Abstract
Nowadays intelligent techniques such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as effective and suitable methods for modeling an engineering system. The hallmark of this paper presents a new intelligent hybrid technique (Multiple Adaptive Neuro-Fuzzy Inference System) based on the combination of fuzzy inference system and artificial neural network for solving path planning problem of autonomous mobile robot. First we develop an adaptive fuzzy controller with four input parameters, two output parameters and five parameters each. Afterwards each adaptive fuzzy controller acts as a single takagi-sugeno type fuzzy inference system, where inputs are front obstacle distance (FOD), left obstacle distance (LOD), right obstacle distance (ROD) (from robot), Heading angle (HA) (angle to target) and output corresponds to the wheel velocities ( Left wheel and right wheel) of the mobile robot. The effectiveness, feasibility and robustness of the proposed navigational controller have been tested by means of simulation results. It has been observed that the proposed path planning strategy is capable of avoiding obstacles and effectively guiding the mobile robot moving from the start point to the desired target point with shortest path length.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Latombe, J.C.: Robot Motion Planning. Kluwer Academic Publishers, New York (1990)
Canny, J.E.: The Complexity of Robot Motion Planning. MIT Press, MA (1988)
Lozano-Perez, T.: A simple motion planning algorithm for general robot manipulators. IEEE Journal of Robotics and Automation 3, 224–238 (1987)
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)
Payton, D., Rosenblatt, J., Keirsey, D.: Grid-based mapping for autonomous mobile robot. Robotics and Autonomous Systems 11, 13–21 (1993)
Regli, L.: Robot Path Planning. Lectures Notes of Department of computer Science. Drexel University (2007)
Khatib, O.: Real time Obstacle Avoidance for manipulators and Mobile Robots. In: IEEE Conference on Robotics and Automation, vol. 2, pp. 505–505 (1985)
Jang, J.S.R.: ANFIS: Adaptive network-based fuzzy inference system. IEEE Transaction on System, Man and Cybernetics –Part b 23, 665–685 (1993)
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)
Selekwa, M.F., Dunlap, D.D., Shi, D., CollinsJr, E.G.: Robot navigation in very cluttered environment by preference based fuzzy behaviors. Autonomous System 56, 231–246 (2007)
Abdessemed, F., Benmahammed, K., Monacelli, E.: A fuzzy based reactive controller for a non-holonomic mobile robot. Robotics Autonomous System 47, 31–46 (2004)
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)
Motlagh, O., Tang, S.H., Ismail, N.: Development of a new minimum avoidance system for behavior based mobile robot. Fuzzy Sets System 160, 1929–1946 (2009)
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)
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)
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)
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)
Nefti, S., Oussalah, M., Djouani, K., Pontnau, J.: Intelligent Adaptive Mobile Robot Navigation. Journal of Intelligent and Robotic Systems 30, 311–329 (2001)
Hui, N.B., Mahendar, V., Pratihar, D.K.: Time-optimal, collision-free navigation of a car-like mobile robot using neuro-fuzzy approaches. Fuzzy Sets and Systems 157, 2171–2204 (2008)
Godjevac, J., Steele, N.: Neuro-fuzzy control of a mobile robot. Neuro Computing 28, 127–142 (1999)
The Math Works Company, Natick, MA, ANFIS Toolbox User’s Guide of MATLAB
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Mohanty, P.K., Parhi, D.R. (2014). Path Planning Strategy for Mobile Robot Navigation Using MANFIS Controller. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-02931-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-02931-3_40
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02930-6
Online ISBN: 978-3-319-02931-3
eBook Packages: EngineeringEngineering (R0)