Advertisement

Bio-inspired Navigation of Mobile Robots

  • Lei Wang
  • Simon X. Yang
  • Mohammad Biglarbegian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7326)

Abstract

This paper presents a bio-inspired neural network algorithm for mobile robot path planning in unknown environments. A novel learning algorithm combining Skinner’s operant conditioning and a shunting neural dynamics model is applied to the path planning. The proposed algorithm depends mainly on an angular velocity map that has two parts: one from the target, which drives the robot to move toward to target, and the other from obstacles that repels the robot for obstacle avoidance. An improved biological learning algorithm is proposed for mobile robot path planning. Simulation results show that the proposed algorithm not only allows the robot to navigate efficiently in cluttered environments, but also significantly improves the computational and training time. The proposed algorithm offers insights into the research and applications of biologically inspired neural networks.

Keywords

Path Planning Collision Avoidance Mobile Robot Robot learning Biological Inspiration Neural Network Neural Dynamics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Floreano, D., Mattiussi, C.: Bio-Inspired Artificial Intelligence Theories, Methods, and Technologies. MIT Press, Cambridge (2008)Google Scholar
  2. 2.
    Grossberg, S.: On the dynamics of operant conditioning. Theoretical Biology 33, 225–255 (1971)CrossRefGoogle Scholar
  3. 3.
    Yang, S.X., Lou, C.: A Neural Network Approach to Complete Coverage Path Planning. IEEE Trans. Systems 33, 718–724 (2004)Google Scholar
  4. 4.
    Chang, C., Gudiano, P.: Application of biological learning theories to mobile robot avoidance and approach behaviours. Complex Systems 1, 79–114 (1998)zbMATHCrossRefGoogle Scholar
  5. 5.
    Gutnisky, D.A., Zanutto, B.S.: Learning Obstacle Avoidance with an Operant Behavior Model. Artificial Life 10, 65–81 (2004)CrossRefGoogle Scholar
  6. 6.
    Aren, P., Fortuna, L., Patané, L.: Learning Anticipation via Spiking Networks: Application to Navigation Control. IEEE Trans. Neural Netw. 20(2), 202–216 (2009)CrossRefGoogle Scholar
  7. 7.
    Yang, S.X., Meng, M.: An efficient neural network approach to dynamic robot motion planning. IEEE Trans. Neural networks 13, 143–148 (2000)CrossRefGoogle Scholar
  8. 8.
    Saksida, D.S., Sariff, L.M.: Operant Conditioning in Skinnerbots. Adaptive Behavior 5, 1–28 (1997)Google Scholar
  9. 9.
    Gaudiano, P., Chang, C.: Adaptive obstacle avoidance with a neural network for operant conditioning: experiments with real robots. In: Computational Intelligence in Robotics and Automation, pp. 13–18 (1997)Google Scholar
  10. 10.
    Grossberg, S.: Nonlinear neural networks: principle, mechanisms, and architecture. Neural Networks 1, 17–61 (1988)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lei Wang
    • 1
  • Simon X. Yang
    • 1
  • Mohammad Biglarbegian
    • 1
  1. 1.School of EngineeringUniversity of GuelphGuelphCanada

Personalised recommendations