Skip to main content
SpringerLink
Log in

Adaptive behaviors of reactive mobile robot with Bayesian inference in nonstationary environments

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

This paper presents a technique for a reactive mobile robot to adaptively behave in unforeseen and dynamic circumstances. A robot in nonstationary environments needs to infer how to adaptively behave to the changing environment. Behavior-based approach manages the interactions between the robot and its environment for generating behaviors, but in spite of its strengths of fast response, it has not been applied much to more complex problems for high-level behaviors. For that reason many researchers employ a behavior-based deliberative architecture. This paper proposes a 2-layer control architecture for generating adaptive behaviors to perceive and avoid moving obstacles as well as stationary obstacles. The first layer is to generate reflexive and autonomous behaviors with behavior network, and the second layer is to infer dynamic situations of the mobile robot with Bayesian network. These two levels facilitate a tight integration between high-level inference and low-level behaviors. Experimental results with various simulations and a real robot have shown that the robot reaches the goal points while avoiding stationary or moving obstacles with the proposed architecture.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Mataric MJ (1994) Interaction and intelligent behavior. PhD thesis, MIT

  2. Tyrrell T (1994) An evaluation of Maes’s bottom-up mechanism for behavior selection. Adapt Behav 2(4):307–348

    Article  Google Scholar 

  3. Kim K-J, Cho S-B (2006) A unified architecture for agent behaviors with selection of evolved neural network modules. Appl Intell 25(3):253–268

    Article  Google Scholar 

  4. Hu H, Brady M, Probert P (1993) A decision theoretic approach to real-time obstacle avoidance for a mobile robot. Intell Robots Syst 1457–1464

  5. Gomi T, Volpe P (1993) Collision avoidance using behavioral-based AI techniques. In: Proceedings of intelligent vehicles ’93 symposium, pp 141–145

  6. Arkin RC (1998) Behavior-based robotics. MIT Press, Cambridge

    Google Scholar 

  7. Smart WD (2002) Making reinforcement learning work on real robots. PhD thesis, Brown Univ

  8. Hashimoto S, Kojima F, Kubota N (2003) Perceptual system for a mobile robot under a dynamic environment. In: Proceedings 2003 IEEE international symposium on computational intelligence in robotics and automation, pp 747–752

  9. Inamura T, Inaba M, Inoue H (2000) User adaptation of human-robot interaction model based on Bayesian network and introspection of interaction experience. In: Proceedings of the 2000 IEEE/RSJ international conference on intelligent robots and systems, pp 2139–2144

  10. Nicolescu MN, Mataric MJ (2002) A hierarchical architecture for behavior-based robots. In: Proceedings of autonomous agents and multi-agent systems, pp 227–233

  11. Beetz M, Arbuckle T, Belker T, Cremers AB, Schulz D, Bennewitz M, Burgard W, Hahnel D, Fox D, Grosskreutz H (2001) Integrated, plan-based control of autonomous robot in human environments. IEEE Intell Syst 16(5):56–65

    Google Scholar 

  12. Bennett AA (2000) A behavior-based approach to adaptive feature detection and following with autonomous underwater vehicles. IEEE Ocean Eng 25(2):213–226

    Article  Google Scholar 

  13. Sukhatme GS, Mataric MJ (2000) Embedding robots into the internet. Commun ACM 43(5):67–73

    Article  Google Scholar 

  14. Nicolescu MN, Mataric MJ (2000) Extending behavior-based systems capabilities using an abstract behavior representation. In: Proceedings of AAAI fall symposium on parallel cognition, pp 27–34

  15. Khoo A, Zubek R (2002) Applying inexpensive AI techniques to computer games. IEEE Intell Syst 17(4):48–53

    Article  Google Scholar 

  16. Weigel T, Gutmann J-S, Dietl M, Kleiner A, Nebel B, Freiburg CS (2002) Coordinating robots for successful soccer playing. IEEE Trans Robot Autom 19(5):685–699

    Article  Google Scholar 

  17. Matsuura M, Wada M (2000) Formative behavior network for a biped robot: A control system in consideration of motor development. Robot Hum Interact Commun 101–106

  18. Mucientes M, Iglesias R, Regueiro CV, Bugarin A, Carinena P, Barro S (2001) Fuzzy temporal rules for mobile robot guidance in dynamic environments. IEEE Trans Syst Man Cybern 31(3):391–398

    Google Scholar 

  19. Fujimori A, Tani S (2002) A navigation of mobile robots with collision avoidance for moving obstacles. In: Proceedings of international conference on industrial technology, pp. 1–6

  20. Mbede JB, Ele P, Mveh-Abia C-M, Toure Y, Graefe V, Ma S (2005) Intelligent mobile manipulator navigation using adaptive neuro-fuzzy system. Inf Sci 171(4):447–474

    Article  MATH  Google Scholar 

  21. Sabourin C, Madani K (2008) Obstacle avoidance strategy for biped robot based on fuzzy Q-learning. In: Proceedings of international conference on climbing and walking robots and the support technologies for mobile machines, pp 695–702

  22. Lane T, Kaelbling LP (2001) Toward hierarchical decomposition for planning in uncertain environments. In: Proceedings of the 2001 IJCAI workshop on planning under uncertainty and incomplete information, pp 1–7

  23. Sabourin C, Bruneau O (2005) Robustness of the dynamic walk of a biped robot subjected to disturbing external forces by using CMAC neural networks. Robot Auton Syst 51(2–3):81–99

    Google Scholar 

  24. Carreras M, Yuh J, Batlle J, Ridao P (2007) Application of SONQL for real-time learning of robot behaviors. Robot Auton Syst 55(8):628–642

    Article  Google Scholar 

  25. Sabourin C, Madani K, Bruneau O (2007) Autonomous biped gait pattern based on fuzzy-CMAC neural networks. Integr Comput-Aided Eng 14(2):173–186

    Google Scholar 

  26. Basye K, Dean T, Kirman J, Lejter M (1992) A decision-theoretic approach to planning, perception, and control. IEEE Expert 7(4):58–65

    Article  Google Scholar 

  27. Akiba T, Tanaka H (1994) A Bayesian approach for user modeling in dialogue systems. Technical Report of Tokyo Institute of Technology

  28. Neapolitan RE (2003) Learning Bayesian network. Prentice Hall series in artificial intelligence

  29. Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kauffman, San Mateo

    Google Scholar 

  30. Haider S, Levis AH (2008) Modeling time-varying uncertain situations using dynamic influence net. Int J Approx Reas 42(2):488–502

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Yonsei University, 262 Seongsanro, Sudaemoon-gu, Seoul, 120-749, South Korea

    Hyeun-Jeong Min & Sung-Bae Cho

Authors
  1. Hyeun-Jeong Min
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sung-Bae Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sung-Bae Cho.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Min, HJ., Cho, SB. Adaptive behaviors of reactive mobile robot with Bayesian inference in nonstationary environments. Appl Intell 33, 264–277 (2010). https://doi.org/10.1007/s10489-009-0164-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-009-0164-0

Keywords

Search

Navigation