Advertisement

MANFIS Approach for Path Planning and Obstacle Avoidance for Mobile Robot Navigation

  • Prases Kumar Mohanty
  • Krishna K. Pandey
  • Dayal R. Parhi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 248)

Abstract

Path planning and obstacle avoidance are very crucial issues for an Autonomous mobile robot. In this research paper an intelligent hybrid approach MANFIS (Multiple Adaptive Neuro-Fuzzy Inference system) has been implemented for mobile robot navigation. The adaptive neuro-fuzzy inference system (ANFIS) has taken the advantages of expert knowledge of fuzzy inference system and learning capability of artificial neural network. The inputs to the MANFIS controller include the front obstacle distance, the left obstacle distance, the right obstacle distance and the target angle and outputs from the controller are left wheel velocity and right wheel velocity of the mobile robot. In order to validate the proposed hybrid technique a series of simulation experiments using MATLAB were performed and it was found that the proposed navigational controller is capable to avoid obstacle and reach the destination successfully. The experimental results also have been compared with simulation results to prove the authenticity of the developed navigational controller MANFIS.

Keywords

Neuro-Fuzzy Obstacle avoidance Mobile robot Navigation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Latombe, J.C.: Robot Motion Planning. Kluwer Academic Publishers, New York (1990)Google Scholar
  2. 2.
    Canny, J.E.: The Complexity of Robot Motion Planning. MIT Press, Cambridge (1988)Google Scholar
  3. 3.
    Lozano-Perez, T.: A simple motion planning algorithm for general robot manipulators. IEEE Journal of Robotics and Automation 3, 224–238 (1987)CrossRefGoogle Scholar
  4. 4.
    Leven, D., Sharir, M.: Planning a purely translational motion for a convex object in two dimensional space using generalized voronoi diagrams. Discrete & Computational Geometry 2, 9–31 (1987)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Payton, D., Rosenblatt, J., Keirsey, D.: Grid- based mapping for autonomous mobile robot. Robotics and Autonomous Systems 11, 13–21 (1993)CrossRefGoogle Scholar
  6. 6.
    Regli, L.: Robot Lab: Robot Path Planning. Lectures Notes of Department of computer Science. Drexel University (2007)Google Scholar
  7. 7.
    Khatib, O.: Real time Obstacle Avoidance for manipulators and Mobile Robots. IEEE Conference on Robotics and Automation 2, 505 (1985)Google Scholar
  8. 8.
    Huq, R., Mann, G.K.I., Gosine, R.G.: Mobile robot navigation using motor schema and fuzzy content behavior modulation. Application of Soft Computing 8, 422–436 (2008)CrossRefGoogle Scholar
  9. 9.
    Selekwa, M.F., Dunlap, D.D., Shi, D., Collins Jr., E.G.: Robot navigation in very cluttered environment by preference based fuzzy behaviors. Autonomous System 56, 231–246 (2007)CrossRefGoogle Scholar
  10. 10.
    Abdessemed, F., Benmahammed, K., Monacelli, E.: A fuzzy based reactive controller for a non-holonomic mobile robot. Robotics Autonomous System 47, 31–46 (2004)CrossRefGoogle Scholar
  11. 11.
    Pradhan, S.K., Parhi, D.R., Panda, A.K.: Fuzzy logic techniques for navigation of several mobile robots. Application of Soft Computing 9, 290–304 (2009)CrossRefGoogle Scholar
  12. 12.
    Velagic, J., Osmic, N., Lacevic, B.: Neural Network Controller for Mobile Robot Motion Control. World Academy of Science, Engineering and Technology 47, 193–198 (2008)Google Scholar
  13. 13.
    Singh, M.K., Parhi, D.R.: Intelligent Neuro-Controller for Navigation of Mobile Robot. In: Proceedings of the International Conference on Advances in Computing, Communication and Control, Mumbai, Maharashtra, India, pp. 123–128 (2009)Google Scholar
  14. 14.
    Castro, V., Neira, J.P., Rueda, C.L., Villamizar, J.C., Angel, L.: Autonomous Navigation Strategies for Mobile Robots using a Probabilistic Neural Network (PNN). In: 33rd Annual Conference of the IEEE Industrial Electronics Society, Taipei, Taiwan, pp. 2795–2800 (2007)Google Scholar
  15. 15.
    Jang, J.S.R.: ANFIS: Adaptive network-based fuzzy inference system. IEEE Transaction on System, Man and Cybernetics –Part B 23, 665–685 (1993)CrossRefGoogle Scholar
  16. 16.
    Nefti, S., Oussalah, M., Djouani, K., Pontnau, J.: Intelligent Adaptive Mobile Robot Navigation. Journal of Intelligent and Robotic Systems 30, 311–329 (2001)CrossRefMATHGoogle Scholar
  17. 17.
    Sudhakar, K., Noorul, A., Selvaraj, T.: Neuro-Fuzzy Based navigation for Truck like Mobile Robot. International Journal of Soft Computing 5, 633–637 (2007)Google Scholar
  18. 18.
    Demirli, K., Khoshnejad, M.: Autonomous parallel parking of a car-like mobile robot by a neuro-fuzzy sensor-based controller. Fuzzy Sets and Systems 160, 2876–2891 (2009)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Kim, C.N., Trivedi, M.M.: A Neuro-Fuzzy Controller for Mobile Robot Navigation and Multirobot Convoying. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics 28, 829–840 (1998)CrossRefGoogle Scholar
  20. 20.
    Pradhan, S.K., Parhi, D.R., Panda, A.K.: Neuro-fuzzy technique for navigation of multiple mobile robots. Fuzzy Optimum Decision Making 5, 255–288 (2006)CrossRefMATHGoogle Scholar
  21. 21.
    Marichal, G.N., Acosta, L., Moreno, L., Mendez, J.A., Rodrigo, J.J., Sigut, M.: Obstacle avoidance for a mobile robot: A neuro-fuzzy approach. Fuzzy Sets and Systems 124, 171–179 (2001)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Mohanty, P.K., Parhi, D.R.: Path Planning Strategy for Mobile Robot Navigation using MANFIS Controller. In: 2013 International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), Bhubaneswar (accepted 2013)Google Scholar
  23. 23.
    The Math Works Company, Natick, MA, ANFIS Toolbox User’s Guide of MATLABGoogle Scholar
  24. 24.
    Parhi, D.R.: Navigation of multiple mobile robots in an unknown environment. Doctoral Thesis, Cardiff School of Engineering. University of Wales, UK (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Prases Kumar Mohanty
    • 1
  • Krishna K. Pandey
    • 1
  • Dayal R. Parhi
    • 1
  1. 1.Robotics Laboratory, Department of Mechanical EngineeringNational Institute of TechnologyRourkelaIndia

Personalised recommendations