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Real-Time Navigation for a Mobile Robot Based on the Autonomous Behavior Agent

  • Lu Xu
  • Liguo Zhang
  • Yangzhou Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4707)

Abstract

Autonomous behavior agent includes the robot most behaviors which are designed using hierarchical control method to guarantee their real time performance for real time navigation in response to different situation perceived. The process of robot real time navigation based on the autonomous behavior agent mainly includes three behaviors. The sensing behavior translates the configuration space that the robot and obstacles exist in into 2D Cartesian Grid by Quadtree method. The path planning behavior designs the sub-goals given the global map, start and goal points by improved D* Lite Algorithm. And the obstacle avoidance behaivor replans the path between two adjacent sub-goals when the environment changes. It is able to replan faster than planning from scratch since it modifies its previous search results locally and enables robots adapt to the dynamic environment. The simulation results that are reported show that the mobile robot navigation method is efficient and feasible.

Keywords

mobile robot real-time navigation dynamic environment path planning autonomous agent 

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References

  1. 1.
    Brooks, R.A.: A Robust Layered Control System for A Mobile Robot. IEEE Journal of Robotics And Automation, RA-2(1) (March 1986)Google Scholar
  2. 2.
    Samer, A.M., Ahmad, A.M.: Constrained Motion Control Using Vector Potential Fields. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans 30(3) (2000)Google Scholar
  3. 3.
    Khatib, 0.: A Unified Approach for Motion and Force Control of Robot Manipulators: The Operational Space Formulation. IEEE Journal of Robotics and Automation RA. 3(1) (February 1987)Google Scholar
  4. 4.
    Choset, H., Walker, S.: Sensor-Based Exploration: Incremental Construction of the Hierarchical Generalized Voronoi Graph. The International Journal of Robotics Research 19, 126–148 (2000)CrossRefGoogle Scholar
  5. 5.
    Latombe, J-C., Barraquand, J.: Robot Motion Planning: A Distributed Presentation Approach. International Journal of Robotics Research 10, 628–649 (1991)CrossRefGoogle Scholar
  6. 6.
    Jacobs, R., Canny, J.: Planning Smooth Paths for Mobile Robots. In: Proc. IEEE Conference on Robotics and Automation, pp. 2–7. IEEE Press, Los Alamitos (1989)Google Scholar
  7. 7.
    Russell, S., Norvig, P.: Artificial Intelligence, a Modern Approach. Prentice-Hall, Englewood Cliffs (1995)zbMATHGoogle Scholar
  8. 8.
    Stentz, A.: Optimal and efficient path planning for partially-known environments. In: Proc. Int. Conf. Robot. Autom., pp. 3310–3317 (1994)Google Scholar
  9. 9.
    Stentz, A.: The Focussed D* Algorithm for Real Time Replanning. In: Proc. Int. Joint Conf. Artificial Intelligence, pp. 1652–1659 (1995)Google Scholar
  10. 10.
    Pearl, J.: Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, Reading, MA (1985)Google Scholar
  11. 11.
    Hebert, M., et al.: Experiments with Driving Modes for Urban Robots. In: Proc. SPIE Mobile Robots, pp. 140–149 (1999)Google Scholar
  12. 12.
    Likhachev, M.: Anytime Dynamic A*: An Anytime, Replanning Algorithm. In: Proc. Int. Conf. Automated Planning and Scheduling (2005)Google Scholar
  13. 13.
    Koening, S., Likhachev, M.: Incremental A*, Advances in Neural Information Processing Systems 14. MIT Press, Cambridge, MA (2002)Google Scholar
  14. 14.
    Likhachev, M.: Search-based Planning for Large Dynamic Environments. PhD thesis, CMU (2005)Google Scholar
  15. 15.
    Koenig, S.: Fast Replanning for Navigation in Unknown Terrain. IEEE Transactions on Robotics 21(3) (June 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Lu Xu
    • 1
  • Liguo Zhang
    • 1
  • Yangzhou Chen
    • 1
  1. 1.School of Electronic Information & Control Engineering, Beijing University of Technology, Beijing, 100022, Email: flysand4@gmail.comChina

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