, Volume 2, Issue 2, pp 100–108 | Cite as

A method for path planning strategy and navigation of service robot

  • Widodo BudihartoEmail author
  • Ari Santoso
  • Djoko Purwanto
  • Achmad Jazidie
Research Article


This paper presents our work on the development of Path Planning Strategy and Navigation by using ANFIS(Adaptive Neuro-Fuzzy Inference System)controller for a vision-based service robot. The robot will deliver a cup to a recognized customer and a black line as the guiding track for navigating a robot with a single camera. The contribution of this research includes a proposed architecture of ANFIS controller for vision-based service robot integrated with improved face recognition system using PCA, and the algorithm for moving obstacle avoidance. We also propose a path planning algorithm based on Dijkstra’s algorithm to obtain the shortest path for robot to move from the starting point to the destination. In order to avoid moving obstacles, we have proposed an algorithm using binaural ultrasonic sensors. The service robot called Srikandi is also equipped with 4 DOF arm and a framework of face recognition system. The proposed path planning strategy and navigation was tested empirically and proved effective.


service robot navigation ANFIS path planning face recognition 


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  1. [1]
    T. Tsubouchi and M. Rude, Motion planning for mobile robots in a time-variying environment: A Survey, Journal of Robotics and Mechatronics, vol. 8 no.1(1996), 15–24.Google Scholar
  2. [2]
    Y.Z. Chang and R. P. Huang, A simple Fuzzy motion planning strategy for autonomous mobile robots, 33rd Annual Conference of the IEEE Industrial Electronics Society, Taiwan, 2008, 477–480.Google Scholar
  3. [3]
    D. Ito (ed), Robot Vision, Nova Science Publisher, 2009.Google Scholar
  4. [4]
    W. Budiharto, D. Purwanto and A. Jazidie, Indoor Navigation using ANFIS controller for Servant Robot, 2nd IEEE International Conference on Computer Engineering and Its Application (ICCE 2010), Bali, 2010, 582.586. DOI: 10.1109/ICCEA.2010.119.Google Scholar
  5. [5]
    H. Kang, B. Lee and K. Kim, Path Planning Algorithm Using the Particle Swarm Optimization and the Improved Dijkstra Algorithm, Workshop on Computational Intelligence and Industrial Application, vol 2 (2008), 1002–1005.CrossRefGoogle Scholar
  6. [6]
    H. Uozumin and Y. Shirai, Mobile Robot Motion Planning considering the Motion Uncertainty of Moving Obstacles, IEEE Int. Conf. on System, Man, and Cybernetics, 1999, 692–698.Google Scholar
  7. [7]
    J. Kim Pearce and R. Amato, Extracting optimal paths from roadmaps for motion planning, procedding IEEE International Conference on Robotics and Automation, 2003.Google Scholar
  8. [8]
    H. Li and SX. Yang, A behaviour-based mobile robot with a visual landmark recognition system, IEEE transactions on Mechatronics, vol 8 (2003), 390–400.CrossRefGoogle Scholar
  9. [9]
    D. Rodgriguez and L. Pedraza, “Latest developments in feature based mapping and Navigation for indoor service robots”, Nova Science Publishers, 2009, 123–150.Google Scholar
  10. [10]
    A. Zhu and S. Yang, “An Adaptive Neuro Fuzzy Controller for Robot Navigation, “Book chapter in Recent Advances in Intelligent Control Systems, Springer, 2009, 277–281.Google Scholar
  11. [11]
  12. [12]
    J.F Canny, The complexity of robot motion planning, MIT Press, Cambridge, MA, 1988.Google Scholar
  13. [13]
    T. Simeon and J.P Laumond, Manipulation planning with Probabilistic Roadmaps, International Journal in Robotics Research, vol. 23 (2004), 729–746.CrossRefGoogle Scholar
  14. [14]
    E.W. Dijkstra, A note on two problems in connection with graphs, Numerical Math. I (1959), 269–271.Google Scholar
  15. [15]
    Briggs et al, Expected Shortest Paths for Landmark-Based Robot Navigation, The International Journal of Robotics Research, vol. 23(2004), 717–728.CrossRefGoogle Scholar
  16. [16]
    J.-S. R. Jang, ANFIS: Adaptive-network-based fuzzy inference systems, IEEE Trans. Syst., Man, Cybernetics, vol. 23(1993), 665–685.CrossRefGoogle Scholar
  17. [17]
    J. -S. R. Jang, C.T. Sun, E. Mizutani, Neuro Fuzzy and Soft Computing, Prentice Hall Publisher, 1997.Google Scholar
  18. [18]
    R. Petru and M. Emil, Behaviour-based neuro-fuzzy controller for mobile robot navigation, IEEE Transaction on Instrumentation and measurement, vol 52, no. 4(2003).Google Scholar
  19. [19]
    D. Nauck and R. Kruse, Designing neuro-fuzzy systems through backpropagation, In Fuzy Modelling:Paradigms and Practice, Kluwer, Boston, 1996), 203–228.Google Scholar
  20. [20]
    J.J. Kuffner, K. Nishiwaki, S. Kagami, M. Inaba and H. Inoue, Motion planning for humanoid robots, Proc. 20th Int’l Symp. Robotics Research (ISRR’03), 2003.Google Scholar
  21. [21]
    B. Stanley and P. McKerrow Measuring range and bearing with a binaural ultrasonic sensor, Proc. of the IEEE/RSJ International Conf. on Intelligent Robots and Systems,vol.2 (1997), 565–571.Google Scholar
  22. [22]

Copyright information

© © Versita Warsaw and Springer-Verlag Wien 2011

Authors and Affiliations

  • Widodo Budiharto
    • 2
    Email author
  • Ari Santoso
    • 1
  • Djoko Purwanto
    • 1
  • Achmad Jazidie
    • 1
  1. 1.Institute of Technology Sepuluh NopemberSurabayaIndonesia
  2. 2.BINUS UniversityJakartaIndonesia

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