Path Planning and Control of Autonomous Robotic Agent Using Mamdani Based Fuzzy Logic Controller and ARDUINO UNO Micro Controller | SpringerLink
Skip to main content

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 327))

Abstract

The autonomous mobile robots are used for various purposes like materials transportation, nuclear and military environments etc. In this paper fuzzy logic technique is used for controlling the mobile robot in an unknown environment. The main goal of robot is to reach the target point from a starting point with avoiding obstacles in the way. The Mamdani fuzzy logic controller is used to obtain collision free path where inputs are front obstacle distance (FOD), left obstacle distance (LOD), right obstacle distance (ROD), heading angle (HA) and the output corresponds to the steering angle (SA) of the mobile robot. The effectiveness of the controller is verified using Mamdani fuzzy inference system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 22879
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 28599
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Samsudin, K., Ahmad, F.A., Mashohor, S.: A highly interpretable fuzzy rule base using ordinal structure for obstacle avoidance of mobile robot. International Journal of Applied soft Computing 11, 1631–1637 (2011)

    Article  Google Scholar 

  2. Jiang, M., Yu, Y.X., Liu, F.Z., Hong, Q.: Fuzzy Neural Network Based Dynamic Path Planning. In: Proceedings of International Conference on Machine Learning and Cybernetics, Xian, pp. 15–17 (2012)

    Google Scholar 

  3. Bayar, G., Konukseven, E.I., Koku, A.B.: Control of a Differentially Driven Mobile Robot using Radial Basis Function Based Neural Networks. WSEAS Transactions on Systems and Control 3(12), 1002–1013 (2008)

    Google Scholar 

  4. Mohanty, P.K., Parhi, D.R.: A New Intelligent Motion Planning for Mobile Robot Navigation Using Multiple Adaptive Neuro-fuzzy Inference System. International Journal of Applied Mathematics and Information Sciences 8(5), 2527–2535 (2014)

    Article  Google Scholar 

  5. Yang, Y.K., Lin, Y., Fang, W.L., Pan, J.K.: A Fuzzy-reasoning Radial Basis Function Neural Network with Reinforcement Learning Method. In: Proceedings of ICAI, pp. 1–5 (2012)

    Google Scholar 

  6. Eskandar, H., Salehi, P., Sabour, M.H.: Fuzzy Logic Tracking Control for a Three Wheel Circular Robot in Unknown Environment. World Applied Science Journal 11, 321–326 (2010)

    Google Scholar 

  7. Castillo, O., Trujillo, L., Melin, P.: Multiple Objective Genetic Algorithms for Path Planning Optimization in Autonomous Mobile Robots. International Journal of Computer Systems and Signals 6(1), 48–63 (2005)

    Google Scholar 

  8. Faisal, M., Hedjar, R., Suaiman, M.A., Mutib, K.A.: Fuzzy Logic Navigation and Obstacle Avoidance by a Mobile Robot in an Unknown Dynamic Environment. International Journal of Advanced Robotic Systems 10, 1–7 (2013)

    Article  Google Scholar 

  9. Boubertakh, H., Tadjine, M., Glorennec, Y., Labiod, S.: A Simple Goal Seeking Navigation Method for a Mobile robot using Human Sense. Fuzzy Logic and Reinforcement learning, Journal of Automatic Control 18(1), 23–27 (2008)

    Article  Google Scholar 

  10. Benavidez, P., Jamshidi, M.: Mobile Robot Navigation and Target Tracking System. In: Proceedings of IEEE, 6th International Conference on System of Systems Engineering, Albuquerque, December 27-30, pp. 299–304 (2011)

    Google Scholar 

  11. Pradhan, S.K., Parhi, D.R., Panda, A.K.: Fuzzy Logic Techniques for Navigation of Several Mobile Robots. International Journal of Applied Soft Computing 9, 290–304 (2009)

    Article  Google Scholar 

  12. Farooq, U., Hasan, K.M., Abbas, G., Asad, M.U.: Comparative Analysis of Zero Order Sugeno and Mamdani Fuzzy Logic Controllers for Obstacle Avoidance of Mobile Robot Navigation. In: IEEE International Conference and Workshop on Current Trends in Information Technology (CTIT), Dubai, October 26-27, pp. 113–119 (2011)

    Google Scholar 

  13. Shayestegan, M., Marhaban, M.H.: Mobile Robot Safe Navigation in Unknown Environment. In: IEEE International Conference on Control System, Computing and Engineering, Penang, November 23-25, pp. 44–49 (2012)

    Google Scholar 

  14. Jaradat, M.A.K., Al-Rousan, M., Quadan, L.: Reinforcement Based Mobile Robot Navigation in Dynamic Environment. International Journal of Robotics and Computer Integrated Manufacturing 27, 135–149 (2011)

    Article  Google Scholar 

  15. Rusu, P., Petriu, T.E., Cornell, W.A., Spoelder, H.J.W.: Behavior Based Neuro Fuzzy Controller for Mobile Robot Navigation. In: Proceedings of IEEE,19th Instrumentation and Measurement Conference, vol. 2(4), pp. 1617–1622 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pratap Kumar Panigrahi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Panigrahi, P.K., Sahoo, S. (2015). Path Planning and Control of Autonomous Robotic Agent Using Mamdani Based Fuzzy Logic Controller and ARDUINO UNO Micro Controller. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11933-5_20

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11932-8

  • Online ISBN: 978-3-319-11933-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics