WPŁYW ALGORYTMÓW STEROWANIA ROBOTEM MOBILNYM NA PROCES UNIKANIA PRZESZKÓD

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