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New Strategy for Mobile Robot Navigation Using Fuzzy Logic

  • B. B. V. L. Deepak
  • D. R. Parhi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 862)

Abstract

The current research work aims to develop an efficient motion planner for a differential vehicular system inspired from the Fuzzy inference system. In this strategy, rolling and sliding kinematic constraints have been considered while implementation. The proposed fuzzy model requires two inputs: (1) the distance between the robot and the obstacles in the environment and (2) position of the target, i.e., the robot heading angle towards the destination. Once the system receives information from its search space, the robot obtains the suitable steering angle for an intelligent system. Experimental analysis has been conducted to a differential robot in order to represent its effectiveness.

Keywords

Design for assembly Assembly sequence planning Assembly constraints Firefly algorithm Computer aided design (CAD) 

References

  1. 1.
    B.B.V.L. Deepak, M.R. Bahubalendruni, Development of a path follower in real-time environment. World J. Eng. 14(4), 297–306 (2017)CrossRefGoogle Scholar
  2. 2.
    B.B.V.L. Deepak, D.R. Parhi, Control of an automated mobile manipulator using artificial immune system. J. Exp. Theor. Artif. Intell. 28(1–2), 417–439 (2016)CrossRefGoogle Scholar
  3. 3.
    B.B.V.L. Deepak, D. Parhi, Intelligent adaptive immune-based motion planner of a mobile robot in cluttered environment. Intell. Serv. Robot. 6(3), 155–162 (2013)CrossRefGoogle Scholar
  4. 4.
    A.T. Azar, H.H. Ammar, H. Mliki, Fuzzy logic controller ith color vision system tracking for mobile manipulator robot, in International Conference on Advanced Machine Learning Technologies and Applications (Springer, Cham, 2018), pp. 138–146CrossRefGoogle Scholar
  5. 5.
    T.C. Lin, C.C. Chen, C.J. Lin, Navigation control of mobile robot using interval type-2 neural fuzzy controller optimized by dynamic group differential evolution. Adv. Mech. Eng. 10(1), 1687814017752483 (2018)CrossRefGoogle Scholar
  6. 6.
    B.B.V.L. Deepak, D.R. Parhi, B.M.V.A. Raju, Advance particle swarm optimization-based navigational controller for mobile robot. Arab. J. Sci. Eng. 39(8), 6477–6487 (2014)CrossRefGoogle Scholar
  7. 7.
    O.S. Syed, U.A. Muhammad, A.J. Muhammad, M. Hassam, Design and implementation of neural network based controller for mobile robot navigation, in 26th IEEEP Students’ Seminar 2011 (2011)Google Scholar
  8. 8.
    M.P. Garcia, O. Montiel, O. Castillo, R. Sepúlveda, P. Melin, Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Appl. Soft Comput. 9(3), 1102–1110 (2009)CrossRefGoogle Scholar
  9. 9.
    C.F. Juang, Y.C. Chang, Evolutionary-group-based particle-swarm-optimized fuzzy controller with application to mobile-robot navigation in unknown environments. IEEE Trans. Fuzzy Syst. 19(2), 379–392 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.National Institute of TechnologyRourkelaIndia

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