Advertisement

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
  • 12 Downloads

Abstract

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.

Keywords

ANFIS artificial intelligence dynamic environments mobile robot robot navigation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    L. Zuo, Q. Guo, X. Xu and H. Fu, “A hierarchical path planning approach based on A* and least–squares policy iteration for mobile robots,” Neurocomputing, vol. 170, pp.257–266, December 2015.Google Scholar
  2. [2]
    C. Xia and A. El Kamel, “Neural inverse reinforcement learning in autonomous navigation,” Robotics and Autonomous Systems, vol. 84, pp. 1–14, June 2016.Google Scholar
  3. [3]
    Y. H. Lee, S. G. Kim, T. Y. Kuc, J. K. Park, S. H. Ji, Y. S. Moon, and Y. J. Cho, “Virtual target tracking of mobile robot and its application to formation control,” International Journal of Control, Automation and Systems, vol. 12, no. 2, pp. 390–398, April 2014.Google Scholar
  4. [4]
    P. K. Mohanty and D. R. Parhi, “A new hybrid optimization algorithm for multiple mobile robots navigation based on the CS–ANFIS approach,” Memetic Computing, vol. 7, no.4, pp. 255–273, December 2015.Google Scholar
  5. [5]
    I. Ullah, F. Ullah, Q. Ullah, and S. Shin, “Integrated tracking and accident avoidance system for mobile robots,” International Journal of Control, Automation and Systems, vol. 11, no. 6, pp. 1253–1265, December 2013.Google Scholar
  6. [6]
    D. W. Kim, T. A. Lasky, and S. A. Velinsky, “Autonomous multi–mobile robot system: Simulation and implementation using fuzzy logic,” International Journal of Control, Automation and Systems, vol. 11, no. 3, pp. 545–554, June 2013.Google Scholar
  7. [7]
    S. H. Tang, F. Kamil, W. Khaksar, N. Zulkifli, and S. A. Ahmad, “Robotic motion planning in unknown dynamic environments: existing approaches and challenges,” Proc. of IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), IEEE, pp. 288–294, 2015.Google Scholar
  8. [8]
    C. Lee and M. Chiu, “Recurrent neuro fuzzy control design for tracking of mobile robots via hybrid algorithm,” Expert Syst. Appl., vol. 36, no. 5, pp. 8993–8999, July 2009.Google Scholar
  9. [9]
    A. Yorozu and M. Takahashi, “Obstacle avoidance with translational and efficient rotational motion control considering movable gaps and footprint for autonomous mobile robot,” International Journal of Control, Automation and Systems, vol. 14, no. 5, pp. 1352–1364, October 2016.Google Scholar
  10. [10]
    A. Abraham, “Adaptation of fuzzy inference system using neural learning,” Fuzzy Systems Engineering Anonymous Springer, pp. 53–83, 2005.Google Scholar
  11. [11]
    J. R. Jang, “Fuzzy modeling using generalized neural networks and kalman filter algorithm,” Proceedings of 9th National Conference on Artificial Intelligence (AAAI), Anaheim, CA, USA, vol. 2, pp. 762–767, 14–19 July 1991.Google Scholar
  12. [12]
    Y.Wei, J. Qiu, and H. R. Karimi, “Reliable output feedback control of discrete–time fuzzy affine systems with actuator faults,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 64, no. 1, pp. 170–181, January 2017.Google Scholar
  13. [13]
    J. Jang, “ANFIS: adaptive–network–based fuzzy inference system,” IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665–685, May 1993.Google Scholar
  14. [14]
    J. R. Jang, C. Sun, and E. Mizutani, “Neuro–fuzzy and soft computing; a computational approach to learning and machine intelligence,” IEEE Transactions on Automatic Control, vol. 42, no. 10, pp. 1482–4, October 1997.Google Scholar
  15. [15]
    Y. Wei, J. Qiu, X. Peng, and H. K. Lam. “T–S fuzzy–affinemodel–based reliable output feedback control of nonlinear systems with actuator faults,” Circuits, Systems, and Signal Processing, vol. 37, no. 1, pp. 81–97, January 2017.Google Scholar
  16. [16]
    Y. Wei, J. Qiu, and H. Lam, “A novel approach to reliable output feedback control of fuzzy–affine systems with timedelays and sensor faults,” IEEE Transactions on Fuzzy Systems, vol. 25, no. 6, pp. 1808–1823, 2017.Google Scholar
  17. [17]
    C. Lakhmissi, “Comparison between fuzzy, neural and neuro–fuzzy controllers for mobile robot path tracking,” Proc. of the 3rd International Conference on Control, Engineering & Information Technology (CEIT), pp. 1–6, May 2015.Google Scholar
  18. [18]
    H. Chang and T. Jin, “Command fusion based fuzzy controller design for moving obstacle avoidance of mobile robot,” Future Information Communication Technology and Applications Anonymous Springer, pp. 905–913, 2013.Google Scholar
  19. [19]
    P. K. Mohanty and D. R. Parhi, “Path generation and obstacle avoidance of an autonomous mobile robot using intelligent hybrid controller,” Proc. of International Conference on Swarm, Evolutionary, and Memetic Computing, Springer, Berlin, Heidelberg, pp. 240–247, 2012.Google Scholar
  20. [20]
    T. Arora, Y. Gigras, and V. Arora, “Robotic path planning using genetic algorithm in dynamic environment,” International Journal of Computer Application, vol. 89, no. 11, pp. 8–12, March 2014.Google Scholar
  21. [21]
    B. Li, J. Chang, and C. Wu, “A potential function and artificial neural network for path planning in dynamic environments based on self–reconfigurable mobile robot system,” Proc. of IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 1–6, November 2012.Google Scholar
  22. [22]
    P. Goel and D. Singh, “An improved abc algorithm for optimal path planning,” Int. J. Sci. Res. (IJSR), vol. 2, no. 6, pp. 261–264, June 2013.Google Scholar
  23. [23]
    M. R. Islam, M. Tajmiruzzaman, M. M. H. Muftee, and M. S. Hossain, “Autonomous robot path planning using particle swarm optimization in dynamic environment with mobile obstacles & multiple target,” Proc. of International Conference on Mechanical, Industrial and Energy Engineering, pp. 1–6. 2014.Google Scholar
  24. [24]
    X. Yan, Q. Wu, C. Hu, H. Yao, Y. Fan, Q. Liang, and C. Liu, “Robot path planning based on swarm intelligence,” International Journal of Control and Automation, vol. 7, no. 7, pp. 15–32, July 2014.Google Scholar
  25. [25]
    J. Riget and J. S. Vesterstrøm, “A diversity–guided particle swarm optimizer–the ARPSO,” Dept. Comput. Sci., Univ. of Aarhus, Aarhus, Denmark, Tech. Rep, vol. 2, pp. 2002. 2002.Google Scholar
  26. [26]
    A. Hussein, A. Al–Kaff, A. Escalera, and J.M. Armingol, “Autonomous indoor navigation of low–cost quadcopters,” Proc. of IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), IEEE, pp. 133–138, November 2015.Google Scholar
  27. [27]
    A. Janis and A. Bade, “Path planning algorithm in complex environment: a survey,” Transactions on Science and Technology, vol. 3, no. 1, pp. 31–40. April 2016.Google Scholar
  28. [28]
    P. Payeur, H. Le–Huy, and C. Gosselin, “Robot path planning using neural networks and fuzzy logic,” Proc. of the 20th International Conferenceon Industrial Electronics, Control and Instrumentation (IECON’94), vol. 2, pp. 800–805, September 1994.Google Scholar
  29. [29]
    D. K. Pratihar, K. Deb, and A. Ghosh, “A genetic–fuzzy approach for mobile robot navigation among moving obstacles,” International Journal of Approximate Reasoning, vol. 20, no. 2, pp. 145–172, February 1999.zbMATHGoogle Scholar
  30. [30]
    F. K. Purian and E. Sadeghian, “Mobile robots path planning using ant colony optimization and fuzzy logic algorithms in unknown dynamic environments,” Proc. of International Conference on Control, Automation, Robotics and Embedded Systems (CARE), pp. 1–6, December 2013.Google Scholar
  31. [31]
    P. Woo and V. Polisetty, “ANFIS generated dynamic path planning for a mobile robot to track a randomly moving target in a 3D space with obstacle avoidance,” Proc. of IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1–8, July 2010.Google Scholar
  32. [32]
    J. Jang, “ANFIS: adaptive–network–based fuzzy inference system,” IEEE Trans. Syst. Man Cybern., vol. 23, no. 3, pp. 665–685, May 1993.Google Scholar
  33. [33]
    M. Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, Pearson Education, 2005.Google Scholar
  34. [34]
    S. Guillaume, “Designing fuzzy inference systems from data: an interpretability–oriented review,” IEEE Trans. Fuzzy Syst., vol. 9, no. 3, pp. 426–443, June 2001.MathSciNetGoogle Scholar
  35. [35]
    S. L. Chiu, “Fuzzy model identification based on cluster estimation,” Journal of Intelligent & Fuzzy Systems, vol. 2, no. 3, pp. 267–278, January 1994.Google Scholar
  36. [36]
    J. R. Jang, “Input selection for ANFIS learning,” Proc. of the Fifth IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1493.1499, September 1996.Google Scholar
  37. [37]
    H. Choset, K. M. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. E. Kavraki, and S. Thrun, Principles of Robot Motion: Theory, Algorithms, and Implementations, MITPress, Boston, 2005.zbMATHGoogle Scholar
  38. [38]
    D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” IEEE Robotics & Automation Magazine, vol. 4, no. 1, pp. 23–33, March 1997.Google Scholar
  39. [39]
    J. Borenstein and Y. Koren, “The vector field histogramfast obstacle avoidance for mobile robots,” Robotics and Automation, IEEE Transactions, vol. 7, no. 3, pp. 278–288, June 1991.Google Scholar
  40. [40]
    P. Svestka, J. C. Latombe, and L. E. O. Kavraki, “Probabilistic roadmaps for path planning in high–dimensional configuration spaces,” IEEE Transactions on Robotics and Automation, vol. 12, no. 4, pp. 566–580, August 1996.Google Scholar
  41. [41]
    B. Park, J. Choi, and W. K. Chung, “Sampling–based retraction method for improving the quality of mobile robot path planning,” International Journal of Control, Automation and Systems, vol. 10, no. 5, pp. 982–991, October 2012.Google Scholar
  42. [42]
    S. M. LaValle and J. J. Kuffner, “Randomized kinodynamic planning,” The International Journal of Robotics Research, vol. 20, no. 5, pp. 378–400, May 2001.Google Scholar
  43. [43]
    K. Yang, “Anytime synchronized–biased–greedy rapidlyexploring random tree path planning in two dimensional complex environments,” International Journal of Control, Automation and Systems, vol. 9, no 4, pp. 750–758, August 2011.Google Scholar

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

Personalised recommendations