An ANFIS-based Optimized Fuzzy-multilayer Decision Approach for a Mobile Robotic System in Ever-changing Environment

  • Farah KamilEmail author
  • Tang Sai Hong
  • Weria Khaksar
  • Norzima Zulkifli
  • Siti Azfanizam Ahmad
Regular Papers Intelligent Control and Applications


In robotics, resolution of several difficult issues requires process intelligence. In many applications, the environment of a robot changes with time in a manner that has not been foreseen by its designer. Additionally, information on the environment is commonly inaccurate and incomplete, which is attributed to the restricted sensory activity of sensors. A new online sensor-based motion planning algorithm, which employs a fuzzy multilayer decision controller, is proposed in this study to enhance the quality of the next position in terms of safety and optimality. Fuzzy logic controller (FLC) utilizes the prediction and priority rules of multilayer approach for an effective and intelligent proposed method. Moreover, an adaptive neuro-fuzzy inference system (ANFIS) is designed, which constructs and optimizes an FLC using a given dataset of input/output variables. The ANFIS shortens the high runtime of fuzzy system, optimizes the parameters of the membership functions of inputs and outputs of the fuzzy-multilayer decision controller, and rearranges the rules to enhance the efficiency of the overall approach. The simulation and comparison results indicate the superiority of the proposed path planning algorithm from other well-known algorithms.


ANFIS artificial intelligence dynamic environments mobile robot robot navigation 


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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Farah Kamil
    • 1
    Email author
  • Tang Sai Hong
    • 2
  • Weria Khaksar
    • 3
  • Norzima Zulkifli
    • 2
  • Siti Azfanizam Ahmad
    • 2
  1. 1.AL-Furat AL-Awast Technical UniversityAL-Diwaniyah Technical InstituteIraqIraq
  2. 2.Department of Mechanical and Manufacturing EngineeringUniversity Putra MalaysiaSerdangMalaysia
  3. 3.Robotics and Intelligent Systems Group (ROBIN), Department of InformaticsUniversity of OsloOsloNorway

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