MANFIS Approach for Path Planning and Obstacle Avoidance for Mobile Robot Navigation
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.
KeywordsNeuro-Fuzzy Obstacle avoidance Mobile robot Navigation
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- 1.Latombe, J.C.: Robot Motion Planning. Kluwer Academic Publishers, New York (1990)Google Scholar
- 2.Canny, J.E.: The Complexity of Robot Motion Planning. MIT Press, Cambridge (1988)Google Scholar
- 6.Regli, L.: Robot Lab: Robot Path Planning. Lectures Notes of Department of computer Science. Drexel University (2007)Google Scholar
- 7.Khatib, O.: Real time Obstacle Avoidance for manipulators and Mobile Robots. IEEE Conference on Robotics and Automation 2, 505 (1985)Google Scholar
- 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.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.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
- 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
- 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.The Math Works Company, Natick, MA, ANFIS Toolbox User’s Guide of MATLABGoogle Scholar
- 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