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An Intelligent System-Based Strategic Plan for a Humanoid Robot Playing the Game of Dominoes
Plan estratégico basado en sistemas inteligentes para el juego de dominó por parte de un robot humanoide
DOI:
https://doi.org/10.15446/ing.investig.108904Keywords:
computer vision, decision tree, dominoes, board games, forward kinematics, human robot interaction, image processing, intelligent system, NAO robot, robotics (en)visión por computadora, árbol de decisión, dominó, juegos de mesa, cinemática inversa, interacción humano-robot, procesamiento de imágenes, sistema inteligente, robot NAO, robótica (es)
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The application of intelligent systems in humanoid robots provides research and development alternatives, as is the case with human-robot interaction. This paper focuses on the design and implementation of an intelligent system in the NAO robot to plan and execute moves in the board game known as dominoes. This system uses the NAO robot’s vision to determine the distribution of tiles on the board, as well as those available in hand. The appropriate moves are planned using a computational intelligence technique, and a kinematics model executes them. The results show that the vision system has an average error of 5.62%, in addition to 3.37% for the decision-making system and 7.87% for the kinematics of the robot. This leads to the NAO robot being capable of making successful plays through the proposed system, with an average effectiveness of 83.15%.
La aplicación de sistemas inteligentes en robots humanoides brinda alternativas de investigación y desarrollo, como es el caso de la interacción humano-robot. Este trabajo se enfoca en el diseño e implementación de un sistema inteligente en el robot NAO para planificar y ejecutar movimientos en el juego de mesa conocido como dominó. Este sistema utiliza el sistema de visión del robot NAO para determinar la distribución de fichas en el tablero y de las disponibles en la mano. Los movimientos adecuados se calculan mediante una técnica de inteligencia computacional, y un modelo de cinemática los ejecuta. Los resultados muestran que el sistema de visión tiene un error promedio del 5.62 %, ası como del 3.37 % para el sistema de decisión y de 7.87 % para la cinemática del robot. Esto lleva a que, a través del sistema propuesto, el robot NAO sea capaz de realizar jugadas exitosas con una efectividad promedio del 83.15 %.
References
Ajani, H., Obasekore, B., Kang, and Rammohan, M. (2023). Robotic assistance in radiology: A covid-19 scenario. IEEE Access, 11, 49785–49793.
Asfour, et al. (2018). Armar-6: A collaborative humanoid robot for industrial environments. In 2018 ieee-ras 18th int. conf. humanoid robots (humanoids) (pp. 447–454).
Barakeh, S., Alkork, A., Karar, S., Said, S., and Beyrouthy, T. (2019). Pepper humanoid robot as a service robot: A customer approach. In 2019 3rd int. conf. bio-eng. smart tech. (biosmart) (pp. 1–4).
Barnes, J., FakhrHosseini, S., Vasey, E., Ryan, J., Park, C., and Jeon, M. (2019). Promoting steam education with child-robot musical theater. In 2019 14th acm/ieee int. conf. human-robot interact. (hri) (p. 366).
Bi, Q., Yang, M., Zhang, A., Goupil, A., and Feng, L. (2018). Accurate football detection and localization for nao robot with the improved hog svm approach. In 2018 chinese autom. cong. (cac) (pp. 567–571).
Budiharto, W., Cahyani, A. D., Rumondor, P. C. B., and Suhartono, D. (2017). Edurobot: intelligent humanoid robot with natural interaction for education and entertainment. Procedia Comp. Sci., 116, 564–570.
Cao, J., et al. (2020). Robot-assisted joint attention: A comparative study between children with autism spectrum disorder and typically developing children in interaction with nao. IEEE Access, 8, 1–1.
Dı́az, J., Shaik, J., Santofimio, J., and Quintero, M. (2018). Intelligent execution of behaviors in a nao robot exposed to audiovisual stimulus. In 2018 ieee 2nd colombian conf. robotics automat. (ccra) (pp. 1–6).
Feidakis, I., Gkolompia, A., Marnelaki, K., Marathaki, S., Emmanouilidou, S., and Agrianiti, E. (2023). Nao robot, an educational assistant in training, educational and therapeutic sessions. In 2023 ieee global eng. educ. conf. (educon) (pp. 1–6).
Goenaga, L., Navarro, C., Quintero, M., and Pardo, M. (2020). Imitating human emotions with a nao robot as interviewer playing the role of vocational tutor. Electronics, 9.
Hu, F., Zhao, J., Meng, and Wu, S. (2020). Application of deep reinforcement learning in the board game. In 2020 ieee int. conf. info. tech. big data art. intel. (iciba) (Vol. 1, pp. 809–812).
Inoue, F., Jimenez, T., Haruta, M., and Oonuki, M. (2022). Effect of impression on learners during partnered robots learning programming while playing board games against each other. In 2022 joint 12th int. conf. soft comp. intel. syst. and 23rd int. symp. adv. intel. syst. (scis&isis) (pp. 1–4).
Jeon, M., et al. (2017). Robot opera: A modularized afterschool program for steam education at local elementary school. In 2017 14th int. conf. ubiquitous robots ambient intel. (urai) (pp. 935–936).
Juang. (2022, Jan.). Humanoid robots play chess using visual control. Multimedia Tools App., 81, 1–22.
Juang, and Zhang, J. (2019). Visual tracking control of humanoid robot. IEEE Access, 7, 29213–29222.
Karmanova, V., Serpiva, S., Perminov, A., Fedoseev, A., and Tsetserukou, D. (2021). Swarmplay: Interactive tic-tac-toe board game with swarm of nano-uavs driven by reinforcement learning. In 2021 30th ieee int. conf. robot human interac. comm. (ro-man) (pp. 1269–1274).
Karunanayake, et al. (2020). Towards a smart opponent for board games: Learning beyond simulations. In 2020 ieee int. conf. syst. man cyber. (smc) (pp. 1943–1950).
Knox, and Watanabe, K. (2018). Aibo robot mortuary rites in the japanese cultural context. In 2018 ieee/rsj int. conf. intel. robots syst. (iros) (pp. 2020–2025).
Kofinas, E., Orfanoudakis, M., and Lagoudakis, M. (2013). Complete analytical inverse kinematics for nao. In 2013 13th int. conf. autonom. robot syst. (pp. 1–6).
Kołosowski, A., Wolniakowski, K., and Miatliuk, K. (2020). Collaborative robot system for playing chess. In 2020 int. conf. mechatronic sys. mat. (msm) (pp. 1–6).
Lestriandoko, and Sadikin, R. (2016). Circle detection based on hough transform and mexican hat filter. In 2016 int. conf. control infor. app. (ic3ina) (pp. 153–157).
Li, E., Imeokparia, M., Ketzner, M., and Tsahai, T. (2019). Teaching the nao robot to play a human-robot interactive game. In 2019 int. conf. comp. sci. comp. intel. (csci) (pp. 712–715).
Lin, S., Ng, and Sebo, S. (2022). Benefits of an interactive robot character in immersive puzzle games. In 2022 31st ieee int. conf. robot human interac. comm. (ro-man) (pp. 37–44).
Lobos-Tsunekawa, F., Leiva, S., and Solar, J. (2018). Visual navigation for biped humanoid robots using deep reinforcement learning. IEEE Robotics Automat. Lett., 3, 3247–3254.
Mercier, and Lubart, T. (2021, Feb.). The effects of board games on creative potential. J. Creat. Behavior, 55.
Mohan, and Kuchenbecker, K. (2019). A design tool for therapeutic social-physical human-robot interactions. In 2019 14th acm/ieee int. conf. human-robot interac. (hri) (pp. 727–729).
Moya, E., Slawiñski, V., Mut, B., and Wagner, B. (2021, May). Intercontinental bilateral-by-phases teleoperation of a humanoid robot. IEEE Latin Amer. Trans., 20, 64–72.
Ovalle-Magallanes, R., et al. (2021). Transfer learning for humanoid robot appearance-based localization in a visual map. IEEE Access, 9, 6868–6877.
Patil, D., Fegade, A., Kadam, P., Patil, N., and Singhaniya, N. (2021). A novel framework for robotic chess. In 2021 2nd int. smart elec. comm. (icosec) (pp. 1–6).
Piperakis, M., Koskinopoulou, A., and Trahanias, P. (2018). Nonlinear state estimation for humanoid robot walking. IEEE Robotics Automat. Lett., 3, 3347–3354.
Raghavan, H. M. B., Srinivasan, R., Dey, S., and Chandar, T. (2021). Automated laser alignment and image processing-based robotic carrom player. In 2021 20th int. conf. adv. robotics (icar) (pp. 499–504).
Rath, N., Mahapatro, P., Nath, P., and Dash, R. (2019). Autonomous chess playing robot. In 2019 28th ieee int. conf. robot human interac. comm. (ro-man) (pp. 1–6).
Schadenberg. (2019). Predictability in human-robot interactions for autistic children. In 2019 14th acm/ieee int. conf. human-robot interac. (hri) (pp. 748–750).
Sun, C., Wang, T., Zheng, H., and Liu, H. (2023). An improved object detection method based on nao robot. In 2023 ieee 3rd int. conf. power elec. comp. app. (icpeca) (pp. 1184–1188).
Tres. (2014). Estudio e implementación de algoritmos de resolución del juego del dominó para un robot antropomórfico (Unpublished master’s thesis). Universidad Politécnica de Cataluña, Spain.
Wang, X., Xue, B., and Chen, B. (2020). Matsuoka’s cpg with desired rhythmic signals for adaptive walking of humanoid robots. IEEE Trans. Cyber., 50, 613–626.
Wei. (2020). A comprehensive approach to the generation of human-like arm movements on robot nao. IEEE Access, 8, 172869–172881.
Yan, S., Li, C., Liu, M., Liu, M., and Chen, Q. (2022). Roboseg: Real-time semantic segmentation on computationally constrained robots. IEEE Trans. Syst. Man Cyber. Syst., 52, 1567–1577.
Yang, M., Shyu, H., Yu, S., Sun, N., Yin, W., and Chen,
W. (2019). Integrating image and textual information in human–robot interactions for children with autism spectrum disorder. IEEE Trans. Multimedia, 21, 746–759.
Zhai, S., Wen, J., Zhu, J., and Guo, G. (2017). Trajectory planning of nao robot arm based on target recognition. In 2017 int. conf. adv. mechatronic syst. (icamechs) (pp. 403–407).
Zhu, H., Yi, R., Chellali, and Feng, L. (2018). Object recognition and localization algorithm based on nao robot. In 2018 27th ieee int. symp. robot human interac. comm. (roman) (pp. 483–486).
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Copyright (c) 2024 Álex Medina, Daniela Charris, Mauricio Pardo, Christian G. Quintero M.
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