The Emotional Robot Model Based on Endocrine System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 180)

Abstract

Inspired by the regulation mechanism of endocrine system, we attempts to build an emotional robot model with the purpose to achieve the self-adaptation of robot behaviour in a complex and changeable environment. The model designs an internal environment and emotion- & motive-generating models based upon the endocrine regulation mechanism. The robot relies upon the dynamic stability of the internal environment to accomplish its self- organization to the external environment. Emotion-generating module is applied to in vivo and in vitro contingencies of various kinds; by releasing hormones, the emotion generated influences the robot’s in vivo and in vitro perception, and then its behavioural choice. The motive-generating model is employed to maintain the relative stability of the internal environment, and the motive generated leads to the corresponding behavioural choice directly. In the end, the paper conducts detailedly the simulation experiment of robot planning, whose results prove the effectiveness of this emotional robot model.

Keywords

endocrine system emotion motive robot control model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Shen, W.M., Salemi, B., Will, P.: Hormone-inspired adaptive communication and distributed control for CONRO Self-reconfigurable robots. IEEE Transaction on Robotics and Automation 18(5), 700–712 (2002)CrossRefGoogle Scholar
  2. 2.
    Ma, C., Yue, X.: Intelligent Model of Urban Road Tunnel Ventilation System Based on Multi-Level Neural Network. In: Pacific-Asia Conference on Circuits, Communications and Systems, pp. 636–639 (2009)Google Scholar
  3. 3.
    Yang, Y., Yu, X.: Cooperative Coevolutionary Genetic Algorithm for Digital IIR Filter Design. IEEE Trans. on Industrial Electronics 54, 1311–1318 (2007)CrossRefGoogle Scholar
  4. 4.
    Dasgupta, D.: Advances in artificial immune systems. IEEE Trans. on Computational Intelligence Magazine 1, 40–49 (2006)Google Scholar
  5. 5.
    Timmis, J., Neal, M., Thorniley, J.: An Adaptive Neuro-Endocrine System for Robotic Systems. In: IEEE Workshop on Robotic Intelligence in Informationally Structured Space, pp. 129–136 (2009)Google Scholar
  6. 6.
    Mendao, M.: A Neuro-Endocrine Control Architecture Applied to Mobile Robotics. PhD Thesis, University of Kent, Canterbury, UK (2007)Google Scholar
  7. 7.
    Ganapathy, V., Yun, S.C., Lui, W.L.D.: Utilization of Webots and Khepera II as a platform for Neural Q-Learning controllers. In: IEEE Symposium on Industrial Electronics & Applications, pp. 783–788 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.New Star Research Institute of Applied TechnologyHefeiChina

Personalised recommendations