INFLUENCE OF MOBILE ROBOT CONTROL ALGORITHMS ON THE PROCESS OF AVOIDING OBSTACLES

Piotr Wójcicki

p.wojcicki@pollub.pl
Lublin University of Technology, Institute of Computer Science (Poland)
http://orcid.org/0000-0002-0522-6223

Paweł Powroźnik


Lublin University of Technology, Institute of Computer Science (Poland)
http://orcid.org/0000-0002-5705-4785

Kamil Żyła


Lublin University of Technology, Institute of Computer Science (Poland)
http://orcid.org/0000-0002-6291-003X

Stanisław Grzegórski


Lublin University of Technology, Institute of Computer Science (Poland)
http://orcid.org/0000-0001-7640-6195

Abstract

This article presents algorithms for controlling a mobile robot. An algorithms are based on artificial neural network and fuzzy logic. Distance was measured with the use of ultrasonic sensor. The equipment applied as well as signal processing algorithms were characterized. Tests were carried out on a mobile wheeled robot. The analysis of the influence of algorithm while avoiding obstacles was made.


Keywords:

mobile robot, algorithms, collision avoidance

Adib A., Masoumi B.: Mobile robots navigation in unknown environments by using fuzzy logic and learning automata. Artificial Intelligence and Robotics (IRANOPEN), 2017, 58–63 [doi: 10.1109/RIOS.2017.7956444].
  Google Scholar

Bajrami X., Dërmaku A., Demaku N., Maloku S., Kikaj A., Kokaj A.: Genetic and Fuzzy logic algorithms for robot path finding. 5th Mediterranean Conference on Embedded Computing (MECO), 2016, 195–199 [doi: 10.1109/MECO.2016.7525739].
  Google Scholar

Boujelben M., Ayedi D., Rekik C., Derbel N.: Fuzzy logic controller for mobile robot navigation to avoid dynamic and static obstacles. 14th International Multi-Conference on Systems, Signals & Devices (SSD), 2017, 293–298 [doi: 10.1109/SSD.2017.8166963].
  Google Scholar

Hammed A. A., Karlik B., Salman M. S.: Back-propagation algorithm with variable adaptive momentum. Knowledge-Based Systems 114, 2016, 79–87 [doi: 10.1016/j.knosys.2016.10.001].
  Google Scholar

Handayani A. S., Dewi T., Husni N.L., Nurmaini S., Yani I.: Target tracking in mobile robot under uncertain environment using fuzzy logic controller. 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017, 1–5 [doi: 10.1109/EECSI.2017.8239079].
  Google Scholar

He K., Sun H., Cheng W.: Application of fuzzy neural network based on T-S model for mobile robot to avoid obstacles. 7th World Congress on Intelligent Control and Automation, 2008, 8282–8285 [doi: 10.1007/978-3-540-88513-9_120].
  Google Scholar

Khan S., Ahmmed M. K.: Where am I? Autonomous navigation system of a mobile robot in an unknown environment. 5th International Conference on Informatics, Electronics and Vision (ICIEV), 2016, 56–61 [doi: 10.1109/ICIEV.2016.7760188].
  Google Scholar

Mazare A., Ionescu L., Lita A., Serban G., Ionut M.: Mobile system with real time route learning using Hardware Artificial Neural Network. 7th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2015, 45–48 [doi: 10.1109/ECAI.2015.7301250].
  Google Scholar

McCulloch W., Pitts W.: A logical calculations of the ideas in nervous activity. Bulletin of Mathematical Biophisics 5, 1943, 115–133.
  Google Scholar

Mohammad S. H. A., Jeffril M. A., Sariff N.: Mobile robot obstacle avoidance by using Fuzzy Logic technique. IEEE 3rd International Conference on System Engineering and Technology, 2013, 331–335 [doi: 10.1109/ICSEngT.2013.6650194].
  Google Scholar

Panigrahi P.K., Ghosh S., Parhi D.R.: A novel intelligent mobile robot navigation technique for avoiding obstacles using RBF neural network. International Conference on Control, Instrumentation, Energy and Communication (CIEC), 2014, 1–6 [doi: 10.1109/CIEC.2014.6959038].
  Google Scholar

Powroźnik P., Czerwiński D.: Effectiveness comparison on an artificial neural networks to identify Polish emotional speech. Przegląd Elektrotechniczny 07/2016, 45–48 [doi: 10.15199/48.2016.07.08].
  Google Scholar

Stączek P.: Digital signal processing in ultrasonic based navigation system for mobile robots. ITM Web Conf. 15, 2017 [doi:10.1051/itmconf/20171505008].
  Google Scholar

Tiwari S., Naresh R.: Comparative study of backpropagation algorithms in neural network based identification on power system, International Journal of Computer Science and Information Technology 5(4), 2013, 93–107 [doi: 10.5121/ijcsit.2013.5407].
  Google Scholar

Wu T. F., Tsai P. S., Hu N. T., Chen J. Y.: Use of Ultrasonic Sensors to Enable Wheeled Mobile Robots to Avoid Obstacles. Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2014, 958–961 [doi: 10.1109/IIH-MSP.2014.240].
  Google Scholar

Yong L., Yang F., Hui L., Si-Wen Z.: The Improved Training Algorithm of Back Propagation Neural Network with Selfadaptive Learning Rate, International Conference on Computational Intelligence and Natural Computing, 2009, 73–76 [doi:10.1109/CINC.2009.111].
  Google Scholar

Download


Wójcicki, P., Powroźnik, P., Żyła, K., & Grzegórski, S. (2018). INFLUENCE OF MOBILE ROBOT CONTROL ALGORITHMS ON THE PROCESS OF AVOIDING OBSTACLES. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 8(4), 60–63. https://doi.org/10.5604/01.3001.0012.8041

Authors

Piotr Wójcicki 
p.wojcicki@pollub.pl
Lublin University of Technology, Institute of Computer Science Poland
http://orcid.org/0000-0002-0522-6223

Authors

Paweł Powroźnik 

Lublin University of Technology, Institute of Computer Science Poland
http://orcid.org/0000-0002-5705-4785

Authors

Kamil Żyła 

Lublin University of Technology, Institute of Computer Science Poland
http://orcid.org/0000-0002-6291-003X

Authors

Stanisław Grzegórski 

Lublin University of Technology, Institute of Computer Science Poland
http://orcid.org/0000-0001-7640-6195

Statistics

Abstract views: 277
PDF downloads: 140