consumption estimation models for processors, virtual machines, and servers. IEEE Trans Parallel Distrib Syst. 2013;25(6):1600–1614.

Abstract

This study introduces the development of an interactive robotic companion designed to enhance human-robot interaction (HRI) through mobility, animatronic eye movements, and natural language processing (NLP). Targeting broader accessibility, the prototype aims to provide companionship, especially for elderly individuals and those with physical limitations. The system integrates an Arduino MEGA 2560 microcontroller with key components, including an Echo Dot for NLP and voice recognition, servo-controlled animatronic eyes for lifelike responses, a TV CRT for visual feedback, and 360-degree wheels for full-range mobility. User feedback highlights high satisfaction with the robot’s responsiveness, natural interactions, and ease of use. Performance metrics reveal a power efficiency of 85%, an average response time of 1.2 seconds, and seamless mobility across various surfaces. The robot engages users through real-time responses to presence and voice commands, leveraging lifelike eye movements and interactive displays. Implementation challenges, such as sourcing compatible CRT displays and managing complex wiring, were effectively mitigated to ensure system reliability. Future work will focus on optimizing power consumption, expanding interaction capabilities, and enhancing accessibility for diverse user groups, establishing a scalable framework for AI-driven robotic companions in healthcare and other domains.

Keywords:Interactive robotic companion, animatronic eye movements, Echo Dot, human-robot interaction, voice recognition, remote control interface, power efficiency.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. White GNT, Cagiltay B, Michaelis JE, Mutlu B. Designing emotionally expressive social commentary to facilitate child-robot interaction. In: Interaction Design and Children. Athens, Greece: ACM; 2021. 314–325. doi:10.1145/3459990.3460714.
2. Kok CL, Ho CK, Teo TH, Kato K, Koh YY. A novel implementation of a social robot for sustainable human engagement in homecare services for ageing populations. Sensors. 2024;24(14):4466. doi:10.3390/s24144466.
3. Robinson H, MacDonald B, Broadbent E. The role of healthcare robots for older people at home: A review. Int J Soc Robot. 2014;6(4):575–591.
4. Cooper S, Di Fava A, Vivas C, Marchionni L, Ferro F. ARI: The social assistive robot and companion. 29^th International Conference on Robot and Human Interactive Communication (RO-MAN). Naples, Italy. 2020, Aug 31–Sept 4. 745–751. IEEE. doi:10.1109/RO-MAN47096.2020.9223470.
5. Pollmann K, Loh W, Fronemann N, Ziegler D. Entertainment vs. manipulation: Personalized human-robot interaction between user experience and ethical design. Technol Forecast Soc Change. 2023;189:122376. doi:10.1016/j.techfore.2023.122376.
6. Fujii A, Fukuda K. Initial study on robot emotional expression using Manpu. In: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. Boulder, CO, USA: ACM; 2024. 463–467. doi:10.1145/3610978.3640652.
7. Aly A, Tapus A. On designing expressive robot behavior: The effect of affective cues on interaction. SN Comput Sci. 2020;1(6):314. doi:10.1007/s42979-020-00263-3.
8. Hoffman G, Zuckerman O, Hirschberger G, Luria M, Shani Sherman T. Design and evaluation of a peripheral robotic conversation companion. In: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction. Portland, Oregon, USA: ACM; 2015. 3–10. doi:10.1145/2696454.2696495.
9. Marti P, Giusti L. A robot companion for inclusive games: A user-centred design perspective. International Conference on Robotics and Automation. Anchorage, AK. 2010, May 3–7. 4348–4353. IEEE. doi:10.1109/ROBOT.2010.5509385.
10. Whittaker S, Rogers Y, Petrovskaya E, Zhuang H. Designing personas for expressive robots: Personality in the new breed of moving, speaking, and colorful social home robots. J Hum-Robot Interact. 2021;10(1):1–25. doi:10.1145/3424153.
11. García-Madurga MÁ, Grilló-Méndez AJ. Artificial intelligence in the tourism industry: An overview of reviews. Adm Sci. 2023;13(8):172.
12. Kumar R, et al. Speed control of DC motor using pulse width modulation. Department of Electrical Engineering, RCC Institute of Information Technology. Kolkata, West Bengal; 2019.
13. Abul Kalam Azad University of Technology (MAKAUT).
14. Kapri N. Performance analysis and comparative study of different types of DC-DC converters. Int Res J Adv Sci Hub. 2019;1(2):5–10.
15. Yilmazlar İ. Single phase inverter design for LCD TV [Master's thesis]. Izmir, Turkey: Dokuz Eylul University; 2007.
16. Kassim AM, et al. Performance study of distance measurement sensor with different object materials and properties. 3rd International Conference on System Engineering and Technology. Shah Alam, Malaysia. 2013, Aug 19-20. 281–284. IEEE. doi:10.1109/ICSEngT.2013.6650185.
17. Möbius C, Dargie W, Schill A. Power consumption estimation models for processors, virtual machines, and servers. IEEE Trans Parallel Distrib Syst. 2013;25(6):1600–1614.