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AKH-NFIS: Adaptive Krill Herd Network Fuzzy Inference System for Mobile Robot Navigation

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Abstract

Mobile robot navigation has been a current issue in the most recent two decades. Mobile robots are necessary to explore in obscure and dynamic situations. To solve the aforementioned issues an extended Kalman filter (EKF) and adaptive Krill Herd network fuzzy inference system (AKH-NFIS) techniques are proposed for the self-sufficient portable robot route. This is in charge of avoidance of obstacles in an obscure static and dynamic environment. Initially, the start and goal position will be set and the obstacles identified in front of the robot will be checked using the sensor. This sensor captures the environmental information around the mobile robot. Subsequently, to deal with the filtering problem of sensor data, EKF will be used. By EKF more accurate position estimation will be obtained by using dynamic information of data. Subsequently, the obstacle distances from the robot and the obstacle avoidance angle are calculated and fed as input to the training dataset. This training data set trains AKH-NFIS controller obtained by designing a Krill herd optimization algorithm adaptive network fuzzy logic-based navigation controller. The left wheel velocity and right wheel velocity are the output from the proposed system. The robustness of the proposed navigation controller will be assessed by exploring the mobile robot in various conditions. The experimental result demonstrates that our proposed strategy outperforms by correlation with existing strategies.

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References

  1. Wai, R.-J., & Lin, Y.-W. (2013). Adaptive moving-target tracking control of a vision-based mobile robot via a dynamic petri recurrent fuzzy neural network. IEEE Trans. Fuzzy Systems, 21(4), 688–701.

    Article  Google Scholar 

  2. Pandey, A., & Parhi, D. R. (2014). MATLAB simulation for mobile robot navigation with hurdles in cluttered environment using minimum rule-based fuzzy logic controller. Procedia Technology, 14, 28–34.

    Article  Google Scholar 

  3. Mac, T. T., Copot, C., & Tran, D. T. (2016). De Keyser R 2016 Heuristic approaches in robot path planning: A survey. Robotics and Autonomous Systems, 86, 13–28.

    Article  Google Scholar 

  4. Algabri, M., Mathkour, H., Ramdane, H., & Alsulaiman, M. (2015). Comparative study of soft computing techniques for mobile robot navigation in an unknown environment. Computers in Human Behavior, 50, 42–56.

    Article  Google Scholar 

  5. Deepak, B. B. V. L., Parhi, D. R., & Raju, B. M. V. A. (2014). Advance particle swarm optimization-based navigational controller for mobile robot. Arabian Journal for Science and Engineering, 39(8), 6477–6487.

    Article  Google Scholar 

  6. Pandey, A., & Parhi, D. R. (2017). Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm. Defence Technology, 13(1), 47–58.

    Article  Google Scholar 

  7. Mohanty, P. K., & Parhi, D. R. (2014). A new intelligent motion planning for mobile robot navigation using multiple adaptive neuro-fuzzy inference system. Applied Mathematics & Information Sciences, 8(5), 2527.

    Article  Google Scholar 

  8. Pandey, A., Sonkar, R. K., Pandey, K. K., & Parhi, D. R. (2014). Path planning navigation of mobile robot with obstacles avoidance using fuzzy logic controller. In 2014 IEEE 8th international conference on intelligent systems and control (ISCO) (pp. 39–41). IEEE, 2014.

  9. Meléndez, A., & Castillo, O. (2013). Evolutionary optimization of the fuzzy integrator in a navigation system for a mobile robot. In O. Castillo, P. Melin, & J. Kacprzyk (Eds.), Recent advances on hybrid intelligent systems. Studies in Computational Intelligence (Vol. 451, pp. 21–31). Berlin, Heidelberg: Springer.

    Google Scholar 

  10. Farooq, U., Amar, M., Asad, M. U., Hanif, A., & Saleh, S. O. (2014). Design and implementation of neural network based controller for mobile robot navigation in unknown environments. International Journal of Computer and Electrical Engineering, 6(2), 83.

    Article  Google Scholar 

  11. Kayacan, E., Kayacan, E., Ramon, H., & Saeys, W. (2013). Adaptive neuro-fuzzy control of a spherical rolling robot using sliding-mode-control-theory-based online learning algorithm.". IEEE Transactions on Cybernetics, 43(1), 170–179.

    Article  Google Scholar 

  12. Algabri, M., Mathkour, H., & Ramdane, H. (2014). Mobile robot navigation and obstacle-avoidance using ANFIS in unknown environment. International Journal of Computer Applications, 91, 14.

    Article  Google Scholar 

  13. Pandey, A., Kumar, S., Pandey, K. K., & Parh, D. R. (2016). Mobile robot navigation in unknown static environments using ANFIS controller. Perspectives in Science, 8, 421–423.

    Article  Google Scholar 

  14. Faisal, M., Hedjar, R., Sulaiman, M. A., & Al-Mutib, K. (2013). Fuzzy logic navigation and obstacle avoidance by a mobile robot in an unknown dynamic environment. International Journal of Advanced Robotic Systems, 10(1), 37.

    Article  Google Scholar 

  15. Sanchez, M. A., Castillo, O., & Castro, J. R. (2015). Generalized type-2 fuzzy systems for controlling a mobile robot and a performance comparison with interval type-2 and type-1 fuzzy systems. Expert Systems with Applications, 42(14), 5904–5914.

    Article  Google Scholar 

  16. Castillo, O., & Melin, P. (2014). A review on interval type-2 fuzzy logic applications in intelligent control. Information Sciences, 279, 615–631.

    Article  MathSciNet  Google Scholar 

  17. Rezaee, H., & Abdollahi, F. (2014). A decentralized cooperative control scheme with obstacle avoidance for a team of mobile robots. IEEE Transactions on Industrial Electronics, 61(1), 347–354.

    Article  Google Scholar 

  18. Pothal, J. K., & Parhi, D. R. (2015). Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system. Robotics and Autonomous Systems, 72, 48–58.

    Article  Google Scholar 

  19. Parhi, D. R., & Mohanty, P. K. (2016). IWO-based adaptive neuro-fuzzy controller for mobile robot navigation in cluttered environments. The International Journal of Advanced Manufacturing Technology, 83(9–12), 1607–1625.

    Article  Google Scholar 

  20. Mohanty, P. K., & Parhi, D. R. (2015). A new hybrid intelligent path planner for mobile robot navigation based on adaptive neuro-fuzzy inference system. Australian Journal of Mechanical Engineering, 13(3), 195–207.

    Article  Google Scholar 

  21. Mohanty, P. K., & Parhi, D. R. (2015). A new hybrid optimization algorithm for multiple mobile robots navigation based on the CS-ANFIS approach. Memetic Computing, 7(4), 255–273.

    Article  Google Scholar 

  22. Wang, D., Yuhang, H., & Ma, T. (2020). Mobile robot navigation with the combination of supervised learning in cerebellum and reward-based learning in basal ganglia. Cognitive Systems Research, 59, 1–14.

    Article  Google Scholar 

  23. Ponce, H., Moya-Albor, E., Martínez-Villaseñor, L., & Brieva, J. (2020). Distributed evolutionary learning control for mobile robot navigation based on virtual and physical agents. Simulation Modelling Practice and Theory, 102, 102058.

    Article  Google Scholar 

  24. Kim, C., & Won, J.-S. (2020). A fuzzy analytic hierarchy process and cooperative game theory combined multiple mobile robot navigation algorithm. Sensors, 20(10), 2827.

    Article  Google Scholar 

  25. Zhang, Y., Zhang, C.-H., & Shao, X. (2021). User preference-aware navigation for mobile robot in domestic via defined virtual area. Journal of Network and Computer Applications, 173, 102885.

    Article  Google Scholar 

  26. Chen, C.-H., Lin, C.-J., Jeng, S.-Y., Lin, H.-Y., & Cheng-Yi, Yu. (2021). Using ultrasonic sensors and a knowledge-based neural fuzzy controller for mobile robot navigation control. Electronics, 10(4), 466.

    Article  Google Scholar 

  27. Kowalski, P. A., & Łukasik, S. (2015). Experimental study of selected parameters of the krill herd algorithm. In Intelligent Systems' 2014 (pp. 473–485). Cham: Springer.

  28. Karaboga, D., & Kaya, E. (2019). Training ANFIS by using an adaptive and hybrid artificial bee colony algorithm (aABC) for the identification of nonlinear static systems. Arabian Journal for Science and Engineering, 44(4), 3531–3547.

    Article  Google Scholar 

  29. Pal, D., & Bhagat, S. K. (2020). Design and analysis of optimization based integrated ANFIS-PID controller for networked controlled systems (NCSs). Cogent Engineering, 7(1), 1772944.

    Article  Google Scholar 

  30. Ibrahim, A. A., Zhou, H.-b, Tan, S.-x, Zhang, C.-l, & Duan, J.-a. (2020). Regulated Kalman filter based training of an interval type-2 fuzzy system and its evaluation. Engineering Applications of Artificial Intelligence, 95, 103867

    Article  Google Scholar 

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Correspondence to Madhu Sudan Das.

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Das, M.S., Samanta, A., Sanyal, S. et al. AKH-NFIS: Adaptive Krill Herd Network Fuzzy Inference System for Mobile Robot Navigation. Wireless Pers Commun 120, 3389–3413 (2021). https://doi.org/10.1007/s11277-021-08619-5

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  • DOI: https://doi.org/10.1007/s11277-021-08619-5

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