Office Delivery Robot Controlled by Modular Behavior Selection Networks with Planning Capability

  • Young-Seol Lee
  • Sung-Bae Cho
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 283)


Recently, assistance service using mobile robots becomes one of important issues. Accordingly, studies on controlling the mobile robots are spreading all over the world. In this line of research, we propose a hybrid architecture based on hierarchical planning of modular behavior selection networks for generating autonomous behaviors of the office delivery robot. Behavior selection network is suitable for goal-oriented problems, but it is too difficult to design a monolithic behavior network to deal with complex robot control. We decompose it into several smaller behavior modules and construct sequences of the modules considering the sub-goals, the priority in each task and the user feedback. The feasibility of the proposed method is verified on both the Webot simulator and Khepera II robot in an office environment with delivery tasks. Experimental results confirm that a robot can achieve goals and generate module sequences successfully even in unpredictable and changeable situations, and the proposed planning method reduces the elapsed time during tasks by 17.5 %.


Office delivery robot Hybrid robot control Behavior networks 



This research was supported by the Original Technology Research Program for Brain Science through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0018948).


  1. 1.
    Park, H.-S., Cho, S.-B.: A modular design of bayesian networks using expert knowledge: context-aware home service robot. Expert Syst. Appl. 39(3), 2629–2642 (2012)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Garcia, E., Jimenez, M.A., Santos, P.G., Armada, M.: The evolution of robotics research. IEEE Robot Autom. Mag. 14(1), 90–103 (2007)CrossRefGoogle Scholar
  3. 3.
    Beetz, M., Arbuckle, T., Belker, T., Cremers, A.B., Schulz, D., Bennewitz, M., Burgard, W., Hahnel, D., Fox, D., Grosskreutz, H.: Integrated, plan-based control of autonomous robots in human environments. IEEE Intell. Syst. 15(5), 56–65 (2001)Google Scholar
  4. 4.
    Chung, S.H., Williams, B.C.: A Decomposed symbolic approach to reactive planning. Master’s Thesis, MIT (2003)Google Scholar
  5. 5.
    Milford, M., Wyeth, G.: Hybrid robot control and SLAM for persistent navigation and mapping. Rob. Auton. Syst. 58(9), 1096–1104 (2010)CrossRefGoogle Scholar
  6. 6.
    Ramachandran, D., Gupta, R.: Smoothed sarSa: reinforcement learning for robot delivery tasks. In: IEEE International Conference on Robotics and Automation, pp. 2125–2132. IEEE Press, New York (2009)Google Scholar
  7. 7.
    Mataric, M.J.: Using communication to reduce locality in distributed multi-agent learning. J. Exp. Theor. Artif. Intell. 10(3), 357–369 (1998)Google Scholar
  8. 8.
    Nicolescu M.N., Mataric, M.J.: A hierarchical architecture for behavior-based robots. In: First International Joint Conference on Autonomous Agents and Multi-Agent Systems, pp. 227–233. ACM, New York (2002)Google Scholar
  9. 9.
    Weigel, T., Gutmann, J.-S., Dietl, M., Kleiner, A., Nebel, B.: CS freiburg: coordinating robots for successful soccer playing. IEEE Trans. Robot. Autom. 19(5), 685–699 (2002)CrossRefGoogle Scholar
  10. 10.
    Yoon, J.-W., Cho, S.-B.: A mobile intelligent synthetic character with natural behavior generation. In: 2nd International Conference on Agents and, Artificial Intelligence, ICAART 2010, pp. 315–318. Valencia, (2010)Google Scholar
  11. 11.
    Lim, S.-S., Yoon, J.-W., Oh, K.-H. Cho, S.-B.: Gesture based dialogue management using behavior network for flexibility of human robot interaction. In: IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 592–597. IEEE Press, New York (2009)Google Scholar
  12. 12.
    Yoon, J.-W., Cho, S.-B.: Hierarchical planning of modular behaviour networks for office delivery robot. In: 9th International Conference on Informatics in Control, Automation and, Robotics, pp. 14–20. Rome, (2012).Google Scholar
  13. 13.
    Decuqis, V., Ferber, J.: An extension of maes’ action selection mechanism for animats. In: 5th International Conference on Simulation of Adaptive Behavior on From animals to animats 5, pp. 153–158. MIT Press, Cambridge, (1998)Google Scholar
  14. 14.
    Tyrell, T.: Computational mechanisms for action selection. PhD Thesis, University of Edinburgh (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceYonsei UniversitySeodaemun-guKorea

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