JACIII Vol.17 No.5 pp. 753-760
doi: 10.20965/jaciii.2013.p0753


Recognition of Indoor Environment by Robot Partner Using Conversation

Jinseok Woo and Naoyuki Kubota

Graduate School of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

March 20, 2013
July 4, 2013
September 20, 2013
simultaneous localization and mapping, robot partner, neural network, genetic algorithm
To support daily life before performing an action, a robot partner must perceive an unknown environment. Much research has been done from various viewpoints on self-localization estimation and environment perception. In our research, the robot partner performs self-localization and environment recognition using Simultaneous Localization and Mapping for self-localization estimation and map building. In this paper, we propose a method for recognizing indoor environments by robot partners based on conversations with human beings. Information acquired from maps is identified in order to share the meaning with human beings after the required interpretation. In this paper, we therefore propose a method for recognizing environmental maps by labeling these maps based on symbolic information developed through conversation with human beings. The proposed method is composed of four parts. First, the robot partner applies a steady-state genetic algorithm for self-localization estimation. Second, we use a map building algorithm for expressing the topological map. Third, conversation with human beings is performed for acquiring symbolic information in order to recognize object and position locations through the map. Fourth, we perform experiments and discuss the effectiveness of the proposed technique.
Cite this article as:
J. Woo and N. Kubota, “Recognition of Indoor Environment by Robot Partner Using Conversation,” J. Adv. Comput. Intell. Intell. Inform., Vol.17 No.5, pp. 753-760, 2013.
Data files:
  1. [1] Statistics Bureau, Ministry of Internal Affairs and Communications, “population estimates,” September 2012.
  2. [2] Tokyo Fire Department, Disaster Division, Community Safety and Disaster Education, “Conflagration and daily life accident data based on elderly people current situation,” p. 27, 2010.
  3. [3] T. Yoneda, A. Ogawa, J. Sasaki, K. Yonemoto, and Y. Funyu, “Development and Operation of Mimamori Network System for Elders in Kawai Village, Iwate Prefecture,” Personal Computer Users’ Application Technology Association, Vol.16, No.3, 2006.
  4. [4] D. Sperber and D. Wilson, “Relevance: Communication and Cognition,” Blackwell Publishing Ltd., pp. 38-46, 1995.
  5. [5] S. Thrun, W. Burgard, and D. Fox, “Probabilistic Robotics,” MIT Press, Cambridge, 2005.
  6. [6] H. Sasaki, N. Kubota, and K. Taniguchi, “Topological Map and Cell Space Map for SLAM of A Mobile Robot,” GESTS Int. Trans. on Computer and Engineering, Vol.45, No.3, 2008.
  7. [7] M. Tomono and S. Yuta, “Object-based Localization and Mapping using Loop Constraints and Geometric Prior Knowledge,” Proc. of Int. Conf. on Robotics and Automation, pp. 862-867, 2003.
  8. [8] S. Thrun, “Robotic mapping: A survey” In: G. Lakemeyer and B. Neberl (Eds.), Exploring Artificial Intelligence in the New Millenium, Morgan Kaufmann, San Francisco, 2002.
  9. [9] C. Wang and C Thorpe, “Simultaneous localization and mapping with detection and tracking of moving objects,” IEEE Int. Conf. on Robotics and Automation, pp. 2918-2924, 2002.
  10. [10] K. Singh and K. Fujimura, “Map Making by Cooperative Mobile Robots,” Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 254-259, 1993.
  11. [11] M. Tomono and S. Yuta, “Mobile Robot Localization based on an Inaccurate Map,” Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 399-405, 2001.
  12. [12] S. Thrun, D. Fox,W. Burgard, and F. Dellaert, “RobustMonte Carlo localization for mobile robots,” pp. 99-141, Artificial Intelligence, Vol.128, 2001.
  13. [13] J. Peltason, F. Siepmann, T. P. Spexard, B. Wrede, M. Hanheide, and E. A. Topp, “Mixed-initiative in human augmented mapping,” IEEE Int. Conf. on Robotics and Automation 2009 (ICRA ’09), pp. 2146-2153, May 2009.
  14. [14] A. Diosi, G. Taylor, and L. Kleeman, “Interactive slam using laser and advanced sonar,” Proc. of the 2005 IEEE Int. Conf. on Robotics and Automation 2005 (ICRA 2005), pp. 1103-1108, April 2005.
  15. [15] R. Pfeifer and C. Scheier, “Understanding Intelligence, 2001 ed,” MIT Press, pp. 69-71, 2001.
  16. [16] H. Harasima, T. Yamaguchi, N. Kubota, and Y. Takama, “Intelligent network system introduction,” Corona, pp. 4-9, pp. 32-36, pp. 62-71, 2008.
  17. [17] DTalker for Mac OSX Ver3.0 [Online].Available: [Accessed March 15, 2013]
  18. [18] Laser Range Finder sensor information of HOKUYO AUTOMATIC CO., LTD. [Online].Available: [Accessed March 15, 2013]
  19. [19] T. Kohonen, “Self-Organizing Maps,” 3rd ed., Springer, Heidelberg, 2001.
  20. [20] B. Fritzke, “Growing Cell Structures – A Self-Organising Network for Unsupervised and Supervised Learning,” Neural Networks, Vol.7, No.9, pp. 1441-1460, 1994.
  21. [21] V. J. Hodge and J. Austin, “Hierarchical Growing Cell Structures: TreeGCS,” IEEE Trans. Knowledge and Data Engineering, Vol.13, No.2, pp. 207-218, 2001.
  22. [22] T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction,” Springer, New York, 2001.
  23. [23] B. Fritzke, “A growing neural gas network learns topologies,” G. Tesauro, D. S. Touretzky, and T. K. Leen (Eds.), Advances in Neural Information Processing Systems, Vol.7, pp. 625-632, MIT Press, Cambridge, 1995.
  24. [24] T. Fukuda, N. Kubota, and T. Arakawa, “GA Algorithms in Intelligent Robots,” Fuzzy Evolutionary Computation, Kluwer Academic Publishers, Dordrecht, pp. 81-105, 1997.
  25. [25] N. Kubota, H. Neya, and K. Taniguchi, “Sensory Network and Evolutionary Programming for a Mobile Robot,” Proc. of the 4th Asia-Pacific Conf. on Simulated Evolution And Learning (SEAL’02), CD-ROM, pp. 119-123, 2002.
  26. [26] D. E. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning,” Addison Wesley, Reading, 1989.
  27. [27] D. Tang, J. Botzheim, N. Kubota, and T. Yamaguchi, “Estimation of Human Transport Modes by Fuzzy Spiking Neural Network and Evolution Strategy in Informationally Structured Space,” IEEE Int. Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS), No.SS-1003, Singapore, April 16-19, 2013.
  28. [28] N. Kubota, Y. Nojima, F. Kojima, and T. Fukuda, “Multi-Objective Behavior Coordinate for A mobile Robot with Fuzzy Neural Networks,” Proc. (CD-ROM) of IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks, 2000.
  29. [29] D. B. Fogel, “Evolutionary Computation,” IEEE Press, Los Alamitos, 1995.
  30. [30] G. Syswerda, “A Study of Reproduction in Generational and Steady-State Genetic Algorithms,” Foundations of Genetic Algorithms, Morgan Kaufmann Publishers, San Mateo CA, pp. 94-101, 1991.
  31. [31] N. Kubota, K. Yuki, and N. Baba, “Integration of Intelligent Technologies for Simultaneous Localization and Mapping,” In: The Society of Instrument and Control Engineers (SICE), 2009.
  32. [32] N. Kubota and A. Yorita, “Topological environment reconstruction in informationally structured space for pocket robot partners,” in Proc. 2009 IEEE Int. Symp. Computational Intelligence in Robotics and Automation, Daejeon, Korea, pp. 165-170, 2009.
  33. [33] J. Woo, J. Shimazaki, and N. Kubota, “Conversation system for robot partner based on human states,” FAN Symposium, Okinawa, August 30-31, 2012.

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