Journal of Computer Science and Technology

, Volume 10, Issue 4, pp 375–379 | Cite as

Situated learning of a behavior-based mobile robot path planner

  • Yao Shu 
  • Zhang Bo 
Brief Papers


In this paper, we propose a behavior-based path planner that can self-learn in an unknown environment. A situated learning algorithm is designed which allows the robot to learn to coordinate several concurrent behaviors and improve its performance by interacting with the environment. Behaviors are implemented using CMAC neural networks. A simulation environment is set up and some simulation experiments are carried out to rest our learning algorithm.


Situated learning behavior-based path planning CMAC neural networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Brooks R A: A robust layered control system for a mobile robot.IEEE Journal on Robotics and Automation, 1986, RA-1: 14–23.MathSciNetGoogle Scholar
  2. [2]
    Albus J S. A new approach to manipulator control: The cerebellar model articulation controller (CMAC).Trans. ASME, J. Dynamic Syst. Meas. Contr., 1975, 97: 220–227.MATHGoogle Scholar
  3. [3]
    Verschure P Fet al. Distributed adaptive control: The self-organization of structured behavior.Robotics and Autonomous Systems, 1992, 9: 181–196.CrossRefGoogle Scholar
  4. [4]
    Mahadevan S, Connell J. Automatic programming of behavior-based robots using reinforment learning.Artificial Intelligent, 1992, 55: 311–365.CrossRefGoogle Scholar

Copyright information

© Science Press, Beijing China and Allerton Press Inc. 1995

Authors and Affiliations

  • Yao Shu 
    • 1
  • Zhang Bo 
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijing

Personalised recommendations