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Path optimisation of a mobile robot using an artificial neural network controller

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  • M.K. Singh
  • D.R. Parhi

Abstract

This article proposed a novel approach for design of an intelligent controller for an autonomous mobile robot using a multilayer feed forward neural network, which enables the robot to navigate in a real world dynamic environment. The inputs to the proposed neural controller consist of left, right and front obstacle distance with respect to its position and target angle. The output of the neural network is steering angle. A four layer neural network has been designed to solve the path and time optimisation problem of mobile robots, which deals with the cognitive tasks such as learning, adaptation, generalisation and optimisation. A back propagation algorithm is used to train the network. This article also analyses the kinematic design of mobile robots for dynamic movements. The simulation results are compared with experimental results, which are satisfactory and show very good agreement. The training of the neural nets and the control performance analysis has been done in a real experimental setup.

Suggested Citation

  • M.K. Singh & D.R. Parhi, 2011. "Path optimisation of a mobile robot using an artificial neural network controller," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(1), pages 107-120.
    • Handle: RePEc:taf:tsysxx:v:42:y:2011:i:1:p:107-120
    DOI: 10.1080/00207720903470155
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    Cited by:

    1. Kuangrong Hao & Chongbin Guo & Yongsheng Ding, 2014. "Physiological hemostasis based intelligent integrated cooperative controller for precise fault-tolerant control of redundant parallel manipulator," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(10), pages 2072-2087, October.
    2. Lin Xiao & Yunong Zhang, 2016. "Dynamic design, numerical solution and effective verification of acceleration-level obstacle-avoidance scheme for robot manipulators," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(4), pages 932-945, March.
    3. Liguo Weng & Min Xia & Wei Wang & Qingshan Liu, 2015. "Crew exploration vehicle (CEV) attitude control using a neural–immunology/memory network," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(1), pages 152-158, January.
    4. Gabriel G. R. de Castro & Guido S. Berger & Alvaro Cantieri & Marco Teixeira & José Lima & Ana I. Pereira & Milena F. Pinto, 2023. "Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs," Agriculture, MDPI, vol. 13(2), pages 1-25, January.

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